FLP discrete approach versus FLP continuous approach.
\r\n\tAnimal food additives are products used in animal nutrition for purposes of improving the quality of feed or to improve the animal’s performance and health. Other additives can be used to enhance digestibility or even flavour of feed materials. In addition, feed additives are known which improve the quality of compound feed production; consequently e.g. they improve the quality of the granulated mixed diet.
\r\n\r\n\tGenerally feed additives could be divided into five groups:
\r\n\t1.Technological additives which influence the technological aspects of the diet to improve its handling or hygiene characteristics.
\r\n\t2. Sensory additives which improve the palatability of a diet by stimulating appetite, usually through the effect these products have on the flavour or colour.
\r\n\t3. Nutritional additives, such additives are specific nutrient(s) required by the animal for optimal production.
\r\n\t4.Zootechnical additives which improve the nutrient status of the animal, not by providing specific nutrients, but by enabling more efficient use of the nutrients present in the diet, in other words, it increases the efficiency of production.
\r\n\t5. In poultry nutrition: Coccidiostats and Histomonostats which widely used to control intestinal health of poultry through direct effects on the parasitic organism concerned.
\r\n\tThe aim of the book is to present the impact of the most important feed additives on the animal production, to demonstrate their mode of action, to show their effect on intermediate metabolism and heath status of livestock and to suggest how to use the different feed additives in animal nutrition to produce high quality and safety animal origin foodstuffs for human consumer.
",isbn:"978-1-83969-404-2",printIsbn:"978-1-83969-403-5",pdfIsbn:"978-1-83969-405-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8ffe43a82ac48b309abc3632bbf3efd0",bookSignature:"Prof. László Babinszky",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10496.jpg",keywords:"Technological Feed Additives, Feed Industry, Quality of Compound Feed, Non-Antibiotic Growth Promoter, Product Quality, Additive Enzymes, Digestibility of Nutrients, NSP Enzymes, Farm Animals, Livestock, Immunity, Microbiome",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 24th 2020",dateEndSecondStepPublish:"December 22nd 2020",dateEndThirdStepPublish:"February 20th 2021",dateEndFourthStepPublish:"May 11th 2021",dateEndFifthStepPublish:"July 10th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus from the University of Debrecen, Hungary who authored 297 publications (papers, book chapters) and edited 3 books. Member of various committees and chairman of the World Conference of Innovative Animal Nutrition and Feeding (WIANF).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.jpg",biography:"László Babinszky is Professor Emeritus of animal nutrition at the University of Debrecen, Hungary. From 1984 to 1985 he worked at the Agricultural University in Wageningen and in the Institute for Livestock Feeding and Nutrition in Lelystad (the Netherlands). He also worked at the Agricultural University of Vienna in the Institute for Animal Breeding and Nutrition (Austria) and in the Oscar Kellner Research Institute in Rostock (Germany). From 1988 to 1992, he worked in the Department of Animal Nutrition (Agricultural University in Wageningen). In 1992 he obtained a PhD degree in animal nutrition from the University of Wageningen.He has authored 297 publications (papers, book chapters). He edited 3 books and 14 international conference proceedings. His total number of citation is 407. \r\nHe is member of various committees e.g.: American Society of Animal Science (ASAS, USA); the editorial board of the Acta Agriculturae Scandinavica, Section A- Animal Science (Norway); KRMIVA, Journal of Animal Nutrition (Croatia), Austin Food Sciences (NJ, USA), E-Cronicon Nutrition (UK), SciTz Nutrition and Food Science (DE, USA), Journal of Medical Chemistry and Toxicology (NJ, USA), Current Research in Food Technology and Nutritional Sciences (USA). From 2015 he has been appointed chairman of World Conference of Innovative Animal Nutrition and Feeding (WIANF).\r\nHis main research areas are related to pig and poultry nutrition: elimination of harmful effects of heat stress by nutrition tools, energy- amino acid metabolism in livestock, relationship between animal nutrition and quality of animal food products (meat).",institutionString:"University of Debrecen",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Debrecen",institutionURL:null,country:{name:"Hungary"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"25",title:"Veterinary Medicine and Science",slug:"veterinary-medicine-and-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"185543",firstName:"Maja",lastName:"Bozicevic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/185543/images/4748_n.jpeg",email:"maja.b@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"7144",title:"Veterinary Anatomy and Physiology",subtitle:null,isOpenForSubmission:!1,hash:"75cdacb570e0e6d15a5f6e69640d87c9",slug:"veterinary-anatomy-and-physiology",bookSignature:"Catrin Sian Rutland and Valentina Kubale",coverURL:"https://cdn.intechopen.com/books/images_new/7144.jpg",editedByType:"Edited by",editors:[{id:"202192",title:"Dr.",name:"Catrin",surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"54334",title:"Facility Layout Problem for Cellular Manufacturing Systems",doi:"10.5772/67313",slug:"facility-layout-problem-for-cellular-manufacturing-systems",body:'Facility layout problem (FLP) is the arrangement of a given number of non‐equal‐sized facilities within the given space. Good layout plan leads to improve machine utilization, part demand quality, efficient setup time, less work‐in‐process inventory and material handling cost. Generally speaking, efficient layout design provides two main advantages: (1) Reduction of between 30% to 70% in the total material handling cost (MHC) and (2) designing layout is the long term-plan, hence, any changes in layout impose some expenditure such as shutting down production or service line, losing process time and so on. Thus, designing proper facility layout plan would prevent lots of costs [1].
Several algorithms have been developed for FLP problem. The traditional approach to FLP called discrete representation often addressed by quadratic assignment problem (QAP) with the objective of minimizing a given function cost. There are two main assumptions in QAP: firstly, all facilities are equal size and shape; secondly, the location of facilities is known in a priori. However, these kinds of assumptions are not applicable in real‐world case studies. This approach to FLP is not suited to represent the exact location of facilities and cannot formulate FLP especially when facilities are unequal size and shape or if there are different clearances between the facilities. The more suitable approach to such a kind of cases is continuous representation rather than discrete. There are two ways to solve this problem. Chronologically, the first one attempts was to divide each facility into smaller size unit blocks, where the total area of those blocks is approximately equal to the area of the facility. There are two drawbacks to this method: firstly, the problem size is growing as the total number of blocks increase, and secondly, the exact shapes of facilities are ignored. The second approach to continuous problem assumes the exact shape and dimensions of the facilities (Table 1).
Approach | Plant site | Distance | Facilities | Mathematical formulation |
---|---|---|---|---|
Discrete | Divided in rectangular blocks with same size and shape; i.e., predetermined locations | Parameters Meller et al., [2] | Equal‐sized | QAP |
Continuous | No predetermined location, i.e., no blocks | Variable | Unequal‐sized | MIP |
FLP discrete approach versus FLP continuous approach.
The design of a cellular manufacturing system (CMS) includes: (1) cell formation (CF), (2) group layout, (3) group scheduling and (4) resource allocation. FLP to CMS is focusing on the second step of design of CMS which by itself is twofold: inter‐cell and intra‐cell layouts. The main objective of group layout is minimizing material handling cost (MHC) by arranging facilities in their corresponding cells and cells in floor. In this chapter, both demand and operation sequencing have been considered in optimizing the layout both at inter‐ and intra‐cellular levels. However, this was not the case with the literature; there is a dearth of papers that happened to take a discrete approach which really did address those factors. Moreover, in this chapter, a continuous approach has been adopted.
Here, a bi‐level mixed‐integer non‐linear programming continuous model has been developed for both intra‐cell and inter‐cell layout design sequentially. The problem is to arrange facilities that are machine tools in the leader problem and cells in the follower problem on the continual planar site. The objective function of leader and follower problems is minimizing the material handling cost at intra‐ and inter‐cellular levels, respectively. The developed mathematical model has some main novelties. Firstly, a continuous approach has been adopted; i.e., facilities take unequal size and their locations are not predetermined. Secondly, operation sequences and part demands are taken into consideration. Thirdly, the model has the ability to consider certain restrictions or preferences for cells and floors such as aisle. Finally, CMS design of disjoint cells is considered; hence, the overlapping elimination constraint is presented. Since the model is NP‐hard, a novel heuristic has been developed to solve the problem at two different levels (intra‐ and inter‐cellular) in a similar fashion to that used for developing the mathematical model. The developed heuristic is very different from its counterparts in the literature in the sense that it places the facilities radially, while dividing the production floor area into four quadrants. A real case study from the metal cutting industry has been used, where multiple families of inserts have been formed, each with its distinguished master plan.
The block facility layout problem that was originally formulated by Armour and Buffa [3] is concerned with finding the most efficient arrangement of m indivisible departments with unequal area requirements within a facility [4]. As defined in the literature, the objective of the block layout design problem is to minimize the material handling costs by considering the following two sets of constraints: (a) department and floor area requirements; i.e. departments cannot overlap, must be placed within the facility, and some must be fixed to a location or cannot be placed in specific regions; see Refs. [1, 3, 5, 6].
Cellular layout is considered as one of the special cases of the general FLP. There is an increasing interest in solving the block layout problem by taking a continuous approach [6]. Alfa et al., [9] have developed a model to simultaneously solve group formation and intra-cell. The objective function is the summation of both inter‐cell and intra‐cell flow times based on distance. They develop SA/heuristic algorithm to solve their model. SA has been used to find the initial solution, and then a heuristic approach based on the penalty model developed to improve the solution. The main limitation of this model is that the cell locations are predetermined.
Bazargan‐Lari and Kaebernick published few papers about design of cellular manufacturing [10–13]. Bazargan-Lari and Kaebernick [11] present a continuous plane approach where different constraints such as cell boundaries, non‐overlapping, closeness relationships, location restrictions/preferences, orientation constraints and travelling distances have been considered. They develop a hybrid method which combined a non‐linear goal programming (NLGP) and simulated annealing for machine layout problem. They have combined all constraints as goals using goal programming (GP) formulas. Generally speaking, GP divides those constraints into two main categories such as absolute or hard and goal or soft constraints. Hard constraints are those that have to be satisfied absolutely. It means that violation of any of them would yield to infeasibility. However, soft constraints can be compromised and be offset from desired set goals. Those constraints are considered as three separate sets of objectives. The first priority level includes all set of absolute or hard objectives which have to be absolutely satisfied such as non‐overlapped and cell boundary constraints. The second and third priority levels are preferences. The second priority is devoted to minimizing the area of the cells/shop floor, satisfying closeness relationship and orientation. Finally, the third priority is to minimize the total travelling cost. Overall, the approach of Bazargan‐Lari and Kaebernick is a combination of the NLGP and SA. They use the pattern search to solve their NLGP based on those three priorities. Since a pattern search is finding the local minimum, then they have been using SA to exit from the trap of local minimum. The core of their model is that they are generating alternative layout design by changing the order of priority levels 2 and 3 in each outer loop of SA algorithm. In other words, the starting point of new outer loop of SA is generated by the patter search algorithm. By changing the goal priority levels, huge pools of efficient solutions are generating. To solve this issue, they used what they called the filtering process to choose which sets of solutions have more different with the other ones. The logic behind this is giving decision‐makers the chance to consider how changing preferences’ priorities would impact the solutions.
The other important piece of research was written by Imam and Mir [14, 15]. Imam and Mir [14] introduce a heuristic algorithm to place unequal‐sized rectangular facilities in continuous plane by introducing the new concept of ‘controlled coverage’ by using ‘envelop blocks\'. In the initial solution, facilities are randomly placed in plane in the envelop block the size of which is much larger than the actual size of facility and is calculated by multiplying magnification factor with the facilities’ actual dimensions. Afterwards, during the heuristic iterations, the sizes of envelop blocks are gradually decreased by decreasing the magnification factor until the dimensions of envelopes will became equal to the dimensions of their corresponding facilities. By this approach, they were controlling the coverage of facilities together. The improvement iteration is based on the univariate search method. In this method, only one of the
Mir and Imam [15] have mentioned the second drawback above is addressed and try to improve their primary procedure. They develop a hybrid model by using SA for gaining the sub‐optimal initial feasible solution and then they improved it using a steepest descent approach. As they also noted that the number of optimization iterations depends of the magnification factor by which the size of the envelope blocks reduces when the magnification factor was being reduced. The algorithm stopped when the magnification factor is equal to one. So it is obvious that the computational cost and time are quite dependent on magnification factor.
On the other hand, there are various papers that considered alternative as a discrete approach. QAP is an NP‐complete problem, which means that when the size of the problem is increasing it cannot be solved by exact algorithm [16]. Hence, lots of efforts have been made to develop and apply heuristic and meta‐heuristic algorithm for this kind of problem. Wilhelm and Ward [16] have applied simulated annealing (SA) to solve QAP. Their results have been compared with the computerized relative allocation of facilities technique (CRAFT), biased sampling and revised Hillier problem and showed better quality solutions.
Baykasoğlu and Gindy [17] have applied SA for dynamic layout problem, discrete approach. They claim their proposed algorithm finds better solution. They compared their proposed algorithm to the three works done [18–20]. In the first comparison, their SA approach found optimum solution and revealed better solution than dynamic programming algorithm of Rosenblatt [18]. The second comparison has two experiments: first one carried out with no shifting cost and the SA algorithm found optimum solution and outperforms that Conway and Venkataramanan [19] genetic algorithm. In this experiment, relocation costs are included. The optimum solution was not found; however, the results of SA showed a slight improvement over that of Rosenblatt [18]. Finally, in the third comparison the data set obtained from Balakrishnan and Cheng [20]. They develop non‐linear genetic algorithm (NLGA). The comparison between the SA‐based approach and NLGA reveals the superiority of SA algorithm when the size of the problems is large. Since they have taken discrete approach to FLP, the only operator has been used in neighbourhood generation algorithm is the swap operator.
Tavakkoli-Moghaddam et al., [21] are developed a non-linear mathematical modelling to solve the cell formation in dynamic environment in which demand varies in each time horizon. The strength point of their model is that it is a multi‐objective model, i.e. considering more than one objective such as machine cost, operating cost, inter‐cell material handling cost and machine relocation cost. Three meta‐heuristic models, such as genetic algorithm (GA), simulated annealing (SA) and tabu search (TS), have been used to solve this problem. The results show SA outperforms compare to the two meta‐heuristics.
Safaei et al., [22] have developed a mixed integer programming which tries to minimize machine constant and variable costs, inter‐ and intra‐material handling cost and reconfiguration costs. They present a hybrid model called mean field annealing and simulated annealing (MFA‐SA) to solve the problem. MFA stands for mean field annealing which used to find the feasible initial solution for SA. Most of the developed heuristics in the literature have taken a discrete approach to FLP than a continuous one. Developing heuristics for the discrete problem is easier, because locations are predetermineda priori; hence, the only operator that is usually used is the swap operator, to shuffle the different facilities locations. Moreover, in the discrete approach no overlap would happen between facilities. On the other hand, it is harder to design heuristics for the continuous formulation of FLP since overlap takes place. It is usually the case that repeated repairs and checks of validity of the generated solutions have to take place.
The problem is to arrange facilities that are cells in the leader problem and machine tools in the follower problem in their respective space. The site has a rectangular shape with specified length (L) and width (W). Moreover, there is a horizontal aisle in the site by the same length as of site, however, with two different vertical dimensions
Scheme of shop.
The problem is formulated under the following assumptions:
CF is known in advanced.
Machines are not in the same size.
Machines must be located within a given area.
Machines are not allowed to overlap each other.
Cell\'s dimensions and orientation are predetermined.
Each part type has a number of operations that must be processed based on its operation sequence readily available from the route sheet of parts. It should be noted that the process sequence of each part is different.
The demand for each part type in known and is constant.
Material handling devices moving the one part between machines.
Inter‐ and intra‐cell movements related to the part types have different costs that are related to the distance travelled. We assume the rectangular distance between each pair of machines’ centroid.
In determining machine size and dimensions, the workspace required for operator usage and that needed to enforce between the different machines have been taken into account.
The mathematical formulation represented as below
Sets:
Parameters:
L Horizontal dimension of shop floor
W Vertical dimension of shop floor
Decision variables:
The continuous bi‐level programming problem is defined as: the intra‐cell layout mathematical formulation to layout the different machines (machines here are the facilities) of every cell c at a time is as follows:
s.t.
Equation (1) declares the objective function of leader problem, which is minimizes the total intra‐cell transportation cost of parts. Equations (2)–(5) are within site constraints that ensure each machine tool is assigned within the boundaries of its corresponding cell. Equations (6) and (7) force the overlap elimination for machine tools. Equation (8) represents the nature of the decision variables which are binary and non‐negative.
Finally, the inter‐cell layout problem tries to layout the different cells (cells here are the facilities) of the entire shop floor is as follows:
s.t
Aisle constraints:
Horizontal aisle:
Vertical aisle:
Equation (9) represents the objective function of follower program. The objective function minimizes the inter‐cell transportation cost of parts. The within‐site constraints are enforced by the set of constraints 10–13; i.e. these constraints ensure that cells are assigned within the boundaries of shop floor. Moreover, overlap elimination constraints are defined by constraints (14) and (15) which enforce the overlap elimination among cells. Equations (16) and (19) in the follower problem ensure that no cells would be assigned in the aisle boundaries. Finally, Eq. (20) specifies that the decision variables are binary and positive.
Simulated annealing is a stochastic neighbourhood search technique, which was initially developed by Metropolis and applied to combinatorial problems by Kirkpatrich et al. [25] for the first time.
To begin with, the basic of SA is based on statistical mechanics and comes from the similarity between the annealing of solids process and the solving method of combinatorial problem. If each feasible solution to the combinatorial optimization problem as a configuration of atoms and the objective function value of corresponding feasible solution as the energy of the system, then the optimal solution of combinatorial optimization problem is as like as the lowest energy state of the physical system [23]. The core of heuristic algorithms for solving the combinatorial problem is based on continual improvement, moving from one solution to another one in order to decrease the objective function from one iteration to next one. The same procedure is taking in quenching the system from high to low temperature in order to reach the required quality.
The core of SA algorithm is Metropolis algorithm, which allows uphill moves sometimes. Metropolis algorithm has four main elements [24, 25]. Figure 2 represents the simulated annealing steps.
Initial solution and description of system configuration
It is the starting point of SA algorithm. There are two main approaches for generating initial solution. One is generating initial solution randomly; by taking this approach feasibility of initial solution has to be considered. The second approach is getting feasible initial solution by adapting greedy algorithms or another heuristic algorithm. It has to be noted that initial solution should not be too good because escaping from its local optimum is hard.
Configuration changes
By moving from one configuration to another one, new neighbourhood solution is generated. These changes occurred by defining some operators which are responsible to make changes in the current solution.
Objective function that represent the quantitative measurement of goodness of a system
After finding any neighbour, the difference between objective value of new solution (
where
Annealing schedule/cooling schedule
Flowchart of simulated annealing.
The annealing schedule determines four rules:
1. Initial temperature: Since the annealing of solids is the basic of the SA approach, initial temperature is the melting point of SA algorithm and it should be defined in such a way that the solutions generated by high acceptance probability approximately close to one. Kirkpatrick et al. [25] noted that the initial temperature has to be large enough that 80% of generated solutions are accepted. Kia et al., [26] and Baykasoğlu and Gindy [17] defined initial solution high enough in such a way that 95% of generated candidates can be accepted using the following equation:
2. Temperature length
3. Termination: There are different approaches for stopping criteria such as
A specific number of iteration
Exact final temperature
No improvement for a number of iteration
Based on the literature review, there are different approaches for choosing SA parameters as explained briefly in Table 2.
Author | Initial temperature (T0) | Cooling rate (α) | Temperature reduction | Loop length | |
---|---|---|---|---|---|
Inner | Outer | ||||
Bazargan-Lari and Kaebernick [11] | 10 | 0.9 | |||
Baykasoğlu and Gindy [17] | |||||
Heragu and Alfa [27] | 999 | 0.90 | Epoch concept | ||
Wilhelm and Ward [16] | 10 | 0.9 | Epoch concept |
SA parameters.
Epoch: Predetermined specific number of successful pairwise interchanges at each temperature.
A unique heuristic is used to generate a feasible initial solution for SA algorithm [7, 8]. The explanation of the developed heuristic is provided in Section 4.2.1.1.
The mechanics of the developed algorithm are very different than any of the available heuristics in the literature. The whole idea behind our algorithm is to place facilities radially along vectors
At the start of the heuristic method, at first the given area is first divided into four equal size quadrants; i.e.
For each iteration of the outer loop, one random facility (called target facility)
Facilities are permitted to be placed within the boundaries of the given area. In order to satisfy this constraint, vector
Different repair functions based on the type of overlap are being developed to eliminate overlap. Repair functions guarantee the elimination of overlap between facilities and allocation of the facility within the boundaries of its corresponding quadrant. However, if the corresponding quadrant is too congested, the overlapped facility can be placed partially in a different quadrant. Nevertheless, no facilities are allowed to violate the given area boundaries. The inner loop has two main steps: in the first step, the overlap between facility
The second step of the inner loop consists of few iterations. In each iteration, as explained one facility
Since no facility is allowed to violate the given area\'s boundaries, there is a need to know how much distance left between facility
In order to generate new neighbourhood solution, two main operators, namely, move operator and swap operator, have been developed. The move operator tries to make facilities close to each other and also the swap operator switches the location of the two facilities. The details about these two operators explained below.
The developed move operator tries to reduce distances between the facilities. The logic behind this algorithm is decreasing the distance between one facility called in‐context facility, which is chosen randomly and the closest facility towards that. By moving the in‐context facility towards its closest facility, the possibility of overlap between in‐context facility and the rest of facilities is decreased. Main point here is that how much the maximum_movable_ distance is. Maximum_movable_ distance is the maximum length which if in‐context facility moved towards its closest facility no overlap will happen between them. The steps of move operator algorithm are explained below:
Randomly choose one facility, called in‐context facility
The Euclidean distance between the centroid of in‐context facility
Facilities are sorted based on the distances found in step 2 in the descending order. The first one among the above set would be the closest facility
Divide the in‐context facility
Find in which quadrant of in‐context facility
At this point the maximum _movable_ distance
Also:
where
It has to be noted, the length of both vectors
where
Based on in which quadrant closing facility is located,
Coordinates | ||
---|---|---|
Quadrant | ||
1 | ||
2 | ||
3 | ||
4 |
Hence, the length of vector
7. At this point the length of the movement, called
8. If the closest facility is adjacent to the facility
9. Finally, new coordinates of in‐context facility
New coordinates of target facility | ||
---|---|---|
Direction | ||
Quadrant 1 | ||
Quadrant 2 | ||
Quadrant 3 | ||
Quadrant 4 |
New coordinate of
The second operator of the developed SA is the swap operator which is switching positions of two facilities. The point here is how swap two facilities together that with the minimum possibility of overlap. To do that, the new concepts called free zone is defined. To apply this concept, a random facility called
One facility is chosen randomly, called facility
The closest facility to the
Maximum_movable_distance is calculated.
Free zone
Areas of facility
Among the rest of facilities those ones whose areas are greater than the area of facility
Randomly one facility among those facilities is found in step 6 is chosen, call it
Swap facility
Calculated the new coordinates of both
End
Assume:
LFZ: Length of the FZ
WFZ: Width of the FZ
AFZ: Area of FZ
In case of aisle, the operators move and swap vary. The details are presented in the below section.
The move operator has the same procedure as the move operator developed in case of no aisle. Hence, in case of aisle one facility is chosen randomly
The idea behind this function is if there is any overlap between
The steps of the move operator with aisle constraints are explained as follows:
Step 1. Move facility
Step 2. Check overlaps possibility between
Step 3. If there is any overlap, take appropriate repair function.
Step 4. Find the coordinates of
Step 5. End
Horizontal Aisle | ||
---|---|---|
Revised coordinate based on before‐aisle repair function‐horizontal aisle.
Vertical Aisle | ||
---|---|---|
Revised coordinate based on before‐aisle repair function‐vertical aisle.
Repair function‐horizontal aisle
Facility
Facility
Repair function‐vertical aisle
Facility
Facility
In this chapter, the parameters taken by Bazargan‐Lari and Kaebernick [10] have been used in the developed SA algorithm:
Initial temperature: 10
Cooling rate: 0.9
Temperature reduction:
Outer loop: 25
Inner loop:
The mechanics of developed heuristics.
Scheme of overlap between two facilities fi and fj.
Summary of developed initialization heuristic algorithm.
Angle calculation in move operator (I).
Concept of angle in move operator (II).
Free zone concept.
Before‐aisle move operator for horizontal aisle.
Before‐aisle move operator for vertical aisle.
Carbide Tool Inc. manufactures and distributes metalworking tools. The company is dedicated to developing specialized carbide, polycrystalline diamond (PCD) and cubic boron nitride (CBN) inserts, as well as multi‐task tooling for the aerospace, automotive and mould‐die industries. The company currently has a process layout configuration. Five different kinds of family cutting insert tools are produced. Each part has specific monthly demand. There are different kinds of unequal sized grinding machine tools. Some of the machine tools have identical copies on the shop floor to increase productivity. Therefore, the demand is being shared among the different copies of those machine tools. Different operations are performed on inserts with different sequences. The list of operations of each insert and the machine tools used for those operations are shown in Table 7.
ID | Machine | Dimension | Cutting insert tools | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Length | Width | Dog bone | S Shape | Triangular | Top notch | Diamond type 1 | Diamond type 2 | Diamond type 3 | ||
M1 | Double disk (1) | 12.67 | 5 | O1 | O2 | |||||
M2 | Blanchard (2) | 6 | 9.07 | O1 | O1 | O1 | O1 | |||
M3 | Wendt (3) | 8.5 6.8 | 6.1 9.45 | O1 | O2 | O4 | O2 | O2 | O2 | |
M4 | Polish (1) | 6 | 5 | O3 | ||||||
M5 | EWAG (1) | 4.3 | 7.3 | O7 | O3 | |||||
M6 | Surface grinding (2) | 7 | 6 | O4 | O5 | O5 | O3 | |||
M7 | Surface grinding (1) | 6 | 7.54 | O3 | O3 | |||||
M8 | Swing fixture (1) | 8 | 6 | O2 | O3 | |||||
M9 | V‐bottom (1) | 7 | 6 | O3 | O4 | |||||
M10 | Wire cutting (2) | 7.8 7.4 | 6.7 5.7 | O4 | O4 | |||||
M11 | Laser M/C (1) | 7.6 | 9.74 | O6 | ||||||
M12 | Brazing (1) | 4 | 1.8 | O6 | O5 | O1 | ||||
M13 | ETCH (1) | 3 | 4 | O5 | O5 | O6 | O4 | O8 | O7 | O3 |
ST1 | Inspection (1) | 4 | 3 | O6 | O6 | O7 | O5 | O9 | O8 | O4 |
ST2 | Wash (1) | 5 | 3 | O7 | O7 | O8 | O6 | O10 | O9 | O5 |
ST3 | Packing (1) | 16 | 8 | O8 | O8 | O9 | O7 | O11 | O10 | O6 |
Part demand | 1200 | 900 | 500 | 500 | 600 | 600 | 200 |
Machine tool characterizations and parts’ operations sequence.
The company\'s shop floor has a rectangular shape. There is no special material handling device for transforming unfinished products among machine tools. As demonstrated in Table 7, it is obvious that the number of operations performed on each part insert tool is large enough; hence, the amount of travel taking place every day on the production floor is quite significant. Additionally, as per their original layout, all the raw materials are transported from the back side of the shop to the front to start operation. This causes extra unnecessary travel, and hence extra material handling cost. The inspection and shipping stations which are two of the last steps as per the sequence of operations are not properly positioned in the current layout, because they are located in front of the floor. Since the current layout is process layout, similar machine tools are grouped together and located on one side of the floor. The original layout is causing too much traffic, since it is not taking into account the sequence of processing of parts. For an example, the primary operations of all insert tools are performed by the combination of three machine tools: Double Disk, Blanchard and Wendt. All Wendt machines are located in upper side of hall, while Blanchard and Double Disk machines are arranged in the lower side. Therefore, it could be concluded that there are too much back and forth travel being done between the two sides of the floor just for performing the first couple of operations.
Cell name | Machine tools/work station | ||||
---|---|---|---|---|---|
Primary | M1 (1) | M2 (2) | M4 (1) | M3 (3) | |
Grinding | M6 (2) | M8 (2) | M9 (1) | ||
Diamond | M10 (2) | M7 (1) | M5 (1) | M12 (1) | M11 (1) |
Final | M13 (1) | ST1 (1) | ST2 (1) | ST3 (1) |
GF results.
After having several meetings with the plant manager and executive board of the company, cellular layout was chosen as the best layout plan. Group formation was performed by the plant manager. Four cells with specific types of machine tools were designed as given in Table 8. The problem was solved using both the developed mathematical model and heuristic [7].
Cells | Dimension | Centroid | MHC (material handling cost) | ||
---|---|---|---|---|---|
Length | Width | ||||
Primary | 35 | 25 | 42.5 | 13.5 | $1191.550 |
Grinding | 26 | 20 | 74 | 50 | $520.588 |
Diamond | 30 | 20 | 45 | 59.22 | $764.580 |
Final | 30 | 20 | 75 | 8 | $1056.350 |
Aisle | 90 | 60 | 45 | 32.5 |
Intra‐cell material handling costs and inter‐cell dimensions of cells.
For the leader problem the layout of the different machine tools and work stations in their respective cells are being solved. The intra‐cellular travel cost per unit distance per one unit of each respective part are ¢10, ¢10, ¢15, ¢12 and ¢20, respectively for Dog Bone, S Shape, Triangular, Top Notch and all types of Diamond. The results for intra‐cellular layout are summarized in Table 9.
In the follower problem, the four cells with exact dimensions are located on the 90” × 60” shop floor. The inter‐cellular travel cost per unit distance for each unit of Dog Bone, S shape, Triangular, Top Notch, and Diamond are ¢12, ¢12, ¢18, ¢15, and ¢20, respectively. Material handling cost for the inter‐cellular layout is $7520.42. Table 11 shows the coordinates of cells based on inter‐cellular layout plan. The final sketch of inter‐cellular and intra‐cellular layout is shown in Figure 11.
Cell | Machine | Coordinates | |
---|---|---|---|
X | Y | ||
Primary | Blanchard | 14.5 | 18.30 |
Blanchard | 21.27 | 17.16 | |
Polish | 14.5 | 5.98 | |
Wendt | 6.58 | 7.64 | |
Wendt | 23.34 | 4.45 | |
Double Disc | 23.83 | 10 | |
Wendt | 7.25 | 17.72 | |
MHC | $734.581 | ||
Grinding | Surface grinding | 16.79 | 4 |
Surface grinding | 21.05 | 16 | |
Swing fixture | 18.97 | 10 | |
Swing fixture | 5.72 | 15.26 | |
V‐bottom | 8.55 | 6.76 | |
MHC | $669.480 | ||
Diamond | Wire cutting | 10.39 | 3.8 |
Wire cutting | 5.50 | 10 | |
Surface grinding | 18 | 5.64 | |
Brazing | 26 | 10 | |
Ewag | 11.53 | 16.31 | |
Laser M/c | 18.8 | 14.87 | |
MHC | $808.640 | ||
Final | ETCH | 26.19 | 9 |
Wash | 15 | 12.42 | |
Inspection | 4.89 | 9 | |
Packing | 15 | 5 | |
MHC | $2410.760 |
Machine coordinates based on heuristic.
The heuristic is applied to solve the intra‐cellular layout problems for each respective cell. The results obtained are provided in Table 10 and plotted in Figure 12. The material handling cost for the inter‐cellular layout is $6134.50 [6].
Method | Leader problem | Follower problem | |||
---|---|---|---|---|---|
Primary cell | Grinding cell | Diamond cell | Final cell | Shop | |
NLMIP | $ 1191.550 | $520.588 | $764.580 | $1056.350 | $7520.420 |
LMIP | $503.024 | $399.750 | $360.800 | $685.200 | $2838.6 |
SA | $701.592 | $526.004 | $787.940 | $856.508 | $6167.6 |
Comparisons between mathematical modelling and simulate annealing.
Inter‐cell and intra‐cell layout plan.
The comparison between the solutions provided non‐linear, linear model and simulated annealing is represented in Table 11. The linear model gives the exact optimum solution, however simulated annealing provides near optimum solution. The results also prove this fact. In both leader and follower problem, i.e. intra‐ and inter‐cell, respectively, the total material handling cost is less than costs provided by non‐linear mixed integer programming and simulated annealing.
The follower problem solved by simulated annealing has just assumed aisle.
Generally speaking, the linearized model obviously has yielded exact optimal results which proved to be better than those obtained by both the simulated annealing and the original non‐linear model. This was quite expected; in most cases simulated annealing resulted in better solutions than the non‐linear model; however, there were cases where the non‐linear model results were slightly better than those obtained by simulated annealing. The exception was for grinding cell and diamond cell where the non‐linear model outperformed slightly than simulated annealing.
Table 12 summarizes the results from both leader and follower problems. Both mean and SDV from the performed 10 runs are being provided. Standard deviation is good except for inter‐cell layout problem. For inter‐cell, we believe the algorithm is yet to be improved, and variance as shown in Table 12 is relatively high.
Cell | Average | SDV |
---|---|---|
Primary | $633.86 | $11.19 |
Grinding | $492.44 | $15.63 |
Diamond | $759.790 | $22.315 |
Final | $902.62 | $32.23 |
Inter‐cell | $5474.61 | $423.97 |
Mean and standard deviation of SA solutions.
Heuristic results showing layout presented at intra‐cell level for different cells (note: quadrant have been plotted demonstrating how facilities were spread around the different quadrants as per the working of the algorithm).
Cellular manufacturing system (CMS) layout has recently begun to receive heightened attention worldwide. The design of a CMS includes: (1) cell formation (CF), (2) group layout, (3) group and (4) resource allocation. An effective CMS implementation help any company improve machine utilization and quality; it also makes reduction in setup time, work‐in‐process inventory, material handling cost, part makespan and expediting costs.
There are two main approaches to FLP such as the discrete and continuous approaches. The discrete approach holds two main assumptions: one is all facilities are equal size and shape; the other one is predetermined locations of facilities. However, these kinds of assumptions are not realistic. The discrete approach is not suited to represent the exact locations of facilities. Moreover, this approach is not applicable for FLP with unequal size and shape facilities. The appropriate approach to this kind of FLP is continuous representation.
Generally speaking, the design of layout cannot be efficient if manufacturing attributes are not being considered in it. To illustrate, operation sequencing and parts’ demand are the two factors that have significant impacts on the flow rate which minimizes the main objective of FLP. The majority of literature studies have not considered these factors in the design of layout plan. Besides those manufacturing attributes, the available area of the shop that can be used for locating facilities is the other factor that has to be considered.
The facility layout problem for cellular manufacturing system in both inter‐ and intra‐cellular levels is considered in this chapter. The problem is to arrange facilities that are cells in the leader problem and machine tools in the follower problem in the continual planar site. Operation sequence and parts’ demand are the two main manufacturing attributes considered in the developed model. The MIP has been presented for both leader and follower problems. The novel aisle constraints have been presented in the mathematical formulation. Since the model is non‐linear, the linearized model has been developed. Additionally, a novel mathematical modelling has been developed for considering block constraints such as fixed departments and facilities. Since the FLP is an NP‐hard problem, novel heuristics presented in this chapter.
A novel heuristic model developed for finding feasible initial solution for designed meta‐heuristic algorithm, simulated annealing. The initial solution is based on the radial movement. In other words, the algorithm placed facilities along the specific radius with certain angle within site. The algorithm starts with dividing site into four equal‐sized quadrants, start placing facilities into first quadrant to the fourth one. After placing any new facility, the overlap\'s possibility between facilities and between facility and site boundaries is being checked. The different repair functions have been designed for different cases.
The SA algorithm developed for both inter‐ and intra‐cellular problem. The results of heuristic have used to initialize the developed SA algorithm. However, in order to have more efficient SA, the cell size used in heuristic algorithm is assumed two times of the original size of the cells. The two main operators used are move and swap operators. The move operator decreases the distance between facilities by moving the target facility towards the closest facility to it. Furthermore, the swap operator developed by defining the concept of the free zone.
Wines, like many other natural food products, contain varying amounts of different nitrogenous substances, the most important of which are proteins [1]. Proteins found in wine are mostly derived from grape berries. Using immunological methods, the specific polyclonal antibodies raised against the total proteins of a Portuguese Malvasia Fina monovarietal wine have been applied to analyse the origin of the wine proteins [2] which are entirely from the berry pulp. Three years later, a similar immune detection study was conducted [3] and three various polyclonal antibodies raised against must, yeast, and bacteria proteins were applied to analyse the origin of proteins in Chardonnay wine. The results indicated that most of the wine proteins came from grapes and many of them were glycoproteins, but there were also some proteins from the yeast. Yeast may affect the wine protein composition in two ways: by transferring proteins to the wine during the process of yeast autolysis and/or hydrolysing the must proteins via the exocellular protease present in the yeasts [1]. Furthermore, the analysis of a Sauvignon Blanc wine using nano-high performance liquid chromatography (HPLC)/tandem mass spectrometry showed that within the 20 identified proteins there were two proteins from bacteria and one from fungi, which could be attributed to sources in the vineyard including natural infections and improper handling during harvest [4].
In previous studies, proteins from grapes and wines have been reported with molecular weight (MW) in the range 6–200 kDa and isoelectric points (pI) in a range 3–9 kDa, as shown in Table 1 [4, 5, 6, 7, 8, 9]. However, the majority of wine proteins have MW and pI in a low range (20–30 and 4.1–5.8 kDa, respectively) and possess a net positive charge at the pH of the wine [2, 5, 10]. Studies on fractions of wine proteins using denaturing polyacrylamide gel electrophoresis have shown that the wine protein fraction is mainly composed of only a few polypeptides with MW ranging from 15 to 30 kDa, but a more detailed examination of whole protein fraction indicates a very large number of distinct polypeptides, exhibiting similar MW but subtle differences in electrical charges [11]. In that study, the authors also revealed via highly specific antibodies and N-terminal sequencing analysis that most wine polypeptides were structurally similar, suggesting the existence of a common precursor to most or all of the wine proteins which could generate all of the detected polypeptides by limited proteolysis.
Grape variety | Sample type | MW | pI | Reference |
---|---|---|---|---|
Sauvignon Blanc | Wine | 14.6–77.1 kDa | [4] | |
Riesling and Gewürztraminer | Grape | 11.2–190 kDa | [5] | |
Wine | 11.2–65 kDa | 4.1–8.0 | [5] | |
Chardonnay, Verdeca and Pinot Noir | Wine | 6–200 kDa | 3.6–9.0 | [6] |
Macabeo, Xarel-lo, Parellada and Malvar | Wine | 14–94 kDa | 3.0–5.6 | [7] |
Muscadine | Grape | 19–100 kDa | 5.6–7.6 | [8] |
Wine | 12–50 kDa | 4.6–8.8 | [9] |
Proteins reported in grapes and wines from different grape varieties: molecular weight (MW) and isoelectric point (pI).
In a study on Muscat of Alexandria wine [12], two major proteins with MW of 24 and 32 kDa, respectively, by SDS-PAGE are found with significant contribution to protein haze, and the 24 kDa protein produced about 50% more haze than the 32 kDa protein. The N-terminal sequence of the protein with MW of 24 kDa showed homology to thaumatin and to a number of plant thaumatin-like proteins, and the N-terminal sequence of enzyme digested peptides of the protein with MW of 32 kDa showed homology to plant chitinases [13]. Another study analysed the two main wine proteins present in sodium dodecyl sulphate capillary gel electrophoresis (SDS-CGE), which were determined with MW at 22 and 26 kDa, respectively, being concluded as corresponding to thaumatin-like proteins (TLPs) and chitinases [14]. Both thaumatin-like proteins and chitinases in wine are pathogenesis-related (PR) proteins derived from grapes.
Pathogenesis-related proteins are a group of plant proteins induced in pathological or related situations [15]. They were first discovered in tobacco as a result of a hypersensitive reaction to tobacco mosaic virus (TMV) [16]. PR proteins are typically acidic, of low molecular weight and highly resistant to proteolytic degradation and to low pH values. On the basis of similarities in amino acid sequences, serological relationship and/or enzymatic or biological activity, 11 families have been recognised and classified for tobacco and tomato [17]. Some of these PR protein family members have also been found in grapevine. The two prominent soluble proteins accumulated in grapes during ripening have been identified as chitinases (PR-3 family) and thaumatin-like proteins (PR-5 family) [18, 19]. However, in early studies, the β-1, 3-glucanases (PR-2 family), a potential indicator of pathogen attack, were not found in grape juice and/or berry extracts [19, 20, 21, 22]. With the accomplishment of grapevine genome sequencing programs in 2007 [23, 24] and the development of technology in protein analysis, proteomic analysis of grapevine has significantly improved knowledge of grape proteins and produced a better understanding of their characteristics [25]. These have consequently shown that there are more PR protein family members found in grapevines, such as osmotins (PR-5 family), β-1, 3-glucanases (PR-2 family) and the PR-10 proteins [26, 27, 28]. The two major PR proteins in wine, thaumatin-like proteins and chitinases, have been found present in both grape skin and pulp but not in grape seed [29].
Protein content in grape berries generally increases during ripening [30, 31, 32, 33]. The accumulation of PR proteins in grape berries during ripening has been observed [33, 34], with véraison being the trigger for gene expression. The expression of PR genes in grapes can also be modulated by the classical PR protein inducers such as wounding, chemical elicitors, pathogen attack and abiotic stress [18, 35]. Although the level of PR proteins in grape berries increases, the diversity of PR proteins decreases during grape ripening [36]. In addition, the level and proportion of PR proteins in grapes are dependent on the cultivar, region, climate and viticultural practices [36, 37, 38, 39, 40, 41]. Therefore, the actual protein composition in ripe grape berries is a result of the interactions between environmental conditions and intrinsic factors.
Sunlight-exposed fruits presented generally higher total soluble solids, anthocyanins and phenolic compounds and lower titratable acidity, malate and berry weight than non-exposed or canopy-shaded fruits [42, 43, 44, 45, 46]. One study on Riesling must show that the total amino acid concentration was significantly lower for fruits exposed to ambient UV-B levels than the low UV-B treatment and reduced UV-B affected amino acid composition, causing higher levels of arginine and glutamine, the main sources of amino acid for yeast metabolism [47]. In a later study [48], UV exclusion resulted in a lower concentration of not only phenolic compounds such as tannins but also PR proteins in grape skin. Interestingly, UV exclusion showed no effect on the PR proteins in the grape pulp.
Fungal infection can significantly influence the concentration of PR proteins in grapes. Grey mould caused by Botrytis cinerea is one of the main fungal diseases found in grapevines. A study that compared the juice from healthy grapes against Botrytis [49] showed that most proteins normally present in the healthy juice, namely, those between 20 and 30 kDa and a major glycoprotein at 62/64 kDa disappeared in the Botrytis infected juice. These results suggested that some proteinases secreted by Botrytis cinerea could degrade grape proteins. Another study on Botrytis cinerea infection on Chardonnay and Semillon grapes has also revealed that the concentrations of both PR proteins and total proteins in botrytised grape juice decreased compared to the juice from healthy grapes [50]. Conversely, powdery mildew infection on grape berries has been documented as increasing levels of PR proteins [48, 50, 51]. The strongly induced expression of some PR genes such as VvChi3 (coding for an acidic class III chitinase), VvGlub (coding for a basic class I glucanase) and VvTl2 (coding for a thaumatin-like protein) has been reported in powdery mildew infected grape berries [35]. A recent study [38] also showed that a number of proteins were induced in leaf tissues of Cabernet Sauvignon in response to powdery mildew infection, suggesting that Cabernet Sauvignon is able to initiate a basal defence but is unable to restrict fungal growth or slow down disease progression.
Extraction of PR proteins from grapes into juice can be greatly influenced by harvesting and grape processing conditions. Studies carried out in Australia [52, 53] showed that the juice obtained from mechanical harvesting coupled with long-distance transport had a higher concentration of PR proteins than juice obtained from hand harvesting fruit, which is likely due to the long skin contact during transport. A more recent study [54] conducted in New Zealand showed that Sauvignon Blanc juices from machine harvesting followed by 3 h skin contact had a significantly lower concentration of proteins, including PR proteins, than those from hand harvesting followed by 3 h skin contact. It was likely due to the greater juice yield in machine harvesting treatment and the interactions between proteins and phenolic compounds. In the following study [55], the authors confirmed that longer skin contact can increase the extraction of PR protein but the final concentration of PR proteins in juice can be modulated by the co-extracted phenolic compounds.
Protein haze can appear in bottled white wine as shown in Figure 1 if unstable proteins are not removed before the wine bottling. Although research studies have investigated the protein stabilisation in white wines [12, 13, 56, 57, 58, 59], the precise mechanism of protein haze formation still remains incompletely understood. One hypothesis [60] is that the first step in protein haze formation in wines is protein denaturation, a process accelerated by heating, after which the denatured proteins aggregate into large enough particles to be visually detected as haze, a process that may be affected either positively or negatively by non-protein wine components. In a recent study [61], two different mechanisms were proposed responsible for the heat-induced precipitation of the Arinto wine proteins: (1) at the higher pH values, it appears to result from isoelectric precipitation of proteins; (2) at the lower pH values, it seems to be associated with the presence of the non-protein wine components.
Protein haze formed in Sauvignon Blanc wine after heating at 80°C for 2 h.
As the main soluble proteins remain in the finished wine, the slow denaturation of PR proteins is thought to lead to protein aggregation, flocculation into a hazy suspension and formation of precipitates. A study using purified thaumatin-like proteins and chitinases from grape juice [62] suggested that chitinases are the primary cause of heat-induced haze formation and their concentration was directly correlated to the turbidity of heat-induced haze formation, but conversely, thaumatin-like proteins seemed to have no measurable impact on turbidity. This result was confirmed by a latter study [54] in which chitinases were found to have a good linear correlation with protein stabilisation in Sauvignon Blanc wine. Different protein haze formation behaviour between thaumatin-like proteins and chitinases could be due to the difference in the protein structure of these two types of proteins. The thaumatin-like proteins start unfolding (or denaturing) at 62°C, but most of the proteins will refold again when the temperature drops down [63]. In contrast, chitinases have a lower unfolding temperature and this denaturation is irreversible. Thus, once chitinases are unfolded, they may aggregate and precipitate out of the solution.
A study used a reconstitution method [64] to investigate the heat-induced aggregation behaviour of purified wine proteins and showed that the chitinases were the protein most prone to aggregate and the one that formed the largest particles. It is important to note that in the reconstitution experiment, four thaumatin-like protein isoforms, chitinases, phenolics and polysaccharides in a Chardonnay wine were isolated individually, and the wine stripped of these compounds was used as a base to reconstitute each of the proteins alone or in combination with the isolated phenolics and/or polysaccharides. Although phenolics and polysaccharides did not show a significant impact on aggregation behaviour of chitinases, the thaumatin-like protein isoforms varied in susceptibility to haze formation and interactions with phenolics and polysaccharides. These observations obtained in the model system indicated the importance of non-protein factors in affecting protein haze formation.
Phenolic compounds are important constituents in white wines since they contribute to many characteristics such as appearance, taste, style and quality [65]. A reduction in protein haze observed in commercial wine fined with the addition of polyvinylpolypyrrolidone (PVPP) suggested that phenolics may play a modulating role in haze formation [60]. The interaction between phenolics and proteins has been studied in relation to haze formation [59, 66]. Hydrophobic bonding was suggested as the major model of interaction in tannin-protein complexes [67]. A conceptual model for the protein-phenolics interaction is that a protein molecule has a fixed number of polyphenol binding sites and more sites were exposed when protein hydrogen bonds were broken [68]. Thus, the concentration of various phenolics in wine could have a great impact on heat stability through the interaction between phenolics and proteins.
Most polysaccharides present in musts are derived from grape cell walls, which include arabinogalactans, galacturonans, arabinans and smaller amounts of xyloglucans, cellulose and mannans. In the musts, type II arabinogalactan proteins are the main polysaccharides released from berries at the initial time of pressing; in the resulting wine, polysaccharides consist essentially of type II arabinogalactan proteins and rhamnogalacturonan-II [69]. Wine polysaccharides can affect the characteristic pattern of haze formation, increasing protein instability under moderately high temperature (40–50°C) [58]. However, mannoproteins, the polysaccharides derived from yeast, have been described as protecting wines from protein haze formation [70]. This polysaccharide is considered a promising prospect for preventing protein haze formation in white wine.
In a model wine system, maximum haze formed at pH 4.0–4.5 when ethanol was 12%, with less haze at lower or higher pH values [71]. However, a study using six Portuguese varietal wines indicated that wine proteins were increasingly heat stable when the pH increased from wine pH to 7.5 [58]. When the wine pH was adjusted to a typical wine pH value at 2.80, 3.00, 3.34, 3.65 and 3.85, lower bentonite dosages for stability was observed in lower pH wines, which was likely due to the improved efficiency of protein adsorption by bentonite at reduced wine pH [72]. This observation is in agreement with another study which investigated the protein haze formation in an Italian white wine as affected by pH ranging from 3.00 to 3.60, and the increased heat stability of wine was found in wines at lower pH [73].
It has been found that the sulphate anion in white wine was an essential factor that is required for protein haze formation [60]. In that study, the authors investigated various common wine anions such as sulphate, acetate, chloride, citrate, phosphate and tartrate and wine cations such as iron and copper. When these ions were added into artificial model wine solutions at typical white wine concentrations, only sulphate was found to be essential for protein haze formation. Furthermore, in this model wine system, the thaumatin-like protein (150 mg/L) required approximately 150 mg/L sulphate, and the chitinase (150 mg/L) required approximately 15 mg/L sulphate, for visible haze formation. The range of sulphate in Australian wines between 1994 and 1997 was from 56 to 1780 mg/L, with a mean of 385 mg/L, which exceeds the requirement of both thaumatin-like protein and chitinase for haze formation. A recent study [74] confirmed that sulphate was essential in the aggregation of grape chitinases and thaumatin-like proteins in a model system, and furthermore, the authors pointed out that the aggregation mechanisms of thaumatin-like proteins and chitinases are different and influenced by the ionic content of the model wine.
There are many ions present in wine, and these ions could play a role in white wine protein haze formation. Metal ions, particularly copper and iron, have been implicated in the formation of protein hazes in white wines, but as they are also associated with hazes of non-protein origin, their role in protein haze formation is very poorly understood [75]. The copper concentration in wine decreased after protein haze removal, suggesting that copper was part of the protein precipitation [76].
In a model wine system, increasing the ionic strength and electrical conductivity could increase protein haze formation after heating by reducing electrostatic repulsion of proteins [74, 77]. A study on Chilean Sauvignon Blanc wine reported that more protein haze formation in wine was observed by increasing the electrical conductivity [78]. However, a more recent study on a range of Australian white wines showed a negative correlation between protein haze formation and electrical conductivity [79]. These contradictory results could be related to the differences in other wine components of importance to protein haze formation.
A recent study [80] revealed the role of sulphur dioxide in the aggregation of heat unstable wine proteins. In comparison to chitinases, TLPs are more reactive to sulphur dioxide. The aggregation of TLPs could be triggered by sulphur dioxide during cooling after heating, with aggregates held by hydrophobic interactions and intermolecular disulphide bonds.
To avoid protein haze formation in bottled white wine, a protein stability test is usually conducted before bottling in the winery. If the wine is not protein stabilised, a range of bentonite fining trials will be carried out to determine the minimum required dosage of bentonite addition for protein stabilisation. The most common protein stability test is the heat test, which is a heating procedure to force protein haze formation. Wine samples are normally heated to 80°C for 6 h and then left to cool down to 4°C overnight. The turbidity in heated wine samples is measured by a nephelometer and expressed as nephelometer turbidity units (NTU). Turbidity measurement of less than 2.0 NTU is usually recommended. Different temperatures and durations of heating could have a great impact on the resulting haze formation [81]. A recent study suggests that the less severe condition of heating at 80°C for 2 h is more appropriate to predict bentonite requirement for wine stored in the short term to medium term [82]. The cooling temperature and time are also critical to the accuracy of heat test results. A recent study [83] investigated the influence of heating and cooling conditions on protein heat test results. In this study, white wines were heated at 80°C for a time ranging from 0.5 to 6.0 h and then cooled down for 0.5–18 h at 0, 4 or 20°C, respectively. The results indicated that heating at 80°C for 2 h and then cooled at 20°C for 3 h enabled the repeatable production of haze and bentonite requirement.
As traditional heat test is very time-consuming, near infrared (NIR) spectroscopy has been studied for its potential to predict protein stability with high efficiency [84]. Results from 111 white wines representing multiple regions and varieties in California showed that the turbidity of wine could be predicted from the short-wavelength NIR spectra, but further IR analysis on a large number of wines will be required for the application of NIR in the global wine industry, and the high cost of equipment may limit its widespread use.
There are also commercial reagents, i.e. Bentotest and Proteotest, available for winemakers to check the protein stability in white wine. In general, these commercial reagents are considered to be harsher than the heat test, and as a result, the addition of bentonite is normally more than the actual requirement for protein stabilisation. Other tests including trichloroacetic acid (TCA), tannin and ethanol tests were originally established for protein quantification by precipitation of proteins in wine, but they are rarely used in the winery for checking the protein stability as bentonite requirement can be significantly overestimated using those tests.
Removal of the proteins remaining in finished wine before bottling is critical for wine protein stabilisation. In the wine industry, bentonite, a swelling 2:1 aluminosilicate clay, is usually added to wine to remove the proteins [85]. As proteins in wine are positively charged at wine pH and bentonite carries a net negative charge, wine proteins can be absorbed onto bentonite by cationic exchange [1, 85]. Hsu and Heatherbell [86] have shown that wine proteins with higher pI (5.8–8.0) and intermediate MW (32–45 kDa) are preferentially removed by bentonite fining, but a proportion of wine proteins, which have a MW range from 60 to 65 kDa and pI range from 4.1 to 8.0, are highly resistant to removal by bentonite fining. In that study, the authors also pointed out that the removal of proteins with lower pI (4.1–5.8) and lower MW (12.6 and 20–30 kDa) was necessary for protein stabilisation. In contrast to this conclusion, another study [56] showed that the amount of protein depletion correlated linearly with the level of bentonite addition, implying no bentonite selectivity based on isoelectric point. Different conclusions from these two studies might be partly attributed to the different methods used to separate and quantify the proteins [87].
Bentonite fining is effective in removing proteins to stabilise the wine, but the use of bentonite could also remove some important aroma and flavour compounds [88], as well as result in loss of wine as lees [89]. Thus, alternative treatments for protein stabilisation that can reduce or eliminate the use of bentonite are of great interest for winemakers. Potential bentonite alternatives that have been studied are summarised in Table 2.
Bentonite alternatives | Advantages | Disadvantages | Reference |
---|---|---|---|
Ultrafiltration | |||
Ultrafiltration | Efficient in protein removal and reduction of bentonite requirement | High setup and running costs and possible loss of flavour compounds | [90, 91] |
Heating | |||
Short-time pasteurisation | Acceleration of protein denaturation in juice and thus lower bentonite requirement in wine | Impart negative sensory implications into wine | [94, 95] |
Enzymes | |||
Aspergillopepsin | Very active towards wine proteins during flash pasteurisation | Heating required for better efficiency, which may alter sensory properties of wine | [96] |
Aspartic acid protease | Capable of degrading chitinases without heat denaturation | Does not remove all PR proteins | [97] |
Polysaccharides | |||
Carrageenan | Effective in heat stabilising white wines at low addition rates without deleterious sensory impacts | Not a good settling agent | [99, 100] |
Mannoproteins | Protective effect on protein haze formation in wine | Long-term prevention of protein haze formation is unknown | [101] |
Chitin and chitosan | Interact and remove PR proteins and protect wine from browning | Interact with phenolics which may alter wine colour and texture | [103] |
Nanomaterials | |||
Nanoparticles | High surface area and efficiency in removing proteins | Removal of nanoparticles and the costs | [104, 105] |
Summary of bentonite alternatives for white wine protein stabilisation.
Ultrafiltration is very effective in removing proteins in wine, and up to 90% of wine proteins can be retained using a 10 kDa molecular weight cut-off membrane; as a result, bentonite requirement is greatly reduced [90]. However, in addition to the high setup and running costs, ultrafiltration could also result in the loss of beneficial aroma compounds in wine [91]. Studies on Riesling and Gewürztraminer wines using ultrafiltration with membrane nominal MW cut-off from 10 to 50 kDa showed that ultrafiltration can significantly decrease the overall aroma intensity and fruity, floral, sweet and honey/caramel aromas, but also increase vegetative aroma [92]. Furthermore, a reduction in browning colour (A420 nm) and total phenolics was also observed in filtered Riesling and Gewürztraminer wines [93].
Short-term pasteurisation (90°C for 1 min) can reduce bentonite requirement up to 70% [94], but heating of juice could impart negative sensory implications into resultant wine [95]. A recent study reported that applying heating of juice at 75°C for 1 min in the presence of a heat-tolerant protease, aspergillopepsin (AGP), derived from fungus Aspergillus niger, before fermentation showed a significant reduction of PR proteins without damaging wine quality [96]. In general, thaumatin-like proteins and chitinases are highly resistant to proteases, but heating unfolds these proteins and thus they can be degraded by protease. Another promising protease BcAP8 (aspartic acid protease) from Botrytis cinerea has been proven to be effective against grape chitinases during juice fermentation without the need for heating [97]. This protease could potentially reduce the bentonite requirement, but it is less effective on the degradation of thaumatin-like proteins.
Novel fining agents such as polysaccharides could be another potential class of bentonite alternatives [98]. Carrageenan, a food grade polysaccharide extracted from seaweeds, is effective in heat stabilising white wines at low addition rates (125–250 mg/L) without deleterious sensory impacts compared to bentonite treated wines [99, 100]. However, technical issues including frothing, slower filterability and risk of over-fining should be considered when applying carrageenan for protein stabilisation, particularly when it is used prior to or during fermentation. In addition, yeast mannoproteins, the highly glycosylated polypeptides present in yeast cell walls, have also been reported to have a protective effect on protein haze formation in wine [101]. Thus, mannoproteins extracted from purified yeast cell walls could be added into juice/wine to reduce the addition of bentonite, but further research is required to understand whether this protection against protein aggregation is suitable for long-term wine storage. Chitin [102] and chitosan [103], polysaccharides principally from Aspergillus niger, also have potential to remove haze-forming proteins in wine, but the wine colour and texture could be affected as chitosan interacts with phenolics and organic acids in wine.
Nanotechnology is currently a very active research topic in food science. Recently some researchers also become interested in using nanomaterials to remove proteins in wine. Magnetic steel nanoparticles coated with acrylic acid have been tested and are highly efficient in attracting and thus removing haze-forming proteins [104]. Another study using mesoporous nanomaterials to fine Muscat Ottonel and Pedro Ximenez wines also confirmed the high efficiency of nanomaterials in removing haze-forming proteins, and the loss of aroma compounds in wine due to addition of nanomaterials was even less than bentonite [105]. Nanomaterials have shown great potential as bentonite alternative to remove proteins in wine, but the cost of coating the nanoparticles and the removal of nanoparticles from wine after the treatment are the main concerns for their wide application in the wine industry.
Pathogenesis-related proteins in grapes play an important role in plant defence mechanisms. The concentration of PR proteins in grapes generally increases during ripening, but a number of factors, e.g. fungal infection and UV radiation, can influence the accumulation of PR proteins in grapes. Extraction of PR proteins from grapes into juice during grape processing, influenced by factors like harvesting (manual or mechanical), skin contact time and grape pressing, could have a great impact on PR protein extraction. The concentration of PR proteins in juice can largely predict their final concentration in wine. If PR proteins were not removed before white wine bottling, they could potentially denature and could form a haze in bottled wine, especially when wines are stored under unfavourable conditions, e.g. high temperatures. Bentonite is commonly used in white wine protein stabilisation, but bentonite is not specific to absorb proteins; it can also remove positive aroma compounds and cause the loss of some wine volume as bentonite lees. Alternatives to bentonite have been widely studied; however, they are not yet enough efficient to replace the cheap bentonite. Carrageenan and some proteolytic enzymes can be in a near future interesting tools to get white protein stabilisation, perhaps more efficient than the classic bentonite.
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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. 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I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. 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