Open access peer-reviewed chapter

Multi-Criteria Decision-Making Methods Application in Supply Chain Management: A Systematic Literature Review

Written By

Sharfuddin Ahmed Khan, Amin Chaabane and Fikri T. Dweiri

Submitted: October 15th, 2017 Reviewed: January 16th, 2018 Published: June 27th, 2018

DOI: 10.5772/intechopen.74067

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Abstract

Over the last decade, a large number of research papers, certified courses, professional development programs and scientific conferences have addressed supply chain management (SCM), thereby attesting to its significance and importance. SCM is a multi-criteria decision-making (MCDM) problem because throughout its process, different criteria related to each supply chain (SC) activity and their associated sub-criteria must be considered. Often, these criteria are conflicting in nature. For their part, MCDM methods have also attracted significant attention among researchers and practitioners in the field of SCM. The aim of this chapter is to conduct a systematic literature review of published articles in the application of MCDM methods in SCM decisions at the strategic, tactical and operational levels. This chapter considers major SC activities such as supplier selection, manufacturing, warehousing and logistics. A total of 140 published articles (from 2005 to 2017) were studied and categorized, and gaps in the literature were identified. This chapter is useful for academic researchers, decision makers and experts to whom it will provide a better understanding of the application of MCDM methods in SCM, at various levels of the decision-making process, and establish guidelines for selecting an appropriate MCDM method for managing SC activities.

Keywords

  • SCM
  • MCDM
  • strategic
  • tactical and operational decision-making
  • fuzzy
  • AHP/ANP
  • TOPSIS
  • systematic literature review

1. Introduction

Supply chain management (SCM) is crucial in today’s competitive environment and is steadily gaining serious research attention. Companies are facing challenges in discovering ways to fulfill ever-rising customer expectations and remain competitive in the market while keeping costs manageable. To that end, they must carry out investigations to isolate inefficiencies in their supply chain (SC) processes. If a company is buying raw materials for use in manufacturing a product, which it then sells to customers that mean the organization has an SC, which it must then manage. According to [1], SCM is:

“A set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouses, and stores, so that merchandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimize system-wide costs while satisfying service level requirements.”

SCM involves managing a series of activities relating to the planning, coordination and control of the movement of materials, parts and products from suppliers; the management of inventories of procured parts and of issues relating to production; an appropriate and cost-effective storage of products, and finally, transportation to the customer.

Another approach defines SCM in terms of different decision-making (DM) levels, namely, strategic, tactical and operational, and indicates that these DM levels of all scales optimize SC performance. On the other hand, traditional SC can be defined as a network which consists of suppliers, manufacturing facilities, distribution centers from which we procure raw materials, converted into finished good and deliver it to end user [2].

Certain differences exist between SCM and traditional logistics. Traditional logistics consists of actions that usually occur inside single organization boundaries, while SCM essentially defines a network of different companies working in coordination, with their main goal being to deliver finished products to customers. In addition, traditional logistics emphasizes SC functions, including purchasing, distribution and inventory management. SCM includes all the components of traditional logistics, but also tags on actions such as new product development, finance, marketing and customer service [3]. In this chapter, we consider following SC functions as mentioned in Figure 1.

Figure 1.

Considered SC functions.

1.1. Decision-making in SCM

An organization’s strategic, tactical and operational decision-making plays a vital role in ensuring that its SC is operating efficiently, allowing it to achieve the highest levels of customer satisfaction at an optimum cost. Decision-making at each level should focus on gaining a competitive edge and increasing market share. At each level, the nature of decision-making as well as and the related activities are different. There are three levels of decision-making which are (1) strategic-level decisions that have a long-lasting effect on the firm such as decisions related to warehouse location, capacity of warehouse and distribution centers, (2) tactical-level decisions that include decisions for the coming year such as decisions related to production, inventory level, absorption of uncertainty in production plan, transportation, and so on and (3) operational decisions that include decisions which are usually day-to-day such as loading/unloading, daily production plan, and so on.

1.2. MCDM methods and SCM

MCDM is a technique that combines alternative’s performance across numerous, contradicting, qualitative and/or quantitative criteria and results in a solution requiring a consensus [4, 5]. Knowledge garnered from many fields, including behavioral decision theory, computer technology, economics, information systems and mathematics is used. Since the 1960s, many MCDM techniques and approaches have been developed, proposed and implemented successfully in many application areas [6, 7]. The objective of MCDM is not to suggest the best decision, but to aid decision makers in selecting shortlisted alternatives or a single alternative that fulfils their requirements and is in line with their preferences [8, 9, 10] mentioned that at early stages, knowledge of MCDM methods and an appropriate understanding of the perspectives of DM themselves (players who are involved in decision process) are essential for efficient and effective DM.

There are several MCDM methods available such as the analytical hierarchal process (AHP), the analytical network process (ANP), TOPSIS, data envelopment analysis (DEA) and fuzzy decision-making. MCDM has been one of the fastest growing problem areas in many disciplines [11]. Over the past decade, many researchers have applied these methods in the field of industrial engineering, particularly in SCM, in making decisions. All the methods are equally capable of making decisions under uncertainty, and each one has its own advantages.

SCM is an MCDM problem because in the entire SC cycle, we must consider different criteria related to each sub-criterion of the SC cycle. In order to manage the entire SC, we have to identify the relationship of each criterion, which in turn impacts the performance of the SC. Based on the indicators identified, we then make decisions. This shows that decision-making is critical in managing the SC cycle, and that SCM is an MCDM problem. Supply chain management decisions are made under the conflicting criteria of maximizing profit and customer responsiveness while minimizing SC risk. Multiple criteria decision-making in supply chain management provides a comprehensive overview of multi-criteria optimization models and methods that can be used in SC decision-making [12].

1.3. Objective of the study

The objective of this study is to provide a systematic literature review on the application of MCDM methods in the decision process related to the considered SC functions (supplier selection, manufacturing, warehousing and logistics). The literature will be also categorized in terms of decision-making level (strategic, tactical and operational) and sector/application area during the decision process. This study is an attempt to answer the following research questions:

  1. What is the (%) distribution of MCDM methods applications in terms of different SC decision levels (strategic, tactical and operational) and in the SC functions considered?

  2. What is the (%) distribution of MCDM methods applications in terms of area of application?

  3. What are the top MCDM methods applied at each SC functions considered and at different SC decision levels (strategic, tactical and operational).

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2. Research methodology

In order to systematically carry out our literature review and use content analysis in the process, we adopt a methodology composed of four steps, based on the practical guidelines provided by Seuring & Gold [13] and Seuring et al. [14]. The process model consists of following steps as mentioned in Figure 2.

Figure 2.

Literature review methodology.

Step 1: The scope of the literature review in this chapter is limited to academic reviewed journals, conference papers and graduate dissertations because of their academic relevance, accessibility and ease of search. We did not include unpublished works, non-reviewed papers, working papers and book chapters. Inclusion of such papers is suggested as a future extension of our work. Papers using only MCDM methods and its integration with MODM methods were also included. However, papers focused solely on applied MODM methods were not included because it is beyond the scope and objective of this study. We searched within titles and abstracts in the Emerald, Elsevier, Taylor & Francis, Springer and Inderscience databases. Keywords that we used are “SCM and MCDM”; “Strategic and SCM”; “Tactical and SCM”; “Operational and SCM”; “MCDM and Supplier selection”; “MCDM and manufacturing”; “MCDM and warehousing”; “MCDM and logistics”; “DM and Supplier Selection”; “DM and Manufacturing”; “DM and Warehousing”; “DM and Logistics”; and so on. We used non-method-specific and method-specific MCDM keywords, DM keywords and SCM keywords. The material selection process led to samples of 140 papers published in more than 80 journals (the complete reference list is presented in a separate reference list).

Step 2: As recommended by [13], descriptive analysis must contain the distribution of selected reviewed articles in terms of time period, papers per country and across journals. Therefore, Figure 3 shows the annual distribution of selected articles across the period of study (2005–2017), Figure 4 shows the top five journals and Figure 5 shows distribution of articles published per country.

Figure 3.

Annual distribution of selected articles across the period of study (2005–2017).

Figure 4.

Top four journals of selected reviewed articles.

Figure 5.

Distribution of articles published per country.

Step 3: Category selection is the most important and essential step of any literature review paper. Category selection developed in this chapter is in line with the objectives set in Section 1.3 and will be able to answer research questions set in the last section. Therefore, in this chapter, we categorized papers in terms of decision-making level, SC functions considered, MCDM methods used and application area. In this step, each author assigned each paper to the specific category. Distribution of papers according to the DM level is aligned with the DM level explained in the abovementioned section, while the SC functions distribution is in line with the standard SC functions definition. Other dimensions, such as the application area and methods applied, were identified by reading the abstract, and in some cases, the conclusion of the article.

Step 4: Once the category selection has been developed, categories’ definitions have been clarified, and the papers were reviewed and categorized accordingly. We developed decision rules, and all authors were agreed on developed rules. All papers were read and categorized by individual reviewer, and all authors came up with the same distribution of articles. Only 11% of the papers had differences which were resolved by discussion. We paid attention to ensure the quality, reliability and validity of the review.

In the discussion given in the following section, we first show qualitative results and then proceed with a quantitative analysis.

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3. Qualitative results

In this section, a systematic review of the literature on the application of MCDM methods will be discussed. We divided the literature review into the functions of SCM considered; decision-making level, sectors/application area, and country. Category selection of any literature review paper is critical to select paper and accept or reject them based on inclusion or exclusion principles.

3.1. Supplier selection

Many authors have used different MCDM methods to select suppliers strategically for different purposes and in different applications such as the best supplier selection based on sustainability principles, to integrate information on supplier behavior in a fuzzy environment, to solve the supplier evaluation problem in companies with bulk production costs associated with raw materials, and so on. At the tactical level, supplier selection involves the administration of procurement activities such as multi-product supplier selection problem, factors affecting the supplier selection process, and so on. At the operational level, supplier selection usually involves one-time procurement due to unavoidable factors. At this level of decision-making, a small quantity of a product is usually procured from a supplier to run the production line. Table 1 shows the categorization of papers in terms of decision-making level, MCDM methods used, application area/sector and country in supplier selection.

PapersMCDM methods usedSector/application areaDM levelCountry
StrategicTacticalOperation
[15]FAHPSMEThailand
[16]FTOPSISGeneralTunisia
[17]FTOPSISFertilizer industryIndia
[18]AHP and VIKORAutomotiveIndia
[19]Fuzzy- Grey TheorySteel industryChina
[20]Fuzzy SystemsGeneralChina
[21]FAHPRailway operationsBrazil
[22]FAHPManufacturing industryItaly
[23]AHP-QFDAutomotivePakistan
[24]AHP -TOPSISGeneralIndia
[25]AHPAutomotiveIndia
[26]AHPAutomotivePakistan
[27]AHP-TOPSISIT industryMorocco
[28]Electre IIIPackaging industryRomania
[29]FuzzyAgri food industryIran
[30]Fuzzy SystemsGear manufacturingChina
[31]Fuzzy-QFDHospitalTurkey
[32]Fuzzy multi-objectiveGeneralUSA
[33]Fuzzy Axiomatic DesignPlastic material manufacturerDenmark
[34]FAHPAirlineNetherlands
[35]FANPAutomotiveMalaysia
[36]FTOPSISEnergyTurkey
[37]FTOPSISAutomotiveIran
[38]FAHPPublishing companyIran
[39]FTOPSIS and MILPAir filter manufacturingTurkey
[40]FTOPSISDetergent manufacturingIran
[41]FAHPGeneralTurkey
[42]Fuzzy-MISOFiber manufacturingIndia
[43]FAHP and Fuzzy Objective LPGarment manufacturingIndia
[44]FAHPGeneralIndia
[45]FAHPWashing machine manufacturingTurkey
[46]FTOPSIS and MCGPWatch manufacturingTaiwan
[47]TOPSISGeneralItaly
[48]FAHPSteelGreece
[49]FAHPManufacturer of medical ConsumablesTaiwan
[50]FTOPSISAutomotiveTurkey
[51]TOPSIS-Grey TheoryGeneralItaly
[52]Fuzzy SystemGeneralChina
[53]AHP - MILPGeneralChina
[54]Fuzzy-TOPSSISGeneralTaiwan
[52]Fuzzy Objective Linear ProgrammingGeneralIndia

Table 1.

Use of MCDM methods in supplier selection at different DM levels.

3.2. Manufacturing

Strategically, decision-making associated with manufacturing involves capacity constraints, manufacturing process selection, and make-or-buy decisions, development of a structural model to identify the cause-and-effect relationships between different criteria in manufacturing, and so on. At the tactical level, the decisions considered are related to the production rate, demand forecast errors, utilization of manufacturing facilities and administrative constraints. MCDM methods are widely applied at the tactical level of manufacturing decision-making. At the operational level, the decisions considered are related to the rejection rate during manufacturing, cycle time and machine breakdown.

Table 2 shows the categorization of papers in terms of decision-making level, MCDM methods used, application area/sector and country in manufacturing.

PapersMCDM methods usedSector/application areaDM levelCountry
StrategicTacticalOperation
[56]DEMATEL- ANP & Grey Relational Analysis- TOPSISGeneralChina
[57]SAW- WASPAS- TOPSISPVC windows manufacturingPoland
[58]AHP-DEMATELPaint shopIndia
[59]FAHP- PROMETHEEMining equipment manufacturerIndia
[60]AHP-TOPSIS and SWOTMining industryIran
[61]AHPHeat devices manufacturingPoland
[62]AHP-WASPASLaser cuttingSerbia
[63]TOPSISFace millingIndia
[64]TOPSISMicro EDMIndia
[65]Fuzzy SystemGeneralDenmark
[66]Fuzzy-DEMATELGeneralIran
[67]Probabilistic Fuzzy-ANPGeneralPhilippines
[68]Fuzzy-VIKORHard disk manufacturingMalaysia
[69]Fuzzy SystemGeneralUK
[70]Fuzzy-ANP and TOPSSISGeneralIndia
[71]DEMATEL-ANPRubber, tire and tube manufacturingDenmark
[72]AHPMining industryIndia
[73]Fuzzy Decision TreeAircraftUK
[74]Fuzzy-DEMATELGeneralTaiwan
[75]Type 2 Fuzzy hybrid experts systemSteel manufacturingIran
[76]Fuzzy Based Genetic AlgorithmGeneralBangladesh
[77]Fuzzy SystemGeneralFinland
[78]Fuzzy-TOPSISCement manufacturingIndia
[79]Fuzzy DEMATELAutomotiveIran
[80]ANP - VIKORTextileUSA
[81]ANP-DEMATELManufacturer of medical consumablesTaiwan
[82]DEMATELGeneralTaiwan
[83]Fuzzy - MPGeneralTurkey
[84]Fuzzy SetsGeneralSpain
[85]Fuzzy SystemGeneralUK
[86]Fuzzy - MPAutomotiveSpain
[87]Fuzzy Linear ProgrammingTextile industryMalaysia
[88]Fuzzy linear regression, Fuzzy time series and Fuzzy grey GMGeneralTurkey
[89]Fuzzy-AHPElectronicsTaiwan
[90]Fuzzy integrated model with fuzzy objective functionHome Appliance CompanyAzerbaijan

Table 2.

Use of MCDM methods in manufacturing at different DM levels.

3.3. Warehousing

Due to high client expectations, warehousing decisions are vital for organizations. At the strategic level, the decisions the authors and researchers in the literature considered were warehouse location selection, space utilization and urban distribution center location. Warehousing decisions have a long-term impact on the overall SC, and as a result, trade-offs must be made between conflicting alternatives. At the tactical level, the decisions considered were warehouse layout design, cost per order and response rate. Many authors applied MCDM methods for tactical warehousing decisions. At the operational level, the decisions considered were damages, reconciliation error and order fulfillment rate. Only a few applications of MCDM methods can be found in the literature on warehousing decisions at the operational level. Table 3 shows the categorization of papers in terms of decision-making level, MCDM methods used, application area/sector and country in warehousing.

PapersMCDM methods usedSector/application areaDM levelCountry
StrategicTacticalOperational
[91]FuzzyWinery’s warehouseArgentina
[92]AHPAutomotiveOthers
[93]AHPGeneralIndia
[94]FAHP and FTOPSISGeneralIndia
[95]FAHP and FANPGeneralTurkey
[96]FAHPInjection molded parts mfg.Pakistan
[97]FAHPRetail industrySerbia
[98]FuzzyGeneralIndia
[99]Fuzzy Multi-attributeGeneralChina
[100]FuzzyGeneralItaly
[101]Electre IIIGeneralPoland
[102]FANPGeneralIran
[103]FTOPSIS - MCGPAirlineTaiwan
[104]AHPLogistics service providerBangladesh
[105]TOPSISRetailing channelTaiwan
[106]Fuzzy SystemLogistics service providerTaiwan
[107]FTOPSISHome appliancesIran
[108]Fuzzy random multi-objective DM modelConstructionsChina
[109]FTOPSISAutomotiveTurkey
[110]FTOPSISGeneralIndia
[111]Fuzzy SystemLogistic companyCanada
[112]FTOPSISLogistic companyCanada
[113]FTOPSISGeneralTurkey
[114]Fuzzy-TOPSSISIC Packaging PlantTaiwan
[115]FuzzyGeneralTaiwan
[116]Fuzzy SystemGeneralJordon
[117]Fuzzy inference systemGeneralIndia

Table 3.

Use of MCDM methods in warehousing at different DM levels.

3.4. Logistics

Logistics plays an important role in overall SC performance. At the strategic level, the decisions researchers considered were logistics provider selection, service reliability and freight cost. Many authors applied MCDM methods and techniques at the strategic level of decision-making in logistics. At the tactical level, decisions considered relate to logistics network design, mode of transport and establishment of logistic centre. At the operational level, the decisions considered were damages, delayed shipment rate, cost per delivery and operational performance (wrong delivery rate, for instance). A few authors applied MCDM techniques at the operational level. Table 4 shows the categorization of papers in terms of decision-making level, MCDM methods used, application area/sector and country in supplier selection, manufacturing, warehousing, and logistics, respectively.

PapersMCDM methods usedSector /application areaDM LevelCountry
StrategicTacticalOperational
[118]FAHPLand transport providerColumbia
[119]DEAGeneralValencia
[120]FAHPGeneralThailand
[121]FQFD-TOPSISAgricultureFrance
[122]FAHPGeneralFrance
[94]FAHP and FTOPSISGeneralIndia
[123]FAHP-VIKORElectronics industryIndia
[124]ANP-BSCLogistics industryTurkey
[125]FAHP-TOPSISLogistic providerColumbia
[126]AHPLogistics industryTurkey
[127]FDelphi-AHPGeneralBrazil
[128]FAHPMilitary logisticsTaiwan
[129]FAHP-SWOTManufacturer of composite pipesUSA
[130]AHP-DEMATELGeneralIndia
[131]DEMATEL and FANPTelecommunicationTurkey
[132]Electre IIIGeneralRomania
[133]FuzzySemiconductor manufacturingTaiwan
[134]Fuzzy-VIKORGeneralNew Zealand
[135]FAHP and FTOPSSISTire manufacturingTurkey
[136]Fuzzy DEMATEL, Fuzzy ANP and Fuzzy VIKORCity logisticsSerbia
[137]FAHPLogistics companyThailand
[138]FAHP and FTOPSISCity LogisticsSerbia
[139]TOPSIS-AHPTelecommunicationIndia
[140]AHPAerospaceUSA
[141]QFD - FAHPHard disk component manufacturerUK
[142]FAHPFMCGBangladesh
[143]FAHPGarment material manufacturingChina
[144]Fuzzy-DelphiLogistic companyTurkey
[145]FTOPSISLogistic companyTurkey
[146]FTOPSISAutomotiveIndia
[147]AHP-TOPSISAutomotiveTurkey
[148]FAHPLogistic CompanyTurkey
[149]FTOPSISLogistic CompanyTurkey
[150]FAHPLogistic CompanyTurkey
[151]ISM-FuzzyBattery manufacturing companyDenmark
[152]ANPFMCGIndia

Table 4.

Use of MCDM methods in logistics at different DM levels.

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4. Quantitative result analysis

Today, competition is shifting from individual company performance to SC performance, thus making it essential for companies to measure their SC performance effectively and efficiently. To that end, they need to identify appropriate methods for evaluating the measurement of the performance of the entire SC cycle. This study will help managers, practitioners and researchers select the most appropriate MCDM method for managing their SC cycle and analyze the results quantitatively in the following aspects.

  1. Figure 6 shows the percentage of papers covering each MCDM methods at different strategic, tactical and operational levels of SC decisions.

  2. Figure 7 shows the top three MCDM methods for considered SC functions which are supplier selection, manufacturing, warehousing and logistics.

  3. Figures 8 and 9 show the paper distribution of the application areas for MCDM methods and its distribution at different levels of DM.

  4. Figure 10 shows the paper distribution at different levels of DM in considered SC functions.

Figure 6.

MCDM approach at the strategic, tactical and operational level.

Figure 7.

Top three MCDM methods of considered SC functions.

Figure 8.

Paper distribution of the application areas for MCDM methods.

Figure 9.

Paper distribution at different levels of DM.

Figure 10.

Paper distribution at different levels of DM of considered SC functions.

After summarizing the methods at the strategic, tactical and operational levels of decision-making, researchers and practitioners can now easily select most widely used MCDM methods in SC decision-making. Further, this research will help managers select a suitable technique from widely used MCDM methods for supplier selection, manufacturing, warehousing and logistics.

This study considered the application of MCDM methods in almost all sectors. After an extensive literature review, we found that many authors, managers and researchers have applied MCDM methods in many sectors and at different DM levels as mentioned in Figures 8 and 9, respectively.

Managers and decision makers need to select the best method at each level of decision-making in the entire SC. Figure 10 shows the use of MCDM methods at each level of decision-making in the entire considered SC cycle. We can infer from the figure that at a strategic level, 74% of papers applied MCDM methods in warehousing decisions; at a tactical level, 49% of papers used MCDM methods in manufacturing; and at an operational level, 15% of papers used MCDM methods in supplier selection.

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5. Discussion

The systematic literature review on the application of MCDM methods in supply chain management demonstrates the richness of MCDM to take different DM perspectives in the decision process. At the early stage of application, most of the methods focus on the fragmented SC structure with inefficient processes at the supply, manufacturing, warehousing and logistics levels. The subsequent integration of SC processes motivates the application of MCDM to improve the global decision process (more holistic). However, the integration comes with many challenges. First, more criteria have to be considered in the decision process. Second, the number of decision makers increases.

For long-term decisions (strategic and tactical), the decision process involves many criteria resulting from the information collected through the different SC functions. Also, most often, different decision makers (SC actors) are involved in the decision process. Thus, the use of MCDM methods is more suitable for long- and mid-term decisions (around 90%). However, the application of MCDM for short-term decisions (operational/real time) is limited to only 10%. Indeed, operational decisions are made very rapidly, and only partial information is usually available due to lack of data. Thus, the application of MCDM is not predominant and sometimes more difficult to implement.

For the supplier selection process, a detailed analysis (Figure 10) shows that MCDM methods are commonly used for long-term (strategic and tactical) decisions (85%). This result can be explained by the intensification of global commerce due to globalization and ever-greater competition, where supplier selection is critical. Thus, the appropriate supplier selection plays a vital role in organizational success. Conversely, the smallest number of researchers and DMs (15%) used MCDM methods at the operational level because of the fact that supplier selection and evaluation decisions have an impact on product quality, delivery, cost of material and service level. Therefore, decisions such as make-or-buy and the establishment of long-term contracts with suppliers must be aligned with the strategic goals of an organization and cannot merely be taken at the operational level.

Regarding the manufacturing process, long-term (strategic and tactical) decisions are also critical and include the development of technology selection and capacity expansion strategies to overcome shortage, minimize cost and maximize overall production efficiency. Again, the literature review analysis shows that 91% of MCDM methods are applied for long-term (strategic and tactical) decisions. For short-term manufacturing decisions, we are usually in the execution process of production, and there is less flexibility in decision-making. Thus, we notice that only 9% of the studies used MCDM methods for short-term decision-making (operational level).

Long-term warehousing decisions include the location and the design (technology choice and capacity) of the facility, which is one of the drivers of SC management. Moreover, the number of facilities (Warehouses and Distribution Centers) determines the total cost and the response time. For that reason, different criteria are used to make appropriate decisions. A significant amount of MCDM methods are applied in this context (96%). However, only 4% of papers applied MCDM methods at the operational level has been reported in our study.

For logistics activities, Figure 10 shows that many researchers and decision makers applied MCDM methods for long-term (strategic and tactical) efforts (approximately 90%). An effective and efficient logistics system requires long-term planning by considering future expansions, mergers and globalization. Long-term decisions help organizations reduce transportation cost and increase delivery service. For short-term decisions (operational), decision makers are obliged to take rapid action because of uncertainty caused by the manufacturing or logistics service provider. Therefore, this study shows that 11% of researchers and decision makers applied MCDM methods for short-term DM (operational), which is the highest among all considered SC functions.

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6. Limitations and further research directions

This chapter has a number of limitations, detailed as follows:

  1. This review is limited to academic reviewed journals and conferences. Therefore, unpublished work, non-reviewed articles, working papers and practitioners’ articles can be included in a future extension of this research.

  2. This review spanned 13 years (2005–2017), and we believe it is representative of the literature on the application of MCDM methods in SCM. Although this study is not exhaustive, it is however comprehensive (140 papers) enough to allow a conclusion.

  3. In the allocation of DM levels (strategic, tactical, and operational) in a particular paper, we followed the definition of DM level by David Simchi-Levi, Kaminsky, & Simchi-Levi (2008).

6.1. Future research directions

In SC, there are many criteria that have to be considered while making decisions. These criteria are often conflicting in nature, and MCDM methods and their integration with other methods are able to provide a framework for DMs in solving SCM problems and challenges. Moreover, with more globalization and digitalization, data availability is increasing, and the potential application of MCDM methods in tackling SCM problems under uncertainties becomes inevitable but needs a transformation. Based on this study, the following future research directions are proposed:

  1. In future, selected papers of this study can be further analyzed to know uncertain criteria that have been used for internal and external uncertainty in considered SC functions.

  2. Analyzed papers were further examined to know the exact criteria that were considered by DM in considered SC functions.

Notwithstanding the above-mentioned limitations and future research direction, we strongly believe that this study is in a very important area, namely, applications of MCDM methods in SCM, and should fill a gap in the literature.

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7. Concluding remarks

This chapter presented a systematic literature review on the application of MCDM methods in considered SC functions, namely, supplier selection, manufacturing, warehousing and logistics. A total of 140 papers covering a time span of 13 years from a well-known database were gathered, analyzed and categorized in terms of a long-term and short-term (strategic, tactical, and operational) DM perspective, MCDM method considered and application area. This study concludes that the research and application of MCDM methods in SCM have grown significantly in recent years. This study will help managers and decision makers select appropriate MCDM methods at a specific level of DM (strategic, tactical, and operational) and provide guidelines to managers to see which application area uses which MCDM methods. It is evident from the literature that shows that fuzzy sets and its integration with other MCDM methods have effectively and efficiently been applied at every level of the SC decision-making process as well as in the considered SC functions. This is because of the fact that due to digitalization and massive data available in the organization, perspective of SC has been totally changed. Organizations and decision makers need to change their traditional thinking when it comes to how to manage SC. Moreover, due to the availability of real-time data and information, the application of MCDM for short-term decisions will add a great value to the decision process and reduce uncertainty in managing SC. On the basis of this study, we conclude that fuzzy DM is the most appropriate MCDM method to use at different levels of DM and in building general DM models. Therefore, we believe that this systematic literature review answers all research questions that were raised and achieved the main objectives of our research.

References

  1. 1. Simchi-Levi D, Kaminsky P, Simchi-Levi E. Designing and managing the supply chain: Concepts, strategies, and case studies. 3rd Edition. Boston: McGraw-Hill, Irwin; 2008
  2. 2. Fox MS, Barbuceanu M, Teigen R. Agent-oriented supply-chain management. International Journal of Flexible Manufacturing Systems. 2000;12(2/3):165-188
  3. 3. Glykas MM. Effort based performance measurement in business process management. Knowledge and Process Management. 2011;18(1):10-33
  4. 4. Seydel J. Data envelopment analysis for decision support. Industrial Management & Data Systems. 2006;106(1):81-95
  5. 5. Kolios A, Mytilinou V, Lozano-Minguez E, Salonitis K. A comparative study of multiple-criteria decision-making methods under stochastic inputs. Energies. 2016;9(7)
  6. 6. Dadda A, Ouhbi I. A decision support system for renewable energy plant projects. In: Fifth International Conference on Next Generation Networks and Services (NGNS). 2014
  7. 7. Mardani A, Jusoh A, Nor KMD, Khalifah Z, Zakwan N, Valipour A. Multiple criteria decision-making techniques and their applications – A review of the literature from 2000 to 2014. Economic Research Istraživanja. 2015;28(1):516-571
  8. 8. Brito TB, Silva RC d S, Pereira RC, Medina AC. Discrete event Simulation combined with multi-criteria decision analysis applied to steel plant logistics system planning. Simulation. 2010:2126-2137
  9. 9. Belton V, Stewart T. Multiple Criteria Decision Analysis: An Integrated Approach. Kluwer Academic Publishers. 2002
  10. 10. Dooley A, Smeaton D, Sheath G, Ledgard S. Application of multiple criteria decision analysis in the NewZealand agricultural industry. Journal of Multi-Criteria Decision Analysis. 2009;16:39-53
  11. 11. Triantaphyllou E. Multi-Criteria Decision Making Methods: A Comparative Study. Boston, USA: Springer; 2013
  12. 12. Snyder LV et al. OR/MS models for supply chain disruptions: A review. IIE Transacrtions. 2016;48(2):89-109
  13. 13. Seuring S, Gold S. Conducting content-analysis based literature reviews in supply chain management. Supply Chain Management An International Journal. 2012;17(5):544-555
  14. 14. Seuring S, Müller M, Westhaus M, Morana R. Conducting a literature review-the example of sustainability in supply chains. Research Methodologies in Supply Chain Management in Collaboration with Magnus Westhaus. 2005;66:91-106
  15. 15. Chaising S, Temdee P. Application of a hybrid multi-criteria decision making approach for selecting of raw material supplier for small and medium enterprises. In: 2nd Joint International Conference on Digital Arts, Media and Technology 2017: Digital Economy for Sustainable Growth, ICDAMT 2017, 2017
  16. 16. Khemiri R, Elbedoui-Maktouf K, Grabot B, Zouari B. A fuzzy multi-criteria decision-making approach for managing performance and risk in integrated procurement–production planning. International Journal of Production Research. 2017;55(18):5305-5329
  17. 17. Agrawal V, Agrawal AM, Mohanty RP. Application of fuzzy MCDM in supplier selection of fertiliser manufacturing industry. International Journal of Business Performance and Supply Chain Modelling. 2017;9(2)
  18. 18. Luthra S, Govindan K, Kannan D, Mangla SK, Garg CP. An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production. 2017;140:1686-1698
  19. 19. Pang Q, Yang T, Li M, Shen Y. A fuzzy-Grey multicriteria decision making approach for green supplier selection in low-carbon supply chain. Mathematical Problems in Engineering. 2017;2017
  20. 20. Sarwar A, Zeng Z, Qadeer RA, Thuong TH, Talat N. A fuzzy multi-criteria decision making approach for supplier selection under fuzzy environment. In: Proceedings of the Eleventh International Conference on Management Science and Engineering Management. 2017. pp. 708-720
  21. 21. de Santis RB, Golliat L, de Aguiar EP. Multi-criteria supplier selection using fuzzy analytic hierarchy process: Case study from a Brazilian railway operator. Brazilian Journal of Operations and Production Management. 2017;14(3):428
  22. 22. Secundo G, Magarielli D, Esposito E, Passiante G. Supporting decision-making in service supplier selection using a hybrid fuzzy extended AHP approach. Business Process Management Journal. 2017;23(1):196-222
  23. 23. Khan SA, Dweiri F, Jain V. Integrating analytical hierarchy process and quality function deployment in automotive supplier selection. International Journal of Business Excellence. 2016;9(2):156-177
  24. 24. Gurung S, Phipon R. Multi-criteria decision making for supplier selection using AHP and TOPSIS method. International Journal of Engineering Inventions. 2016;6(2):13-17
  25. 25. Yadav V, Sharma MK. Multi-criteria supplier selection model using the analytic hierarchy process approach. Journal of Modelling Management. 2016;11(1):326-354
  26. 26. Dweiri F, Kumar S, Khan SA, Jain V. Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications. Nov. 2016;62:273-283
  27. 27. Hanine M, Boutkhoum O, Tikniouine A, Agouti T. Application of an integrated multi-criteria decision making AHP-TOPSIS methodology for ETL software selection. Spring. 2016;5(1):263
  28. 28. Criesta C, Criesta M. A multi-criteria decision making approach for supplier selection. In: MATEC Web of Conferences. 2016. vol. 94, pp. 1-9
  29. 29. Banaeian N, Mobli H, Fahimnia B, Nielsen IE, Omid M. Green supplier selection using fuzzy group decision making methods: A case study from the Agri-food industry. Computers and Operations Research. 2016:1-11
  30. 30. Orji IJ, Wei S. An innovative integration of fuzzy-logic and systems dynamics in sustainable supplier selection: A case on manufacturing industry. Computers and Industrial Engineering. 2015;88:1-12
  31. 31. Karsak EE, Dursun M. An integrated fuzzy MCDM approach for supplier evaluation and selection. Computers and Industrial Engineering. 2015;82:82-93
  32. 32. Moghaddam KS. Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty. Expert Systems with Applications. 2015;42(15–16):6237-6254
  33. 33. Kannan D, Govindan K, Rajendran S. Fuzzy axiomatic design approach based green supplier selection: A case study from Singapore. Journal of Cleaner Production. 2015;96:194-208
  34. 34. Rezaei J, Fahim PBM, Tavasszy L. Supplier selection in the airline retail industry using a funnel methodology: Conjunctive screening method and fuzzy AHP. Expert Systems with Applications. 2014;41(18):8165-8179
  35. 35. Dargi A, Anjomshoae A, Galankashi MR, Memari A, Tap MBM. Supplier selection: A fuzzy-ANP approach. Procedia Computer Science. 2014;31(Itqm):691-700
  36. 36. Öztürk BA, Özçelik F. Sustainable supplier selection with a fuzzy multi-criteria decision making method based on triple bottom line. Businees in Economic Research Journal. 2014;5(3):129-147
  37. 37. Shen L, Olfat L, Govindan K, Khodaverdi R, Diabat A. Resources, conservation and recycling a fuzzy multi criteria approach for evaluating green supplier ’ s performance in green supply chain with linguistic preferences. Resources, Conservation and Recycling. 2013;74:170-179
  38. 38. Shaverdi M, Heshmati MR, Eskandaripour E, Tabar AAA. Developing sustainable SCM evaluation model using fuzzy AHP in publishing industry. Procedia in Computer Science. 2013;17:340-349
  39. 39. Kilic HS. An integrated approach for supplier selection in multi-item/multi-supplier environment. Applied Mathematical Modelling. 2013;37(14–15):7752-7763
  40. 40. Roshandel J, Miri-Nargesi SS, Hatami-Shirkouhi L. Evaluating and selecting the supplier in detergent production industry using hierarchical fuzzy TOPSIS. Applied Mathematical Modelling. 2013;37(24):10170-10181
  41. 41. Arikan F. A fuzzy solution approach for multi objective supplier selection. Expert Systems with Applications. 2013;40(3):947-952
  42. 42. Kumar D, Singh J, Singh OP, Seema. A fuzzy logic based decision support system for evaluation of suppliers in supply chain management practices. Mathematical and Computer Modelling. 2013;57(11–12):2945-2960
  43. 43. Shaw K, Shankar R, Yadav SS, Thakur LS. Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Systems with Applications. 2012;39(9):8182-8192
  44. 44. Koul S, Verma R. Dynamic vendor selection based on fuzzy AHP. Journal of Manufacturing Technology Management. 2012;22(8):963-971
  45. 45. Kilincci O, Onal SA. Fuzzy AHP approach for supplier selection in a washing machine company. Expert Systems with Applications. 2011;38(8):9656-9664
  46. 46. Liao CN, Kao HP. An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management. Expert Systems with Applications. 2011;38(9):10803-10811
  47. 47. Jadidi O, Firouzi F, Bagliery E. TOPSIS method for supplier selection problem. World Academy of Science, Engineering and Technology. 2010;47(11):956-958
  48. 48. Chamodrakas I, Batis D, Martakos D. Supplier selection in electronic marketplaces using satisficing and fuzzy AHP. Expert Systems with Applications. 2010;37(1):490-498
  49. 49. Tseng M-L. Using linguistic preferences and grey relational analysis to evaluate the environmental knowledge management capacity. Expert Systems with Applications. 2010;37(1):70-81
  50. 50. Boran FE, Genç S, Kurt M, Akay D. A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications. 2009;36(8):11363-11368
  51. 51. Jadidi O, Hong TS, Firouzi F, Yusuff RM. An optimal grey based approach based on TOPSIS concepts for supplier selection problem. International Journal of Management Science and Engineering Management. 2009;4(2):104-117
  52. 52. Wang J, Zhao R, Tang W. Fuzzy programming models for vendor selection problem in a supply chain. Tsinghua Science and Technology. 2008;13(1):106-111
  53. 53. Xia W, Wu Z. Supplier selection with multiple criteria in volume discount environments. Omega. 2007;35(5):494-504
  54. 54. Chen C-T, Lin C-T, Huang S-F. A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics. 2006;102(2):289-301
  55. 55. Kumar V, Alvi FS. Use of high-speed microjets for active separation control in diffusers. AIAA Journal. 2006;44(2):273-281
  56. 56. Zhang H, Peng Y, Tian G, Wang D, Xie P. Green material selection for sustainability: A hybrid MCDM approach. PLoS One. 2017;12(5)
  57. 57. Rudnik K. Decision-making in a manufacturing system based on MADM methods. In: Scientific Proceedings XIV International Congress Machines. Technolоgies. Materials, 2017, vol. VI, pp. 472-475
  58. 58. Kumar A, Mussada E, Ashif M, Tyagi D, Srivastava A. Fuzzy Delphi and hybrid AH-MATEL integration for monitoring of paint utilization. Advances in Production and Engineering Management. 2017;12(1):41-50
  59. 59. Kavilal EG, Prasanna Venkatesan S, Harsh Kumar KD. An integrated fuzzy approach for prioritizing supply chain complexity drivers of an Indian mining equipment manufacturer. Resources Policy. 2017;51:204-218
  60. 60. Shahba S, Arjmandi R, Monavari M, Ghodusi J. Application of multi-attribute decision-making methods in SWOT analysis of mine waste management (case study: Sirjan’s Golgohar iron mine, Iran). Resources Policy. 2017;51:67-76
  61. 61. Kluczek A. Application of multi-criteria approach for sustainability assessment of manufacturing processes. Management and Production Engineering Review. 2016;7(3):62-78
  62. 62. Madić M, Antuchevičienė J, Radovanović M, Petković D. Determination of manufacturing process conditions by using MCDM methods: Application in laser cutting. The Engineering Economist. 2016;27(2)
  63. 63. Kumar J, Singh GS. Optimization of machining parameters of titanium alloy steel using: TOPSIS method. International Journal of Scientific Research in Science, Engineering and Technology. 2016;2(2):1019-1022
  64. 64. Manivannan R, Kumar MP. Multi-response optimization of micro-EDM process parameters on AISI304 steel using TOPSIS. Journal of Mechanical Science and Technology. 2016;30(1):137-144
  65. 65. Govindan K, Diabat A, Madan Shankar K. Analyzing the drivers of green manufacturing with fuzzy approach. Journal of Cleaner Production. 2015;96:182-193
  66. 66. Hashemzadeh G, Hazaveh MG. Identification of factors affecting production costs and their prioritization based on MCDM (case study : Manufacturing company). MAGNT Research Report. 2015;3(2):31-43
  67. 67. Ocampo L, Clark E, Tanudtanud KV. A sustainable manufacturing strategy from different strategic responses under uncertainty. Journal of Industrial Engineering. 2015;2015:1-11
  68. 68. Rostamzadeh R, Govindan K, Esmaeili A, Sabaghi M. Application of fuzzy VIKOR for evaluation of green supply chain management practices. Ecological Indicators. 2015;49:188-203
  69. 69. Susilawati A, Tan J, Bell D, Sarwar M. Fuzzy logic based method to measure degree of lean activity in manufacturing industry. Journal of Manufacturing Systems. 2015;34(C):1-11
  70. 70. Aravind Raj S, Vinodh S, Gaurav WS, Shiva Sundaram S. Application of hybrid MCDM techniques for prioritising the gaps in an agile manufacturing implementation project. International Journal of Services Operations Management. 2014;17(4)
  71. 71. Govindan K, Kannan D, Shankar M. Evaluation of green manufacturing practices using a hybrid MCDM model combining DANP with PROMETHEE. International Journal of Production Research. 2014;53(21):6344-6371
  72. 72. Sivakumar R, Kannan D, Murugesan P. Green vendor evaluation and selection using AHP and Taguchi loss functions in production outsourcing in mining industry. Resources Policy. 2015;46:64-75
  73. 73. Evans L, Lohse N, Summers M. A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information. Expert Systems with Applications. 2013;40(16):6412-6426
  74. 74. Lin RJ. Using fuzzy DEMATEL to evaluate the green supply chain management practices. Journal of Cleaner Production. 2013;40:32-39
  75. 75. Zarandi MHF, Gamasaee R. A type-2 fuzzy system model for reducing bullwhip effects in supply chains and its application in steel manufacturing. Scientia Iranica. 2013;20(3):879-899
  76. 76. Chakrabortty RK, Hasin MaA. Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA) approach. International Journal of Industrial Engineering Computations. 2013;4(1):1-12
  77. 77. Kristianto Y, Helo P, Jiao J, Sandhu M. Adaptive fuzzy vendor managed inventory control for mitigating the bullwhip effect in supply chains. European Journal of Operational Research. 2012;216(2):346-355
  78. 78. Muralidhar P, Ravindranath K, Srihari V. Evaluation of green supply chain management strategies using fuzzy AHP and TOPSIS. Engineering. 2012;2(4):824-830
  79. 79. Irajpour A, Golsefid-Alavi M, Hajimirza M, Soleimani-Nezhad N. Evaluation of the most effective criteria in green supply chain Management in Automotive Industries Using the fuzzy DEMATEL method. Journal of Basics and Applied Science Research. 2012;2(9):8952-8961
  80. 80. Wu GC, Ding JH, Chen PS. The effects of GSCM drivers and institutional pressures on GSCM practices in Taiwan’s textile and apparel industry. International Journal of Production Economics. 2012;135(2):618-636
  81. 81. Tseng M-L. Using a hybrid MCDM model to evaluate firm environmental knowledge management in uncertainty. Applied Soft Computing. 2011;11:1340-1352
  82. 82. Lin R-J. Using fuzzy DEMATEL to evaluate the green supply chain management practices. Journal of Cleaner Production. 2011;40:32-39
  83. 83. Bilgen B. Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem. Expert Systems with Applications. 2010;37(6):4488-4495
  84. 84. Campuzano F, Mula J, Peidro D. Fuzzy estimations and system dynamics for improving supply chains. Fuzzy Sets and Systems. 2010;161:1530-1542
  85. 85. Feili HR, Moghaddam MS, Zahmatkesh R. Fuzzy material requirements planning. The Journal of Mathematics and Computer Science. 2010;1(4):333-338
  86. 86. Peidro D, Mula J, Poler R, Verdegay JL. Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems. 2009;160(18):2640-2657
  87. 87. Elamvazuthi I, Ganesan T, Vasant P, Webb JF. Application of a fuzzy programming technique to production planning in the textile industry. International Journal of Computer Science and Information Security. 2008;6(3):6
  88. 88. Tozan H, Vayvay O. Fuzzy forecasting applications on supply chains. WSEAS Transaction System. 2008;7(5):600-609
  89. 89. Hsu CW, Hu AH. Green supply chain management in the electronic industry. International Journal of Environmental Science and Technology. 2008;5(2):205-216
  90. 90. Aliev RA, Fazlollahi B, Guirimov BG, Aliev RR. Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management. Information Science (Ny). 2007;177(20):4241-4255
  91. 91. Buonamico N, Muller L, Camargo M. A new fuzzy logic-based metric to measure lean warehousing performance. Supply Chain Forum An International Journal. 2017;0(0):1-16
  92. 92. Vidal Vieira JG, Ramos Toso M, da Silva JEAR, Cabral Ribeiro PC. An AHP-based framework for logistics operations in distribution centres. International Journal of Production Economics. 2017;187:246-259
  93. 93. Raut RD, Narkhede BE, Gardas BB, Raut V. Multi-criteria decision making approach: A sustainable warehouse location selection problem. International Journal of Management Concepts and Philosophy. 2017;10(3):260-281
  94. 94. Singh RK, Gunasekaran A, Kumar P. Third party logistics (3PL) selection for cold chain management: A fuzzy AHP and fuzzy TOPSIS approach. Annals of Operations Research. 2017:1-23
  95. 95. Apak S, Tozan H, Vayvay O. A new systematic approach for warehouse management system evaluation. Novi sustavni Pristup ocjenjivanju sustava Upravlj. skladištem. 2016;23(5):1439-1446
  96. 96. Khan SA, Dweiri F, Chaabane A. Fuzzy-AHP approach for warehouse performance measurement. IEEE International Conference on Industrial Engineering and Engineering Management. 2016;2016–Decem:871-875
  97. 97. Dobrota M, Macura D, Šelmi M. Multi criteria decision making for distribution center location selection- Serbia case study. In: 2nd Logistics International COnference. 2015. pp. 32-37
  98. 98. Dey B, Bairagi B, Sarkar B, Sanyal SK. Warehouse location selection by fuzzy multi-criteria decision making methodologies based on subjective and objective criteria. International Journal of Management Science and Engineering Management. 2015;9653(January 2016):1-17
  99. 99. Rao C, Goh M, Zhao Y, Zheng J. Location selection of city logistics centers under sustainability. Transportation Research Part D Transport and Environment. 2015;36:29-44
  100. 100. Cagliano AC, Pilloni MT, Rafele C. A multi-criteria fuzzy method for selecting the location of a solid waste disposal facility. International Journal of Management and Decision Making. 2014;13(3):221-249
  101. 101. Zak J, Węgliński S. The selection of the logistics center location based on MCDM/a methodology. In: Transportation Research Procedia. 2014. vol. 3. no. July. pp. 555-564
  102. 102. Rezaeiniya N, Ghadikolaei AS, Mehri-tekmeh J, Rezaeiniya H. Fuzzy ANP approach for new application : Greenhouse location selection; a case in Iran. The Journal of Mathematics and Computer Science. 2014;8:1-20
  103. 103. Chen KH, Liao CN, Wu LC. A selection model to logistic centers based on TOPSIS and MCGP methods: The case of airline industry. Journal of Applied Mathematics. 2014;2014
  104. 104. Bagum N, Abul C, Rashed A. Multi-criteria analysis for the selection of location for distribution Center. Review of General Management. 2014;20(2):67-82
  105. 105. Jinh Chang H. A TOPSIS model for chain store location selection. Integrative and Business Economics. 2014;4(1):410-416
  106. 106. Ding JF. Applying an integrated fuzzy MCDM method to select hub location for global shipping carrier-based logistics service providers. WSEAS Transactions on Information Science and Applications. 2013;10(2):47-57
  107. 107. Ashrafzadeh M, Rafiei FM, Isfahani NM, Zare Z. Application of fuzzy TOPSIS method for the selection of warehouse location: A case study. Interdisciplinary Journal of Contemporary Research in Business. 2012;3:655-671
  108. 108. Xu J, Li Z. Multi-objective dynamic construction site layout planning in fuzzy random environment. Automation in Construction. 2012;27:155-169
  109. 109. Boran F. An integrated intuitionistic fuzzy multi criteria decision making method for facility location selection. Mathematical and Computational Applications. 2011;16(2):487-496
  110. 110. Dheena P, Mohanraj G. Multicriteria decision-making combining fuzzy set theory, ideal and anti-ideal points for location site selection. Expert Systems with Applications. 2011;38(10):13260-13265
  111. 111. Awasthi A, Chauhan SS, Goyal SK. A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty. Mathematical and Computer Modelling. 2011;53(1–2):98-109
  112. 112. Awasthi A, Chauhan SS, Omrani H. Application of fuzzy TOPSIS in evaluating sustainable transportation systems. Expert Systems with Applications. 2011;38(10):12270-12280
  113. 113. Ekmekçioĝlu M, Kaya T, Kahraman C. Fuzzy multicriteria disposal method and site selection for municipal solid waste. Waste Management. 2010;30(8–9):1729-1736
  114. 114. Yang T, Hung C-C. Multiple-attribute decision making methods for plant layout design problem. Robotics and Computer-Integrated Manufacturing. 2007;23(1):126-137
  115. 115. Ishii H, Yung LL, Kuang YY. Fuzzy facility location problem with preference of candidate sites. Fuzzy Sets and Systems. 2007;158(17):1922-1930
  116. 116. Dweiri F, Meier Fa. Application of fuzzy decision-making in facilities layout planning. International Journal of Production Research. 2006;34(11):3207-3225
  117. 117. Deb SK, Bhattacharyya B. Fuzzy decision support system for manufacturing facilities layout planning. Decision Support Systems. 2005;40(2):305-314
  118. 118. Ramirez-Florex G, Tabares-urrea N, Osorio-Gomez J. Fuzzy AHP for 3PL supplier’s performance evaluation considering risk. Faculty of Engineering Magazine. 2017;26(45):165-172
  119. 119. Martí L, Martín JC, Puertas R. A DEA-logistics performance index. Journal of Applied Econometrics. 2017;20(1):169-192
  120. 120. Srisawat P, Kronprasert N, Arunotayanun K. Development of decision support system for evaluating spatial efficiency of regional transport logistics. In: Transportation Research Procedia. 2017. vol. 25. pp. 4836-4855
  121. 121. Yazdani M, Zarate P, Coulibaly A, Zavadskas EK. A group decision making support system in logistics and supply chain management. Expert Systems with Applications. 2017;88:376-392
  122. 122. Moskolaï J, Houé R, Archimède B, Nlong JM. Sustainability assessment of a transportation system under uncertainty: An integrated multicriteria approach. In: IFAC-PapersOnLine. 2017. vol. 50. pp. 7742-7747
  123. 123. Prakash C, Barua MK. An analysis of integrated robust hybrid model for third-party reverse logistics partner selection under fuzzy environment. Resources, Conservation and Recycling. 2016;108:63-81
  124. 124. Kucukaltan B, Irani Z, Aktas E. A decision support model for identification and prioritization of key performance indicators in the logistics industry. Computers in Human Behaviour. 2016;65:346-358
  125. 125. Osorio JC, Manotas DF, García JL. Multicriteria 3PL selection with risk consideration. Research in Computer Science. 2016;109:51-57
  126. 126. Gürcan ÖF, Yazıcı İ, Beyca ÖF, Arslan ÇY, Eldemir F. Third party logistics (3PL) provider selection with AHP application. Procedia - Social Behaviour Science. 2016;235:226-234
  127. 127. Bouzon M, Govindan K, Rodriguez CMT, Campos LMS. Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP. Resources, Conservation and Recycling. 2016;108:182-197
  128. 128. Wang YJ, Han TC, Chou MT. Applying fuzzy AHP in selection of transport modes for Kinmen military logistics. Journal of Marine Science and Technology. 2016;24(2):222-232
  129. 129. Tavana M, Zareinejad M, Di Caprio D, Kaviani MA. An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics. AppliedSoft Computing Journal. 2016;40:544-557
  130. 130. Mangla SK, Govindan K, Luthra S. Critical success factors for reverse logistics in Indian industries: A structural model. Journal of Cleaner Production. 2016;129:608-621
  131. 131. Uygun Ö, Kaçamak H, Kahraman ÜA. An integrated DEMATEL and fuzzy ANP techniques for evaluation and selection of outsourcing provider for a telecommunication company. Computers and Industrial Engineering. 2015;86:137-146
  132. 132. Făgărăşan M, Cristea C. Fascicle of Management and Technological Engineering logistics center location: Selection using multicriteria decision making. Ann. ORADEA Univ., no. May, 2015
  133. 133. Hwang B-N, Shen Y-C. Decision making for third party logistics supplier selection in semiconductor manufacturing industry: A nonadditive fuzzy integral approach. Mathematical Problems in Engineering. 2015;2015(Mcdm):1-12
  134. 134. Haji Vahabzadeh A, Asiaei A, Zailani S. Reprint of ‘green decision-making model in reverse logistics using FUZZY-VIKOR method. Resources, Conservation and Recycling. 2015;104:334-347
  135. 135. Akman GG, Baynal KJ. Logistics service provider selection through an integrated fuzzy multicriteria decision making approach. Journal of Industrial Engineering. 2014;2014
  136. 136. Tadić S, Zečević S, Krstić M. A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection. Expert Systems with Applications. 2014;41(18):8112-8128
  137. 137. Rapee P. The key factors for selecting C2C Logistics Companies by using\nFuzzy AHP in Thailand\n. In: IOSR Journal of Business and Management (IOSR-JBM). 2014;16(11):pp. 75-85
  138. 138. Tadic SR, Zecevic SM, Krstic MD. Ranking of logistics system scenarios for central Business District. Promet-Traffic & Transportation. 2014;26(2):159-167
  139. 139. Jayant A, Gupta P, Garg SK, Khan M. TOPSIS-AHP based approach for selection of reverse logistics service provider: A case study of mobile phone industry. Procedia Engineering. 2014;97:2147-2156
  140. 140. Bayazit O, Karpak B. Selection of a third party logistics service provider for an aerospace company: An analytical decision aiding approach. International Journal of Logistics Systems and Management. 2013;15(4):382-404
  141. 141. Ho W, He T, Lee CKM, Emrouznejad A. Strategic logistics outsourcing: An integrated QFD and fuzzy AHP approach. Expert Systems with Applications. 2012;39(12):10841-10850
  142. 142. Kabir G. Thırd party logıstıc servıce provıder selectıon usıng fuzzy AHP and TOPSIS method. International Journal of Quality Research. 2012;6(1):71-79
  143. 143. Liu G, Chen J, Zhong J. An integrated SVM and fuzzy AHP approach for selecting third party logistics providers. Przegląd Elektrotechniczny. 2012;(9):5-8
  144. 144. Erkayman B, Gundogar E, Yilmaz A. An integrated fuzzy approach for strategic alliance partner selection in third-party logistics. Scientific World Journal. 2012;2012:486306
  145. 145. Erkayman B, Gundogar E, Akkaya G, Ipek M. A fuzzy Topsis approach for logistics Center location selection. Journal of Business Case Studies. 2011;7(3):49-54
  146. 146. Gupta R, Sachdeva A, Bhardwaj A. Selection of 3pl service provider using integrated fuzzy delphi and fuzzy TOPSIS. In: Proceedings of the World Congress on Engineering and Computer Science. 2010. vol. 2. no. February 2016. pp. 20-22
  147. 147. Perçin S. Evaluation of third-party logistics (3PL) providers by using a two-phase AHP and TOPSIS methodology. Benchmarking An International Journal. 2009;16(5):588-604
  148. 148. Çakir E, Tozan H, Vayvay O. A method for selecting third party logistic service provider using fuzzy AHP. Journal of Naval Science Engineering. 2009;5(3):38-54
  149. 149. Soh S. A decision model for evaluating third-party logistics providers using fuzzy analytic hierarchy process. African Journal of Business Management. 2010;4(3):339-349
  150. 150. Çakir E. Logistics Outsourcing and Selection of Third Party Logistics Service Provider Logistics Outsourcing and Selection of Third Party Logistics Service Provider; 2009
  151. 151. Kannan G, Pokharel S, Kumar PS. A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, Conservation and Recycling. 2009;54(1):28-36
  152. 152. Jharkharia S, Shankar R. Selection of logistics service provider: An analytic network process (ANP) approach. Omega. 2007;35(3):274-289

Written By

Sharfuddin Ahmed Khan, Amin Chaabane and Fikri T. Dweiri

Submitted: October 15th, 2017 Reviewed: January 16th, 2018 Published: June 27th, 2018