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Cloud computing is the trendiest technology to perk up system performance with competent task scheduling algorithm. To execute various goals in task scheduling it is an vital way to organize customer desires with an order. Numerous papers attempted diverse areas like distribution of possessions, fortification, assortment and scheduling. Implementation time, carrying out cost, memory and possessions are main areas paying attention by obtainable researchers. In this effort for executing and transmission unusual task algorithm is anticipated. It minimizes the both completion time and implementation time with utilizing utmost resources. Performance of algorithm evaluated with obtainable examples with 40 number of tasks and processors with given process time and travel time.
Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
Varakumari Samudrala
Department of Electronics and Communication Engineering, NRI Institute of Technology (A), Agiripalli, Andhra Pradesh, India
Sri Harsha Arigela
Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
Venkata Kamesh Vinjamuri
Department of Mechanical Engineering, Aditya Engineering College (A), Surampalem, Andhra Pradesh, India
*Address all correspondence to: medikondu1979@gmail.com
1. Introduction
Available Resources and existing manufacturing system to enhance productivity of the system with great reach of customers demand [1]. In this connection FMS is a highly automated and classy system in the area of manufacturing to achieve high flexibility, mid variety and mid range of products [2]. FMS is showed benefits in cost reduction, improved utilizations, condensed work-in-process levels; etc. FMS has emerged as a feasible substitute to the manufacturing system. Design, planning, scheduling and control Karabtik et al. [3] problems are encountered in the life cycle of FMS. In the dynamic nature of FMS during operations of task scheduling and controlling problems are plays an important role such as tools, AGV routing, flexible parts [4] and AS/RS storage assignments [5]. Direction of jobs through the system [6] and sequencing of jobs on machines as well as scheduling of machines in FMS made through with considering other resources. For efficient production in flexible system AGVs and MHS (material Handling system) are plays a vital role employed Blazewicz et al. [7]. Effectiveness of AGVs depends on select and assigning the tasks to AGV, specific path selection and the process of determining arrival and departure times. With available resources to enhance productivity based on demand of the products it will reflects on pressure on the system to reduce pressure on the system there is a only one option is FMS [1]. In FMS scheduling of machines and AGVS simultaneously through priority rules like FCFS (First Come First Serve), SPT (Shortest Processing Time) and LPT(Longest Processing Time) are addressed by Nageswara rao et al. [8, 9, 10, 11, 12]. A new metraheuristic algorithm JAYA is proposed in this paper to implement scheduling of both machines and AGVs in a FMS environment which minimizes the operations completions time with saving of resources.
The configuration of the FMS in Figure 1 (layouts), Table 1 (travel time data) and Table 2 (job set data) are selected in the present study Bilge et al. [13].
Figure 1.
The layout configurations used in the example problem.
From
a
From
b
To
L/U
1
2
3
4
To
L/U
M1
M2
M3
M4
L/U
0
6
8
10
12
L/U
0
4
6
8
6
M1
12
0
6
8
10
1
6
0
2
4
2
M2
10
6
0
6
8
2
8
12
0
2
4
M3
8
8
6
0
6
3
6
10
12
0
2
M4
6
10
8
6
0
4
4
8
10
12
0
From
c
From
d
To
L/U
M1
M2
M3
M4
To
L/U
1
2
3
4
L/U
0
2
4
10
12
L/U
0
4
8
10
14
1
12
0
2
8
10
M1
18
0
4
6
10
2
10
12
0
6
8
M2
20
14
0
8
6
3
4
6
8
0
2
M3
12
8
6
0
6
4
2
4
6
12
0
M4
14
14
12
6
0
Table 1.
Example problems travel time data.
Job Set No
Job/Machine
1
2
3
4
Job Set No
Job/Machine
1
2
3
4
1
1
8
16
0
12
2
1
10
0
0
18
2
20
18
10
0
2
0
10
0
18
3
15
0
12
8
3
10
0
20
0
4
0
18
0
14
4
0
10
15
12
5
15
0
10
0
5
10
15
0
12
6
10
15
12
0
3
1
16
0
15
0
4
1
10
7
0
11
2
0
18
0
15
2
0
10
12
8
3
20
10
0
0
3
9
7
10 + 8
0
4
0
0
15
10
4
12
7 + 6
0
8
5
8
10
15
17
5
9
7 + 10
8
8
6
15
10
15
8
5
1
6
12
0
9
6
1
9
11
0
7
2
18
15
6
0
2
19
20
0
13
3
12
0
9
3
3
0
14
20
9
4
0
15
0
6
4
0
14
20
9
5
9
0
3
0
5
11
0
16
8
6
10
0
12
10
7
1
6
0
0
6
8
1
0
12
21
11
2
0
11
0
9
2
0
12
21
11
3
0
9
0
7
3
0
12
21
11
4
0
0
16
7
4
0
12
21
11
5
9
0
18
0
5
10
14
18
9
6
0
13
19
6
6
10
14
18
9
7
10
9
13
0
8
11
9
0
8
9
1
12
9
9
6
10
1
11
16
19
13
2
0
11
16
9
2
0
21
16
14
3
21
18
0
7
3
14
10
8
9
4
0
20
22
11
4
0
13
20
10
5
16
13
14
9
5
9
0
16
18
6
21
19
11
15
Table 2.
Data for the job sets used in example problems.
2.1 Task scheduling
Formulation of task scheduling problem with objective function of minimization of operational completion time along with vehicle heuristics algorithm shown in Figure 2. FMS consisting of a collection of processing workstations consistent by an MHS, ASRS and controlled by a scattered computer System Nageswararao et al. [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39] and it is highly automated machine cells along with group of workstations.
JAYA algorithm established @@venkatarao (2015) based on constrained and unconstrained optimization function.JAYA means “victory” origin of this term is Sanskrit. This algorithm is based on swarm intelligence and population based and it is enthused by “survival of the fittest”. Based on the concepts of JAYA it is attracted towards the best solutions globally with neglecting of worst solutions. In other words it gets closer to the optimum solutions and also escaping from worst solutions. It is very easy to execute with minimum parameters like population size and number of iterations these are some advantages over the other population based algorithms.
Procedural Steps of JAYA Algorithms
Minimization or maximization depends upon problem it is denoted by f(x)
If the new sequence is best one keep new one as best one or else keep old one as best one.
Step 6: This procedure applied to all 38 sequences around 30 runs to get best optimum solution by JAYA algorithm.
Step 7: Receptor editing.
Eliminate worst solutions and add random sequences either 10–50%.
Step 8: Termination criterion:
Repeating the procedure for number of generations or iterations.
Step 9: after fixed number of iterations selects the best sequence in this paper we are selecting 1000 iterations and these values are presented in Table 4.
Oper .No
M/C No
V.No
Vehi Prev Loc
Pre OMN
Vehicle RT
Prev OCT
Vehicle ET = VRT + TRT1 (4 to 5)
Max (7, 8)
Vehicle LT = VET+TRT2 (5 to 2)
Machine RT
Max (10, 11)
Process Time
Make Span
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
14
1
1
0
0
0
0
0
0
6
0
6
10
16
5
2
2
0
0
0
0
0
0
8
0
8
9
17
9
1
1
1
0
6
0
18
18
24
16
24
9
33
1
1
2
2
0
8
0
18
18
24
33
33
6
39
3
2
1
1
0
24
0
36
36
44
17
44
11
55
7
3
2
1
0
24
0
36
36
46
0
46
16
62
11
2
1
2
0
44
0
54
54
62
55
62
13
75
17
1
2
3
0
46
0
54
54
60
39
60
11
71
10
3
2
1
1
60
33
60
60
68
62
68
18
86
6
4
1
2
2
62
17
62
62
70
0
70
7
77
8
4
2
3
3
68
62
68
68
74
77
77
7
84
18
2
1
4
1
70
71
80
80
86
75
86
9
95
15
2
2
4
1
74
16
84
84
90
95
95
9
104
12
3
1
2
2
86
75
86
86
92
86
92
19
111
4
4
2
2
2
90
55
90
90
98
84
98
9
107
2
4
1
3
1
92
39
100
100
110
107
110
6
116
16
3
2
4
2
98
104
106
106
112
111
112
13
125
13
4
2
3
3
112
111
112
112
118
116
118
6
124
19
4
1
4
2
110
95
118
118
126
124
126
8
134
Table 4.
Operations schedule through JAYA (for Problem set 7 and layout 1).
From above table it is observed that for first two operations AGVs select randomly from third operations onwards based on vehicle algorithm select the AGV s depends on availability. Operation 14th on machine 1 along with vehicle “1” with travel time 6 minutes and process time 10 minutes in this connect operations completion time is 134 minutes.
In this chapter process times of machines and travel times are given as an input for task scheduling simultaneously. Procedure of JAYA algorithm implemented with “JAVA” language. The urbanized software gives the optimal sequence of AGVs, the sequence of jobs and the optimized makespan values for the 82 test problems. For generating 82 problems travel time and process time ratios are the main parameter. It influences the interaction of vehicle and machine schedules in FMS environment. From Table 5 it can be observed that, out of 40 problems, 40 problems give better results using JAYA when compared with all other algorithms (100%) and it can also be observed from Table 6 that out of 42 problems, 42 problems give better results using JAYA when compared with all other algorithms (100%).
Job. No
t/p
FCFS
SPT
LPT
JAYA
1.a
0.59
173
193
177
96
2.a
0.61
158
158
177
108
3.a
0.59
202
224
198
120
4.a
0.91
263
267
264
116
5.a
0.85
148
164
148
89
6.a
0.78
231
240
227
139
7.a
0.78
195
210
201
134
8.a
0.58
261
261
266
171
9.a
0.61
270
277
268
121
10.b
0.55
308
308
310
168
1.b
0.47
143
173
165
82
2.b
0.49
124
124
130
86
3.b
0.47
162
188
160
96
4.b
0.73
217
223
224
92
5.b
0.68
118
144
131
73
6.b
0.54
180
169
165
111
7.b
0.62
149
160
149
95
8.b
0.46
181
181
198
153
9.b
0.49
250
249
244
105
10.b
0.44
290
288
287
148
1.c
0.52
145
175
167
86
2.c
0.54
130
130
136
95
3.c
0.51
160
190
162
98
4.c
0.8
233
237
230
99
5.c
0.74
120
146
133
76
6.c
0.54
182
171
167
118
7.c
0.68
155
166
151
104
8.c
0.5
183
183
200
153
9.c
0.53
252
251
246
106
10.c
0.49
293
294
293
155
1.d
0.74
189
207
189
106
2.d
0.77
174
174
174
122
3.d
0.74
220
250
212
128
4.d
1.14
301
301
298
131
5.d
1.06
171
189
171
97
6.d
0.78
249
252
237
147
7.d
0.97
217
242
151
154
8.d
0.72
285
285
200
182
9.d
0.76
292
311
290
126
10.d
0.69
350
350
345
184
Table 5.
For t/p > 0.25 operation completion time values.
Job. No
t/p
FCFS
SPT
LPT
JAYA
1.a0
0.15
207
248
252
126
2.a0
0.15
217
217
225
148
3.a0
0.15
257
327
282
158
4.a0
0.15
303
328
317
123
5.a0
0.21
152
190
187
102
6.a0
0.16
304
281
297
200
7.a0
0.19
231
240
264
137
8.a0
0.14
338
338
347
292
9.a0
0.15
390
367
359
182
10.a0
0.14
452
429
444
262
1.b0
0.12
194
238
246
123
2.b0
0.12
194
194
206
143
3.b0
0.12
241
311
270
154
4.b0
0.12
285
312
298
116
5.b0
0.17
142
180
184
100
6.b0
0.12
292
260
284
187
7.b0
0.15
212
218
249
136
8.b0
0.11
306
319
334
287
9.b0
0.12
380
355
347
179
10.b0
0.11
445
423
439
255
1.c0
0.13
195
239
247
122
2.c0
0.13
197
197
209
146
3.c0
0.13
240
312
271
146
4.c0
0.13
292
317
301
117
5.c0
0.18
141
181
183
99
6.c0
0.24
296
261
285
199
7.c0
0.17
215
221
250
137
8.c0
0.13
307
320
335
288
9.c0
0.13
381
356
348
180
10.c0
0.12
448
426
442
260
1.d0
0.18
213
255
254
124
2.d1
0.13
307
307
319
217
3.d0
0.18
261
330
282
151
3.d1
0.12
370
476
411
235
4.d1
0.19
434
471
451
177
5.d1
0.18
218
269
270
148
6.d0
0.19
310
288
299
201
7.d0
0.24
239
251
270
141
7.d1
0.16
329
344
385
203
8.d0
0.18
343
343
349
293
9.d0
0.19
396
379
370
182
10.d0
0.17
466
445
455
265
Table 6.
For t/p < 0.25 Operations completion time values.
Table 5 consists of problems whose t/p ratios are higher than 0.25, and those with lower t/p ratios are represented in Table 6. The developed ‘JAVA’ code is used to designate the example problems which are given in the first column. The digits that follow 1.a indicate the job set and the layout. In Table 6 another digit is appended to the code. Here, having a 0 or 1 as the last digit implies that the process times are doubled or tripled, respectively, where in both cases travel times are halved.
Simulations have been performed after implementation of a new metaheuristic JAYA algorithm with Population size is taken as double the job numbers it means it is not a fixed method population size may vary from problem to problem depends upon user. The results are obtained after repeating the simulation process for 30 runs with 1000 generations.
JAYA algorithm is very effective in generating optimal solutions for FMS it is observed from Tables 5 and 6. The iterative algorithm promises development in task scheduling particularly in environments where cycle times are short and travel times are equivalent, or where the layout and the process routes do not suit each other. In this work the FMS scheduling optimization is done by using the JAYA algorithm and the optimal sequences of machines and AGVs are determined. The iterative algorithm created anticipates the complete set of flow requirements for a given machine schedule and makes vehicle assignments accordingly, as opposed to a real-time dispatching scheme that uses no information other than the move request queue. The purpose of this work is to make AGV scheduling an integral part of the scheduling activity, actively participating in the specification of the off-line schedule. It is concluded that the JAYA is the suitable tool for this kind of scheduling problems among the non-traditional techniques considered in this work. Since, the iterative algorithm promises improvement in scheduling of FMS components, this approach can be extended for various multi-objectives scheduling of FMS configurations. Subsystems like automated storage and retrieval systems (AS/RS) and robots can also be included with machine and AGVs scheduling of FMS in future work.
The authors greatly acknowledge the financial support from DST-SERB, Govt. of India (Sanction No: SB/EMEQ-501/2014) for carrying out this R & D activity.
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Written By
Nageswara Rao Medikondu, Varakumari Samudrala, Sri Harsha Arigela and Venkata Kamesh Vinjamuri
Submitted: 05 September 2022Reviewed: 14 September 2022Published: 08 December 2022
Open access peer-reviewed chapter - ONLINE FIRST
