Role of batch size in scheduling optimization of flexible manufacturing system using genetic algorithm

Journal of Industrial Engineering International

دوره 16، شماره 1، خرداد 2020، صفحه 135-146   

نویسندگان

Muhammad Umair Akhtar؛ Muhammad Huzaifa Raza؛ Muhammad Shafiq

Department of Industrial Engineering, University of Engineering and Technology, Taxila, Pakistan

چکیده

Flexible manufacturing system (FMS) readily addresses the dynamic needs of the customers in terms of variety and quality. At present, there is a need to produce a wide range of quality products in limited time span. On-time delivery of customers’ orders is critical in make-to-order (MTO) manufacturing systems. The completion time of the orders depends on several factors including arrival rate, variability, and batch size, to name a few. Among those, batch size is a significant construct for effective scheduling of an FMS, as it directly affects completion time. On the other hand, constant batch size makes MTO less responsive to customers’ demands. In this paper, an FMS scheduling problem with n jobs and m machines is studied to minimize lateness in meeting due dates, with focus on the impact of batch size. The effect of batch size on completion time of the orders is investigated under following strategies: (1) constant batch size, (2) minimum part set, and (3) optimal batch size. A mathematical model is developed to optimize batch size considering completion time, lateness penalties and setup times. Scheduling of an FMS is not only a combinatorial optimization problem but also NP-hard problem. Suitable solutions of such problems through exact methods are difficult. Hence, a meta-heuristic Genetic algorithm is used to optimize scheduling of the FMS.

کلیدواژه‌ها

Flexible manufacturing system (FMS) . Scheduling optimization . Batch size؛ due dates . Completion time . Genetic algorithm (GA)

مراجع

Atmani A, Lashkari R (1998) A model of machine-tool selection and operation allocation in FMS. Int J Prod Res 36(5):1339–1349

Baker KR (1974) Introduction to sequencing and scheduling. Wiley, Hoboken
Beemsterboer B, Land M, Teunter R (2017) Flexible lot sizing in hybrid make-to-order/make-to-stock production planning. Eur J Oper Res 260(3):1014–1023
Browne J, Dubois D, Rathmill K, Sethi SP, Stecke KE (1984) Classification of flexible manufacturing systems. FMS Mag
2(2):114–117
Burnwal S, Deb S (2013) Scheduling optimization of flexible manufacturing system using cuckoo search-based approach. Int J Adv Manuf Technol 64(5–8):951–959
Candan G, Yazgan HR (2015) Genetic algorithm parameter optimisation using Taguchi method for a flexible manufacturing system scheduling problem. Int J Prod Res 53(3):897–915
Chan FT, Chung S, Chan P (2005) An introduction of dominant genes in genetic algorithm for scheduling of FMS. In: Intelligent control, 2005. Proceedings of the 2005 IEEE international symposium on, mediterrean conference on control and automation, IEEE
Chan F, Chung S, Chan P (2006) Application of genetic algorithms with dominant genes in a distributed scheduling problem in flexible manufacturing systems. Int J Prod Res 44(3):523–543
Chaudhry I, Mahmood S, Shami M (2011) Simultaneous scheduling of machines and automated guided vehicles in flexible manufacturing systems using genetic algorithms. J Cent South Univ Technol 18(5):1473–1486

Cheng B, Yang S, Hu X, Chen B (2012) Minimizing makespan and total completion time for parallel batch processing machines with non-identical job sizes. Appl Math Model 36(7):3161–3167
Geng Z, Yuan J (2018) Scheduling family jobs on an unbounded parallel-batch machine to minimize makespan and maximum flow time. J Ind Manag Optim 14:985–1032
Hsu C-M, Chen K-Y, Chen M-C (2005) Batching orders in warehouses by minimizing travel distance with genetic algorithms. Comput Ind 56(2):169–178 
Jain A, Elmaraghy H (1997) Production scheduling/rescheduling in flexible manufacturing. Int J Prod Res 35(1):281–309
Jerald J, Asokan P, Prabaharan G, Saravanan R (2005) Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm. Int J Adv Manuf Technol 25(9):964–971
Keung K, Ip W, Yuen D (2003) An intelligent hierarchical workstation control model for FMS. J Mater Process Technol 139(1):134–139
Kim YK, Kim JY, Shin KS (2007) An asymmetric multileveled symbiotic evolutionary algorithm for integrated FMS scheduling. J Intell Manuf 18(6):631–645
Liu J, MacCarthy B (1996) The classification of FMS scheduling problems. Int J Prod Res 34(3):647–656
Lova A, Tormos P, Cervantes M, Barber F (2009) An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes. Int J Prod Econ 117(2):302–316
Matin HNZ, Salmasi N, Shahvari O (2017) Makespan minimization in flowshop batch processing problem with different batch compositions on machines. Int J Prod Econ 193:832–844
Nidhiry NM, Saravanan R (2014) Scheduling optimization of FMS using NSGA-II. Adv Ind Eng Manag 3(1):63–72
Raj JA, Ravindran D, Saravanan M, Prabaharan T (2014) Simultaneous scheduling of machines and tools in multimachine flexible manufacturing systems using artificial immune system algorithm. Int J Comput Integr Manuf 27(5):401–414
Reddy BS, Rao CS (2006) A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS. Int J
Adv Manuf Technol 31(5):602–613
Rifai AP, Nguyen H-T, Aoyama H, Dawal SZM, Masruroh NA (2018) Non-dominated sorting biogeography-based optimization for bi-objective reentrant flexible manufacturing system scheduling. Appl Soft Comput 62:187–202
Sadeghian R, Sadeghian MR (2016) A decision support system based on artificial neural network and fuzzy analytic network process for selection of machine tools in a flexible manufacturing system. Int J Adv Manuf Technol 82(9–12):1795–1803
Sankar SS, Ponnanbalam S, Rajendran C (2003) A multiobjective genetic algorithm for scheduling a flexible manufacturing
system. Int J Adv Manuf Technol 22(3–4):229–236
Saravanan M, Haq AN (2008) Evaluation of scatter-search approach for scheduling optimization of flexible manufacturing systems. Int J Adv Manuf Technol 38(9–10):978–986
Sivasankaran P, Shahabudeen P (2014) Literature review of assembly line balancing problems. Int J Adv Manuf Technol
73(9–12):1665–1694
Stecke KE (1985) Design, planning, scheduling, and control problems of flexible manufacturing systems. Ann Oper Res 3(1):1–12
Udhayakumar P, Kumanan S (2010) Task scheduling of AGV in FMS using non-traditional optimization techniques. Int J Simul Model 9(1):28–39 
Umar UA, Ariffin M, Ismail N, Tang S (2015) Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment. Int J Adv Manuf Technol 81(9–12):2123–2141
Vidyarthi N, Tiwari M (2001) Machine loading problem of FMS: a fuzzy-based heuristic approach. Int J Prod Res 39(5):953–979
Ying K-C, Lin S-W (2018) Minimizing makespan for the distributed hybrid flowshop scheduling problem with multiprocessor tasks. Expert Syst Appl 92:132–141
Zandieh M, Mozaffari E, Gholami M (2010) A robust genetic algorithm for scheduling realistic hybrid flexible flow line
problems. J Intell Manuf 21(6):731–743
Zhang Q, Manier H, Manier M-A (2012) A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times. Comput Oper Res 39(7):1713–1723