Reentrant FMS scheduling in loop layout with consideration of multi loading-unloading stations and shortcuts

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• 作者：Achmad P. Rifai ; Siti Zawiah Md Dawal…
• 关键词：Reentrant FMS scheduling ; Multi loading ; unloading and shortcuts ; Genetic algorithm ; Crowding distance ; based substitution
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：82
• 期：9-12
• 页码：1527-1545
• 全文大小：1,926 KB
• 参考文献：1.Chan FT, Swarnkar R (2006) Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS. Robot Comput Integr Manuf 22(4):353–362CrossRef
  2.Kumar RS, Asokan P, Kumanan S (2009) Artificial immune system-based algorithm for the unidirectional loop layout problem in a flexible manufacturing system. Int J Adv Manuf Technol 40(5–6):553–565CrossRef
  3.Ozcelik F, Islier AA (2011) Generalisation of unidirectional loop layout problem and solution by a genetic algorithm. Int J Prod Res 49(3):747–764CrossRef
  4.Hall NG, Sriskandarajah C, Ganesharajah T (2001) Operational decisions in AGV-served flowshop loops: scheduling. Ann Oper Res 107(1–4):161–188MATH MathSciNet CrossRef
  5.Gamila MA, Motavalli S (2003) A modeling technique for loading and scheduling problems in FMS. Robot Comput-Integr Manuf 19(1):45–54CrossRef
  6.Zeballos L, Quiroga O, Henning GP (2010) A constraint programming model for the scheduling of flexible manufacturing systems with machine and tool limitations. Eng Appl Artif Intell 23(2):229–248CrossRef
  7.MacCarthy B, Liu J (1993) A new classification scheme for flexible manufacturing systems. Int J Prod Res 31(2):299–309CrossRef
  8.Haq AN, Karthikeyan T, Dinesh M (2003) Scheduling decisions in FMS using a heuristic approach. Int J Adv Manuf Technol 22(5–6):374–379CrossRef
  9.Keung K, Ip W, Yuen D (2003) An intelligent hierarchical workstation control model for FMS. J Mater Process Technol 139(1):134–139CrossRef
  10.Das SR, Canel C (2005) An algorithm for scheduling batches of parts in a multi-cell flexible manufacturing system. Int J Prod Econ 97(3):247–262CrossRef
  11.Sankar SS, Ponnambalam S, Gurumarimuthu M (2006) Scheduling flexible manufacturing systems using parallelization of multi-objective evolutionary algorithms. Int J Adv Manuf Technol 30(3–4):279–285CrossRef
  12.Jerald J, Asokan P, Saravanan R, Rani ADC (2006) Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm. Int J Adv Manuf Technol 29(5–6):584–589CrossRef
  13.Huang R-H, Yu S-C, Kuo C-W (2014) Reentrant two-stage multiprocessor flow shop scheduling with due windows. Int J Adv Manuf Technol 71(5–8):1263–1276CrossRef
  14.Jeong B, Kim Y-D (2014) Minimizing total tardiness in a two-machine re-entrant flowshop with sequence-dependent setup times. Comput Oper Res 47:72–80MathSciNet CrossRef
  15.Burnwal S, Deb S (2013) Scheduling optimization of flexible manufacturing system using cuckoo search-based approach. Int J Adv Manuf Technol 64(5–8):951–959CrossRef
  16.Souier M, Sari Z, Hassam A (2013) Real-time rescheduling metaheuristic algorithms applied to FMS with routing flexibility. Int J Adv Manuf Technol 64(1–4):145–164CrossRef
  17.Joseph O, Sridharan R (2011) Analysis of dynamic due-date assignment models in a flexible manufacturing system. J Manuf Syst 30(1):28–40CrossRef
  18.Cardin O, Mebarki N, Pinot G (2013) A study of the robustness of the group scheduling method using an emulation of a complex FMS. Int J Prod Econ 146(1):199–207CrossRef
  19.Udhayakumar P, Kumanan S (2012) Integrated scheduling of flexible manufacturing system using evolutionary algorithms. Int J Adv Manuf Technol 61(5–8):621–635CrossRef
  20.Hsu T, Korbaa O, Dupas R, Goncalves G (2008) Cyclic scheduling for FMS: modelling and evolutionary solving approach. Eur J Oper Res 191(2):464–484MATH CrossRef
  21.Qin W, Zhang J, Sun Y (2013) Multiple-objective scheduling for interbay AMHS by using genetic-programming-based composite dispatching rules generator. Comput Ind 64(6):694–707CrossRef
  22.Caridá V, Morandin Jr O, Tuma C (2015) Approaches of fuzzy systems applied to an AGV dispatching system in a FMS. Int J Adv Manuf Technol:1–11
  23.Chan FT, Chung S, Chan P An introduction of dominant genes in genetic algorithm for scheduling of FMS. In: Intelligent Control, 2005. Proceedings of the 2005 I.E. International Symposium on, Mediterrean Conference on Control and Automation, 2005. IEEE, pp 1429–1434
  24.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):523–543CrossRef
  25.Chan FT, Chung S, Chan L, Finke G, Tiwari M (2006) Solving distributed FMS scheduling problems subject to maintenance: genetic algorithms approach. Robot Comput Integr Manuf 22(5):493–504CrossRef
  26.Mejía G, Odrey NG (2006) An approach using Petri Nets and improved heuristic search for manufacturing system scheduling. J Manuf Syst 24(2):79–92CrossRef
  27.Lee J, Lee JS (2010) Heuristic search for scheduling flexible manufacturing systems using lower bound reachability matrix. Comput Ind Eng 59(4):799–806CrossRef
  28.Hsu T, Dupas R, Goncalves G A genetic algorithm to solving the problem of flexible manufacturing system cyclic scheduling. In: Systems, Man and Cybernetics, 2002 I.E. International Conference on, 2002. IEEE, p 6 pp. vol. 3
  29.Sun Y, Zhang C, Gao L, Wang X (2011) Multi-objective optimization algorithms for flow shop scheduling problem: a review and prospects. Int J Adv Manuf Technol 55(5–8):723–739CrossRef
  30.Prakash A, Chan FT, Deshmukh S (2011) FMS scheduling with knowledge based genetic algorithm approach. Expert Syst Appl 38(4):3161–3171CrossRef
  31.Ebrahimi M, Fatemi Ghomi S, Karimi B (2014) Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates. Appl Math Model 38(9):2490–2504MathSciNet CrossRef
  32.Chan FT, Chan HK (2004) A comprehensive survey and future trend of simulation study on FMS scheduling. J Intell Manuf 15(1):87–102CrossRef
  33.Low C, Yip Y, Wu T-H (2006) Modelling and heuristics of FMS scheduling with multiple objectives. Comput Oper Res 33(3):674–694MATH CrossRef
  34.Low C, Wu T-H (2001) Mathematical modelling and heuristic approaches to operation scheduling problems in an FMS environment. Int J Prod Res 39(4):689–708MATH CrossRef
  35.Baruwa OT, Piera MA (2014) Anytime heuristic search for scheduling flexible manufacturing systems: a timed colored Petri net approach. Int J Adv Manuf Technol 75(1–4):123–137CrossRef
  36.Zhou R, Nee A, Lee H (2009) Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems. Int J Prod Res 47(11):2903–2920MATH CrossRef
  37.Renna P (2010) Job shop scheduling by pheromone approach in a dynamic environment. Int J Comput Integr Manuf 23(5):412–424CrossRef
  38.Zhao F, Jiang X, Zhang C, Wang J (2014) A chemotaxis-enhanced bacterial foraging algorithm and its application in job shop scheduling problem. Int J Comput Integr Manuf (ahead-of-print):1–16
  39.Kim K, Yamazaki G, Lin L, Gen M (2004) Network-based hybrid genetic algorithm for scheduling in FMS environments. Artif Life Intell 8(1):67–76
  40.Godinho Filho M, Barco CF, Neto RFT (2012) Using Genetic Algorithms to solve scheduling problems on flexible manufacturing systems (FMS): a literature survey, classification and analysis. Flex Serv Manuf J:1–24
  41.Reeves CR, Rowe JE (2003) Genetic algorithms: principles and perspectives: a guide to GA theory, vol 20. Springer, Dordrecht
  42.Chen J-S, Pan JC-H, Lin C-M (2008) A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem. Expert Syst Appl 34(1):570–577CrossRef
  43.Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRef
  44.Barnes J, Laguna M (1991) A review and synthesis of tabu search applications to production scheduling problems. ORP91-05
  45.Brandimarte P (1993) Routing and scheduling in a flexible job shop by tabu search. Ann Oper Res 41(3):157–183MATH CrossRef
  46.Ovacik IM, Uzsoy R (1997) Decomposition methods for complex factory scheduling problems. Kluwer Academic Publishers, BostonCrossRef
  
• 作者单位：Achmad P. Rifai (1)
  Siti Zawiah Md Dawal (1)
  Aliq Zuhdi (1)
  Hideki Aoyama (2)
  K. Case (3)
  
  1. Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
  2. School of Integrated Design Engineering, Keio University, Tokyo, Japan
  3. Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, Leicestershire, UK
  
• 刊物类别：Engineering
• 刊物主题：Industrial and Production Engineering
  Production and Logistics
  Mechanical Engineering
  Computer-Aided Engineering and Design
  
• 出版者：Springer London
• ISSN：1433-3015

文摘

The scheduling problem in flexible manufacturing systems (FMS) environment with loop layout configuration has been shown to be a NP-hard problem. Moreover, the improvement and modification of the loop layout add to the difficulties in the production planning stage. The introduction of multi loading-unloading points and turntable shortcut resulted on more possible routes, thus increasing the complexity. This research addressed the reentrant FMS scheduling problem where jobs are allowed to reenter the system and revisit particular machines. The problem is to determine the optimal sequence of the jobs as well as the routing options. A modified genetic algorithm (GA) was proposed to generate the feasible solutions. The crowding distance-based substitution was incorporated to maintain the diversity of the population. A set of test was applied to compare the performance of the proposed approach with other methods. Further computational experiments were conducted to assess the significance of multi loading-unloading and shortcuts in reducing the makespan, mean flow time, and tardiness. The results highlighted that the proposed model was robust and effective in the scheduling problem for both small and large size problems. Keyword Reentrant FMS scheduling Multi loading-unloading and shortcuts Genetic algorithm Crowding distance-based substitution