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An NSGA-II-based multiobjective approach for real-time routing selection in a flexible manufacturing system under uncertainty and reliability constraints

  • Mehdi SouierEmail author
  • Mohammed Dahane
  • Fouad Maliki
ORIGINAL ARTICLE
  • 38 Downloads

Abstract

Routing flexibility is one of the most common types of flexibilities of manufacturing systems. It allows the system to continue producing given part types despite uncertainties. Its main purpose is to maintain a high level of performance so that the system can deal with disturbances (failures, maintenance actions,…). This type of flexibility occurs when there are alternative or redundant machine tools in the system. However, due to resource and alternative routing limitations, the scheduling problems in such systems can become very complex. Furthermore, though routing flexibility aims to enhance system responsiveness, it still depends on the reliability and availability of machines and individual components in a system. The present paper aims to investigate the scheduling problem in a flexible manufacturing system (FMS) with routing flexibility under uncertainties related to the random arrival of parts orders and machines failures, by considering reliability and maintenance constraints. The real-time decisions for part routing selection are made using a non-dominated sorting genetic algorithm (NSGA-II), by considering the workload, utilization level, and reliability of machines in a workstation, in order to minimize the deadlocks and maximize the overall system reliability. The simulation results obtained showed that, for an overloaded system, the proposed NSGA-II algorithm induces the best performance in terms of total profit, system productivity, and machines utilization.

Keywords

Flexible manufacturing system Routing flexibility Non-dominated sorting genetic algorithm Reliability Maintenance 

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References

  1. 1.
    Albey E, Bilge Ü (2011) A hierarchical approach to FMS planning and control with simulation-based capacity anticipation. Int J Prod Res 49(11):3319–3342Google Scholar
  2. 2.
    Altumi A, Philipose A, Taboun S (2000) Reliability optimisation of fms with spare tooling. Int J Adv Manuf Technol 16(8):551–558Google Scholar
  3. 3.
    Barlow RE, Proschan F (1996) Mathematical theory of reliability. SIAM, PhiladelphiazbMATHGoogle Scholar
  4. 4.
    Bilge Ü, Fırat M, Albey E (2008) A parametric fuzzy logic approach to dynamic part routing under full routing flexibility. Comput Ind Eng 55(1):15–33Google Scholar
  5. 5.
    Caprihan R, Kumar A, Stecke KE (2006) A fuzzy dispatching strategy for due-date scheduling of FMSs with information delays. Int J Flex Manuf Syst 18(1):29–53zbMATHGoogle Scholar
  6. 6.
    Chan F, Chan H, Lau H (2002) The state of the art in simulation study on FMS scheduling: a comprehensive survey. Int J Adv Manuf Technol 19(11):830–849Google Scholar
  7. 7.
    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):523–543Google Scholar
  8. 8.
    Chang CC et al (2010) Development of a web-based decision support system for cell formation problems considering alternative process routings and machine sequences. J Softw Eng Appl 3(02):160–166Google Scholar
  9. 9.
    Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182–197Google Scholar
  10. 10.
    Defersha FM, Chen M (2012) Jobshop lot streaming with routing flexibility, sequence-dependent setups, machine release dates and lag time. Int J Prod Res 50(8):2331–2352Google Scholar
  11. 11.
    Delgoshaei A, Ali A, Ariffin MKA, Gomes C (2016) A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty. Comput Ind Eng 100:110–132Google Scholar
  12. 12.
    Dosdog̈ru A T, Göçken M, Geyik F (2015) Integration of genetic algorithm and Monte Carlo to analyze the effect of routing flexibility. Int J Adv Manuf Technol 81(5):1379–1389Google Scholar
  13. 13.
    Ebrahimi SB (2018) A bi-objective model for a multi-echelon supply chain design considering efficiency and customer satisfaction: a case study in plastic parts industry. Int J Adv Manuf Technol 95(9–12):3631–3649Google Scholar
  14. 14.
    Erozan I, Torkul O, Ustun O (2015) Proposal for a decision support software for the design of cellular manufacturing systems with multiple routes. Int J Adv Manuf Technol 76(9):2027–2041Google Scholar
  15. 15.
    Gaula AK, Sharma RK (2015) Analyzing the effect of maintenance strategies on throughput of a typical fmc (3-m, 1-r). Int J Syst Assur Eng Manag 6(2):183–190Google Scholar
  16. 16.
    Gen M, Lin L (2014) Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey. J Intell Manuf 25(5):849–866MathSciNetGoogle Scholar
  17. 17.
    Guo Z, Wong WK, Leung S, Fan J (2009) Intelligent production control decision support system for flexible assembly lines. Exp Syst Appl 36(3):4268–4277Google Scholar
  18. 18.
    Han L, Xing K, Chen X, Xiong F (2018) A Petri net-based particle swarm optimization approach for scheduling deadlock-prone flexible manufacturing systems. J Intell Manuf 29(5):1083–1096Google Scholar
  19. 19.
    Huang B, Shi XX, Xu N (2012) Scheduling FMS with alternative routings using Petri nets and near admissible heuristic search. Int J Adv Manuf Technol 63(9–12):1131–1136Google Scholar
  20. 20.
    Hwang C, Yoon K (1981) Multiple attribute decision making: methods and applications. Springer, New YorkzbMATHGoogle Scholar
  21. 21.
    Jabal-Ameli M, Moshref-Javadi M (2014) Concurrent cell formation and layout design using scatter search. Int J Adv Manuf Technol 71:1–22Google Scholar
  22. 22.
    Koşucuoğlu D, Bilge Ü (2012) Material handling considerations in the FMS loading problem with full routing flexibility. Int J Prod Res 50(22):6530–6552Google Scholar
  23. 23.
    Kumar MS, Janardhana R, Rao C (2011) Simultaneous scheduling of machines and vehicles in an FMS environment with alternative routing. Int J Adv Manuf Technol 53(1–4):339–351Google Scholar
  24. 24.
    Leung C, Wong T, Mak KL, Fung RY (2010) Integrated process planning and scheduling by an agent-based ant colony optimization. Comput Ind Eng 59(1):166–180Google Scholar
  25. 25.
    Liu J, MacCarthy B (1996) The classification of FMS scheduling problems. Int J Prod Res 34(3):647–656zbMATHGoogle Scholar
  26. 26.
    Luo G, Wen X, Li H, Ming W, Xie G (2017) An effective multi-objective genetic algorithm based on immune principle and external archive for multi-objective integrated process planning and scheduling. Int J Adv Manuf Technol 91(9–12):3145–3158Google Scholar
  27. 27.
    Maheshwari S, Sharma P, Jain M (2010) Unreliable flexible manufacturing cell with common cause failure. Int J Eng Sci Technol 2(9):4701–4716Google Scholar
  28. 28.
    Nejad MG, Shavarani SM, Vizvári B, Barenji RV (2018) Trade-off between process scheduling and production cost in cyclic flexible robotic cells. Int J Adv Manuf Technol 96(1–4):1081–1091Google Scholar
  29. 29.
    Nguyen HP, Pham VD, Ngo N-V (2018) Application of TOPSIS to Taguchi method for multi-characteristic optimization of electrical discharge machining with titanium powder mixed into dielectric fluid. Int J Adv Manuf Technol 98(5):1179–1198.  https://doi.org/10.1007/s00170-018-2321-2 Google Scholar
  30. 30.
    Nonaka Y, Erdős G, Kis T, Nakano T, Váncza J (2012) Scheduling with alternative routings in CNC workshops. CIRP Ann Manuf Technol 61(1):449–454Google Scholar
  31. 31.
    Nsakanda AL, Diaby M, Price WL (2010) A price-directed decomposition approach for solving large-scale capacitated part-routing problems. Int J Prod Res 48(14):4273–4295zbMATHGoogle Scholar
  32. 32.
    Ozmutlu S, Harmonosky CM (2005) A real-time methodology for minimizing mean flowtime in FMSs with routing flexibility: threshold-based alternate routing. Eur J Oper Res 166(2):369–384MathSciNetzbMATHGoogle Scholar
  33. 33.
    Pajoutan M, Golmohammadi A, Seifbarghy M (2014) CMS scheduling problem considering material handling and routing flexibility. Int J Adv Manuf Technol 72(5–8):881–893Google Scholar
  34. 34.
    Philip A, Sharma RK (2013) A stochastic reward net approach for reliability analysis of a flexible manufacturing module. Int J Syst Assur Eng Manag 4(3):293–302Google Scholar
  35. 35.
    Pitts R, Ventura J (2009) Scheduling flexible manufacturing cells using tabu search. Int J Prod Res 47(24):6907–6928zbMATHGoogle Scholar
  36. 36.
    Qu S, Zhao J, Wang T (2017) Experimental study and machining parameter optimization in milling thin-walled plates based on NSGA-II. Int J Adv Manuf Technol 89(5–8):2399–2409Google Scholar
  37. 37.
    Raj T, Shankar R, Suhaib M (2007) A review of some issues and identification of some barriers in the implementation of FMS. Int J Flex Manuf Syst 19(1):1–40zbMATHGoogle Scholar
  38. 38.
    Rifai AP, Dawal SZM, Zuhdi A, Aoyama H, Case K (2016) Reentrant FMS scheduling in loop layout with consideration of multi loading-unloading stations and shortcuts. Int J Adv Manuf Technol 82(9–12):1527–1545Google Scholar
  39. 39.
    Rifai AP, Nguyen HT, Aoyama H, Dawal SZM, Masruroh NA (2018) Non-dominated sorting biogeography-based optimization for bi-objective reentrant flexible manufacturing system scheduling. Appl Soft Comput 62:187–202Google Scholar
  40. 40.
    Rossi A, Dini G (2007) Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method. Robot Comput Integr Manuf 23(5):503–516Google Scholar
  41. 41.
    Savsar M (2005) Performance analysis of an FMS operating under different failure rates and maintenance policies. Int J Flex Manuf Syst 16(3):229–249MathSciNetzbMATHGoogle Scholar
  42. 42.
    Savsar M (2006) Effects of maintenance policies on the productivity of flexible manufacturing cells. Omega 34(3):274–282Google Scholar
  43. 43.
    Shewchuk JP (1999) A set of generic flexibility measures for manufacturing applications. Int J Prod Res 37(13):3017–3042zbMATHGoogle Scholar
  44. 44.
    Sormaz D, Patel C (2016) Development and evaluation of feature-focused dynamic routing policy. Int J Adv Manuf Technol 99(1): 15–28.  https://doi.org/10.1007/s00170-016-8984-7 Google Scholar
  45. 45.
    Souier M, Sari Z (2014) Impacts of scheduling decisions based on PSO algorithm and dispatching rules on FMS performances. Int J Appl Metaheuristic Comput (IJAMC) 5(2):22–38Google Scholar
  46. 46.
    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):145–164Google Scholar
  47. 47.
    Sowmiya N, Gupta NS, Valarmathi B, Ponnambalam S (2017) CORA-a heuristic approach to machine-part cell formation in the presence of alternative process plans. Int J Adv Manuf Technol 91(9–12):4275–4297Google Scholar
  48. 48.
    Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248Google Scholar
  49. 49.
    Sun Y (1994) Simulation for maintenance of an FMS: an integrated system of maintenance and decision-making. Int J Adv Manuf Technol 9(1):35–39Google Scholar
  50. 50.
    Taha Z, Rostam S (2012) A hybrid fuzzy AHP-PROMETHEE decision support system for machine tool selection in flexible manufacturing cell. J Intell Manuf 23(6):2137–2149Google Scholar
  51. 51.
    Tavakkoli-Moghaddam R, Ranjbar-Bourani M, Amin GR, Siadat A (2012) A cell formation problem considering machine utilization and alternative process routes by scatter search. J Intell Manuf 23(4):1127–1139Google Scholar
  52. 52.
    Tian M, Gong X, Yin L, Li H, Ming W, Zhang Z, Chen J (2017) Multi-objective optimization of injection molding process parameters in two stages for multiple quality characteristics and energy efficiency using Taguchi method and NSGA-II. Int J Adv Manuf Technol 89(1–4):241–254Google Scholar
  53. 53.
    Venkatadri U, Elaskari SM, Kurdi R (2017) A multi-commodity network flow-based formulation for the multi-period cell formation problem. Int J Adv Manuf Technol 91(1–4):175–187Google Scholar
  54. 54.
    Vineyard M, Amoako-Gyampah K, Meredith JR (2000) An evaluation of maintenance policies for flexible manufacturing systems: a case study. Int J Oper Prod Manag 20(4):409–426zbMATHGoogle Scholar
  55. 55.
    Wang SJ, Xi Lf, Zhou BH (2008) FBS-enhanced agent-based dynamic scheduling in FMS. Eng Appl Artif Intell 21(4):644–657Google Scholar
  56. 56.
    Wang YC, Chen T, Chiang H, Pan HC (2016) A simulation analysis of part launching and order collection decisions for a flexible manufacturing system. Simul Modell Pract Theory 69:80–91Google Scholar
  57. 57.
    Wong T, Leung C, Mak KL, Fung RY (2006) Dynamic shopfloor scheduling in multi-agent manufacturing systems. Exp Syst Appl 31(3):486–494Google Scholar
  58. 58.
    Wu L, Suzuki S (2015) Cell formation design with improved similarity coefficient method and decomposed mathematical model. Int J Adv Manuf Technol 79(5–8):1335–1352Google Scholar
  59. 59.
    Xu X, Zhang W, Ding X (2018) Modular design method for filament winding process equipment based on GGA and NSGA-IIi. Int J Adv Manuf Technol 94(5–8):2057–2076Google Scholar
  60. 60.
    Yu X, Ram B (2006) Bio-inspired scheduling for dynamic job shops with flexible routing and sequence-dependent setups. Int J Prod Res 44(22):4793–4813zbMATHGoogle Scholar
  61. 61.
    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):229–248Google Scholar
  62. 62.
    Zhang W, Gen M, Jo J (2014) Hybrid sampling strategy-based multiobjective evolutionary algorithm for process planning and scheduling problem. J Intell Manuf 25(5):881–897Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Manufacturing Engineering Laboratory of Tlemcen (MELT)University of TlemcenTlemcenAlgeria
  2. 2.High School of Management of TlemcenTlemcenAlgeria
  3. 3.Université de Lorraine, LGIPMMetzFrance
  4. 4.High School of Applied Sciences of TlemcenTlemcenAlgeria

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