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Research on group search optimizers for a reconfigurable flow shop sequencing problem

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Abstract

A reconfigurable manufacturing system is usually designed for quick re-adjusting of production capacity in response to market changes. In this paper, we study a flow shop sequencing problem (FSSP) with controllable processing times as a special case of reconfigurable manufacturing system. It is possible to speed up the processing times through assigning additional resources or control of machine speed. After formulating this problem mathematically, a novel evolutionary procedure, entitled group search optimizer (GSO), is devised as solution method. The adapted GSO is a population-based search tool which is devised based on the producer and scrounger behavior. GSO emphasizes on imitating searching model of real-world animals. The basic GSO with four promising improvements is elaborated and discussed for addressing the FSSP with controllable processing times. A set of computational experiments is also conducted to demonstrate the applicability of proposed FSSP and performance of improved GSOs.

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References

  1. Al-Fawzan MA, Haouari M (2005) A bi-objective model for robust resource-constrained project scheduling. Int J Prod Econ 96:175–187

    Article  Google Scholar 

  2. Bulfin RL, Hallah M (2003) Minimizing the weighted number of tardy jobs on a two-machine flow shop. Comput Oper Res 30:1887–1900

    Article  MathSciNet  MATH  Google Scholar 

  3. Choi B-C, Yoon S-H, Chung S-J (2007) Single machine scheduling problem with controllable processing times and resource dependent release times. Euro J Oper Res 181:645–653

    Article  MathSciNet  MATH  Google Scholar 

  4. Daniel Ng CC, Edwin Cheng TC, Kovalyov MY, Lam SS (2003) Single machine scheduling with a variable common due date and resource-dependent processing times. Comput Oper Res 30:1173–1185

    Article  MathSciNet  MATH  Google Scholar 

  5. Grabowski J, Pempera J (2005) Some local search algorithms for no-wait flow-shop problem with makespan criterion. Comput Oper Res 32:2197–2212

    Article  MathSciNet  MATH  Google Scholar 

  6. Gupta JND, Stafford EF (2006) Flow shop scheduling research after five decades. Euro J Oper Res 169:699–711

    Article  MATH  Google Scholar 

  7. Haq AN, Ramanan TR, Shashikant KS, Sridharan R (2010) A hybrid neural network-genetic algorithm approach for permutation flow shop scheduling. Int J Prod Res 48(14):4217–4231

    Article  MATH  Google Scholar 

  8. He G, Cui Z, Tan Y (2012) Interactive dynamic neighborhood differential evolutionary group search optimizer. J Chin Comput Syst 33(4):809–814

    Google Scholar 

  9. He S (2010) Training artificial neural networks using Levy group search optimizer. J Multi-Valued Log Soft Comput 16:527–546

    MathSciNet  MATH  Google Scholar 

  10. He S, Wu QH, Saunders JR (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13(5):973–990

    Article  Google Scholar 

  11. Huang J-Y, Yao M-J (2008) A genetic algorithm for solving the economic lot scheduling problem in flow shops. Int J Prod Res 46(14):3737–3761

    Article  MATH  Google Scholar 

  12. Janiak A (1985) Time-optimal control in a single machine problem with resource constraints. Automatica 22:745–747

    Article  MATH  Google Scholar 

  13. Janiak A (1987) One-machine scheduling with allocation of continuously-divisible resource and with no precedence constraints. Kybernetika 23:289–293

    MathSciNet  MATH  Google Scholar 

  14. Janiak A, Kovalyov MY (1996) Single machine scheduling subjective to deadlines and resource dependent processing times. Eur J Oper Res 94:284–291

    Article  MATH  Google Scholar 

  15. Johnson SM (1954) Optimal two- and three-stage production schedules with setup times included. Nav Res Logist Quart 1(1):61–68

    Article  Google Scholar 

  16. Lee CY, Lei L (2001) Multiple-project scheduling with controllable project duration and hard resource constraint: some solvable cases. Ann Oper Res 102:287–307

    Article  MathSciNet  MATH  Google Scholar 

  17. Li K, Shi Y, Yang SL, Cheng BY (2011) Parallel machine scheduling problem to minimize the makespan with resource dependent processing times. Appl Soft Comput 11(8):5551–5557

    Article  Google Scholar 

  18. Mokhtari H (2015) Designing an efficient bi-criteria iterated greedy heuristic for simultaneous order scheduling and resource allocation: a balance between cost and lateness measures. Neural Comput Appl 26(5):1085–1101

    Article  Google Scholar 

  19. Mokhtari H, Abadi INK, Cheraghalikhani A (2011) A multi-objective flow shop scheduling with resource-dependent processing times: trade-off between makes pan and cost of resources. Int J Prod Res 49(19):5851–5875

    Article  Google Scholar 

  20. Mokhtari H, Abadi INK, Zegordi SH (2011) Production capacity planning and scheduling in a no-wait environment with controllable processing times: an integrated modeling approach. Expert Syst Appl 38:12630–12642

    Article  Google Scholar 

  21. Nowicki E, Zdrzalka S (1990) A survey of results for sequencing problems with controllable processing times. Discrete Appl Math 26:271–287

    Article  MathSciNet  MATH  Google Scholar 

  22. Pan JCH, Chen JS, Chao CM (2002) Minimizing tardiness in a two-machine flow-shop. Comput Oper Res 29:869–885

    Article  MathSciNet  MATH  Google Scholar 

  23. Qian B, Wang L, Huang DX, Wang WL, Wang X (2009) An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers. Comput Oper Res 36:209–233

    Article  MathSciNet  MATH  Google Scholar 

  24. Rahimi-Vahed AR, Mirghorbani SM (2007) A multi-objective particle swarm for a flow shop scheduling problem. J Comb Optim 13:79–102

    Article  MathSciNet  MATH  Google Scholar 

  25. Ravindran D, Noorul Haq A, Selvakuar SJ, Sivaraman R (2005) Flow shop scheduling with multiple objective of minimizing makespan and total flow time. Int J Adv Manuf Technol 25:1007–1012

    Article  Google Scholar 

  26. Shabtay D, Steiner G (2007) Optimal due date assignment and resource allocation to minimize the weighted number of tardy jobs on a single machine. Manuf Serv Oper Manag 9:332–350

    Google Scholar 

  27. Tavakkoli-Moghaddam R, Rahimi-Vahed A, Mirzaei AH (2007) A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives: weighted mean completion time and weighted mean tardiness. Inf Sci 177:5072–5090

    Article  MathSciNet  MATH  Google Scholar 

  28. Thornton HW, Hunsucker JL (2004) A new heuristic for minimal makespan in flow shops with multiple processors and no intermediate storage. Euro J Oper Res 152:96–114

    Article  MathSciNet  MATH  Google Scholar 

  29. Tseng C-T, Liao C-J (2008) A particle swarm optimization algorithm for hybrid flow-shop scheduling with multiprocessor tasks. Int J Prod Res 46(17):4655–4670

    Article  MATH  Google Scholar 

  30. Wang J, Ng C, Cheng T, Liu L (2006) Minimizing total completion time in a two-machine flow shop with deteriorating jobs. Appl Math Comput 180:185–193

    MathSciNet  MATH  Google Scholar 

  31. Wei C, Fang J (2013) Group search optimiser: a brief survey. Int J Comput Sci Math 4:42–50

    Article  MathSciNet  Google Scholar 

  32. Xu J, Zhou X (2009) A class of multi-objective expected value decision-making model with birandom coefficients and its application to flow shop scheduling problem. Inf Sci 179:2997–3017

    Article  MathSciNet  MATH  Google Scholar 

  33. Yang T, Kuo Y, Chang I (2004) Tabu-search simulation optimization approach for flow-shop scheduling with multiple processors—a case study. Int J Prod Res 42(19):4015–4030

    Article  MATH  Google Scholar 

  34. Yao J, Cui ZH, Wei ZH, Tan Y (2011) Hybrid group search optimiser with quadratic interpolation method and its application. Int J Wirel Mob Comput 5:98–106

    Article  Google Scholar 

  35. Zandieh M, Gholami M (2009) An immune algorithm for scheduling a hybrid flow shop with sequence-dependent setup times and machines with random breakdowns. Int J Prod Res 47(24):6999–7027

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran

    Hadi Mokhtari

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  1. Hadi Mokhtari
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Correspondence to Hadi Mokhtari.

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Mokhtari, H. Research on group search optimizers for a reconfigurable flow shop sequencing problem. Neural Comput & Applic 27, 1657–1667 (2016). https://doi.org/10.1007/s00521-015-1963-3

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  • DOI: https://doi.org/10.1007/s00521-015-1963-3

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