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A Nash bargaining model for flow shop scheduling problem under uncertainty: a case study from tire manufacturing in Iran

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Abstract

Production scheduling has a considerable impact on productivity and resource assignment. In many situations, every job has an owner, which is called an agent. Since the agents are independent and selfish, it is possible that they have not any incentive to cooperate. Scheduling games help us to understand interactions between the agents. In this study, we consider a real firm with a flow shop manufacturing system that receives various orders from different agents so that each order belongs to a unique agent and includes some jobs. We propose a Nash bargaining model to find a compromise solution among agents. We suppose the utilities of the agents in disagreement point are non-deterministic. Therefore, to overcome this problem, we used linear programming with interval coefficients in order to find the best and the worst Nash bargaining solution. To find a compromised solution, we propose an improved genetic algorithm and compare it with other meta-heuristic algorithms. The comparisons indicate that the proposed algorithm has a good potential to evaluation of Nash bargaining problem in hybrid flow shop environment. Based on the results to reach an agreement between agents, it is required to create a trade-off between usage rates of fastest machines at each stage especially in bottleneck stages and total processing time of orders. The results indicate that the Nash bargaining solution is suitable to solve real-life agent-based production scheduling with the consideration of interactions among the agents when disagreement points are under uncertainty.

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References

  1. Safari E, Sadjadi J (2011) A hybrid method for flowshops scheduling with condition-based maintenance. Expert Syst Appl 38(3):2020–2029. https://doi.org/10.1016/j.eswa.2010.07.138

    Article  Google Scholar 

  2. Li J, Tao F, Cheng Y, Zhao L (2015) Big data in product lifecycle management. Int J Adv Manuf Technol 81(1–4):667–684. https://doi.org/10.1007/s00170-015-7151-x

    Article  Google Scholar 

  3. Jia W, Jiang Z, Li Y (2015) Scheduling to minimize the makespan in large-piece one-of-a-kind production with machine availability constraints. Expert Syst Appl 42(23):9174–9182. https://doi.org/10.1016/j.eswa.2015.08.012

    Article  Google Scholar 

  4. Mokhtari H, Dadgar M (2015) Scheduling optimization of a stochastic flexible job-shop system with time-varying machine failure rate. Comput Oper Res 61:31–45. https://doi.org/10.1016/j.cor.2015.02.014

    Article  MathSciNet  MATH  Google Scholar 

  5. Papadimitriou C (2001) Algorithms, games, and the internet. In: In symposium on theory of computing STOC, pp 749–753

  6. Lin L, Tan Z (2014) Inefficiency of Nash equilibrium for scheduling games with constrained jobs: a parametric analysis. Theor Comput Sci 521:123–134. https://doi.org/10.1016/j.tcs.2013.11.012

    Article  MathSciNet  MATH  Google Scholar 

  7. Nash JF (1950) The bargaining problem. Econometrica 18(2):155–162. https://doi.org/10.2307/1907266

    Article  MathSciNet  MATH  Google Scholar 

  8. Yaacoub E, Dawy Z (2011) Achieving the Nash bargaining solution in OFDMA uplink using distributed scheduling with limited feedback. AEU – Int J of Electron Commun 65(4):320–330. https://doi.org/10.1016/j.aeue.2010.03.007

    Article  Google Scholar 

  9. Epstein L, Feldman M, Tamir T, Witkowski Ł, Witkowski M (2013) Approximate strong equilibria in job scheduling games with two uniformly related machines. Discret Appl Math 161(13-14):1843–1858. https://doi.org/10.1016/j.dam.2013.02.035

    Article  MathSciNet  MATH  Google Scholar 

  10. Feldman M (2008) Approximate strong equilibrium in job scheduling games. In: Monien B, Schroeder U-P (eds) Algorithmic game theory: first international symposium, SAGT 2008, Paderborn, Germany, April 30-May 2, 2008. Proceedings. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 58–69. doi:https://doi.org/10.1007/978-3-540-79309-0_7

  11. Zhou G, Jiang P, Huang GQ (2009) A game-theory approach for job scheduling in networked manufacturing. Int J Adv Manuf Technol 41(9):972–985. https://doi.org/10.1007/s00170-008-1539-9

    Article  Google Scholar 

  12. Spata MO, Rinaudo S (2012) A job scheduling game based on a folk algorithm. In: Greco S, Bouchon-Meunier B, Coletti G, Fedrizzi M, Matarazzo B, Yager RR (eds) Advances in computational intelligence: 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, Catania, Italy, July 9–13, 2012, Proceedings, Part IV. Springer Berlin Heidelberg, pp 622–631. doi:https://doi.org/10.1007/978-3-642-31724-8_65

  13. Thang NK (2013) NP-hardness of pure Nash equilibrium in scheduling and network design games. Theor Comput Sci 482:86–95. https://doi.org/10.1016/j.tcs.2012.10.051

    Article  MathSciNet  MATH  Google Scholar 

  14. (2014) A game-theoretic approach for the web services scheduling problem. Expert Systems with Applications 41:4743–4751

  15. Hassin R, Yovel U (2015) Sequential scheduling on identical machines. Oper Res Lett 43(5):530–533. https://doi.org/10.1016/j.orl.2015.08.003

    Article  MathSciNet  Google Scholar 

  16. Xie F, Zhang Y, Bai Q, Xu Z (2015) Inefficiency analysis of the scheduling game on limited identical machines with activation costs. Inf Process Lett 15

  17. Cole R, Correa JR, Gkatzelisa V, Mirrokni V, Olver N (2015) Decentralized utilitarian mechanisms for scheduling games. Games Econ Behav 92:306–326. https://doi.org/10.1016/j.geb.2013.03.011

    Article  MathSciNet  MATH  Google Scholar 

  18. Du B, Guo S (2016) Production planning conflict resolution of complex product system in group manufacturing: a novel hybrid approach using ant colony optimization and Shapley value. Comput Ind Eng 94:158–169. https://doi.org/10.1016/j.cie.2015.12.015

    Article  Google Scholar 

  19. Nong Q-Q, Guo S-J, Miao L-H (2016) The shortest first coordination mechanism for a scheduling game with parallel-batching machines. J Oper Res Soc China 4(4):517–527. https://doi.org/10.1007/s40305-016-0134-2

    Article  MathSciNet  MATH  Google Scholar 

  20. Gan X, Gu Y, Vairaktarakis GL, Cai X, Chen Q (2007) A scheduling problem with one producer and the bargaining counterpart with two producers. In: Chen B, Paterson M, Zhang G (eds) Combinatorics, algorithms, probabilistic and experimental methodologies: First International Symposium, ESCAPE 2007, Hangzhou, China, April 7–9, 2007, Revised Selected Papers. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 305–316. doi:https://doi.org/10.1007/978-3-540-74450-4_28

  21. Suh S-C, Wen Q (2006) Multi-agent bilateral bargaining and the Nash bargaining solution. J Math Econ 42(1):61–73. https://doi.org/10.1016/j.jmateco.2005.06.001

    Article  MathSciNet  MATH  Google Scholar 

  22. Touati C, Altman E, Galtier J (2006) Generalized Nash bargaining solution for bandwidth allocation. Comput Netw 50(17):3242–3263. https://doi.org/10.1016/j.comnet.2005.12.006

    Article  MATH  Google Scholar 

  23. Lippman SA, McCardle KF, Tang CS (2013) Using Nash bargaining to design project management contracts under cost uncertainty. Int J Prod Econ 145(1):199–207. https://doi.org/10.1016/j.ijpe.2013.04.036

    Article  Google Scholar 

  24. Wang M, Li Y (2014) Supplier evaluation based on Nash bargaining game model. Expert Syst Appl 41(9):4181–4185. https://doi.org/10.1016/j.eswa.2013.12.044

    Article  Google Scholar 

  25. Kerachian R, Fallahnia M, Bazargan-Lari MR, Mansoori A, Sedghi H (2010) A fuzzy game theoretic approach for groundwater resources management: application of Rubinstein Bargaining Theory. Resour Conserv Recycl 54(10):673–682. https://doi.org/10.1016/j.resconrec.2009.11.008

    Article  Google Scholar 

  26. Hawkins WB (2015) Bargaining with commitment between workers and large firms. Rev Econ Dyn 18(2):350–364. https://doi.org/10.1016/j.red.2014.06.003

    Article  Google Scholar 

  27. Hamidi M, Liao H (2017) Maintenance outsourcing contracts based on bargaining theory. In: Matsumoto A (ed) Optimization and Dynamics with Their Applications: Essays in Honor of Ferenc Szidarovszky. Springer Singapore, Singapore, pp 257–279. doi:https://doi.org/10.1007/978-981-10-4214-0_12

  28. Chen JC, Wu CC (2012) Flexible job shop scheduling with parallel machine susing genetic algorithm and grouping genetic algorithm. Exp Syst Appl 39:10016–10021

    Article  Google Scholar 

  29. Chiang TS, Lin HJ (2013) A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling. Int J Prod Econ 141:87–98

    Article  Google Scholar 

  30. Xiong J, Xing LN, Chen YW (2013) Robust scheduling for multi-objective flexible job shop problems with random machine break downs. Int J Prod Econ 141

  31. Costa A, Cappadonna FA, Fichera S (2014) A novel genetic algorithm for the hybrid flow shop scheduling with parallel batching and eligibility constraints. Int J Adv Manuf Technol 75(5–8):833–847

    Article  Google Scholar 

  32. Driss I, Mouss KN, Laggoun A (2015) A new genetic algorithm for flexible job-shop scheduling problems. J Mech Sci Technol 29(3):1273–1281

    Article  Google Scholar 

  33. Jun S, Park J (2015) A hybrid genetic algorithm for the hybrid flow shop scheduling problem with nighttime work and simultaneous work constraints: a case study from the transformer industry. Expert Syst Appl 42(15-16):6196–6204. https://doi.org/10.1016/j.eswa.2015.03.012

    Article  Google Scholar 

  34. Li X, Gao L (2016) An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. Int J Prod Econ 174:93–110

    Article  Google Scholar 

  35. B Naderi, S Gohari, M Yazdani (2014) Hybrid flexible flowshop problems: models and solution methods. Applied Mathematical Modelling

  36. Nash J (1953) Two-person cooperative games. Econometrica 21(1):128–140. https://doi.org/10.2307/1906951

    Article  MathSciNet  MATH  Google Scholar 

  37. Peters H (2015) Game theory: a multi-leveled approach. Springer, DOI: https://doi.org/10.1007/978-3-662-46950-7,

  38. Mas-Colell A, Whinston MD, Green JR (1995) Microeconomic theory, vol 1. Oxford University Press, New York

    MATH  Google Scholar 

  39. Chinneck JW, Ramadan K (2000) Linear programming with interval coefficients. J Oper Res Soc 51(2):209–220. https://doi.org/10.1057/palgrave.jors.2600891

    Article  MATH  Google Scholar 

  40. Kelly FP, Maulloo AK, Tan DK (1998) Rate control for communication networks: shadow prices, proportional fairness and stability. J Oper Res Soc 49(3):237–252. https://doi.org/10.1057/palgrave.jors.2600523

    Article  MATH  Google Scholar 

  41. Gupta JND (1988) Two-stage, hybrid flowshop scheduling problem. J Oper Res Soc 39(4):359–364. https://doi.org/10.1057/jors.1988.63

    Article  MATH  Google Scholar 

  42. Lenstra JK, Rinnooy Kan AHG, Brucker P (1977) Complexity of machine scheduling problems. Ann Discrete Math 1(1):343–362. https://doi.org/10.1016/S0167-5060(08)70743-X

    Article  MathSciNet  MATH  Google Scholar 

  43. Abdollahpour S, Rezaeian J (2015) Minimizing makespan for flow shop scheduling problem with intermediate buffers by using hybrid approach of artificial immune system. Appl Soft Comput 28:44–56. https://doi.org/10.1016/j.asoc.2014.11.022

    Article  Google Scholar 

  44. de Souza CDR, D’Agosto MA (2013) Value chain analysis applied to the scrap tire reverse logistics chain: an applied study of co-processing in the cement industry. Resour Conserv Recycl 78:15–25. https://doi.org/10.1016/j.resconrec.2013.06.007

    Article  Google Scholar 

  45. Gupta V, Narayanamurthy G, Acharya P (2017) Can lean lead to green? Assessment of radial tyre manufacturing processes using system dynamics modelling. Comput Oper Res 89:284–306. https://doi.org/10.1016/j.cor.2017.03.015

    Article  Google Scholar 

  46. Reid DR, Nada RS (2011) Operations management an integrated approach, vol 1. John Wiley & Sons Inc

  47. Djatna T, Munichputranto F (2015) An analysis and design of mobile business intelligence system for productivity measurement and evaluation in tire curing production line. Procedia Manuf 4:438–444. https://doi.org/10.1016/j.promfg.2015.11.060

    Article  Google Scholar 

  48. Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2017). Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 1-14

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Safari, G., Hafezalkotob, A. & Khalilzadeh, M. A Nash bargaining model for flow shop scheduling problem under uncertainty: a case study from tire manufacturing in Iran. Int J Adv Manuf Technol 96, 531–546 (2018). https://doi.org/10.1007/s00170-017-1461-0

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  • DOI: https://doi.org/10.1007/s00170-017-1461-0

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