A coordinated scheduling problem for the supply chain in a flexible job shop machine environment

Abstract

In this study, a new coordinated scheduling problem is proposed for the multi-stage supply chain network. A multi-product and multi-period supply chain structure has been developed, including a factory, warehouses, and customers. Furthermore, the flexible job shop scheduling problem is integrated into the manufacturing part of the supply chain network to make the structure more comprehensive. In the proposed problem, each product includes a sequence of operations and is processed on a set of multi-functional machines at the factory to produce the final product. Final products are delivered to the warehouses to meet customers’ demands. If the demands of customers are not fulfilled, the shortage in the form of backorder may occur at any period. The problem is expressed as a bi-objective mixed-integer linear programming (MILP) model. The first objective function is to minimize the total supply chain costs. On the other hand, the second objective function aims to minimize the makespan in all periods. A numerical example is presented to evaluate the performance of the proposed MILP model. Five multi-objective decision-making (MODM) methods, namely weighted sum, goal programming, goal attainment, LP metric, and max–min, are used to provide different alternative solutions to the decision-makers. The performance of the methods is evaluated according to both objective function values and CPU time criteria. In order to select the best solution technique, the displaced ideal solution method is applied. The results reveal that the weighted sum method is the best among all MODM methods.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  1. Agnetis A, Hall NG, Pacciarelli D (2006) Supply chain scheduling: sequence coordination. Discret Appl Math 154:2044–2063. https://doi.org/10.1016/j.dam.2005.04.019

    Article  Google Scholar 

  2. Aminzadegan S, Tamannaei M, Rasti-Barzoki M (2019) Multi-agent supply chain scheduling problem by considering resource allocation and transportation. Comput Ind Eng 137:106003. https://doi.org/10.1016/j.cie.2019.106003

    Article  Google Scholar 

  3. Augusto O, Fouad B, Caro S, Augusto O, Fouad B, Caro S, Method AN (2013) a new method for decision making in multi-objective optimization problems to cite this version : HAL Id : hal-00914025

  4. Brucker P, Schlie R (1990) Job-shop scheduling with multi-purpose machines. Computing 45:369–375. https://doi.org/10.1007/BF02238804

    Article  Google Scholar 

  5. Çalış B, Bulkan S (2015) A research survey: review of AI solution strategies of job shop scheduling problem. J Intell Manuf 26:961–973. https://doi.org/10.1007/s10845-013-0837-8

    Article  Google Scholar 

  6. Ceylan Z, Bulkan S, Tozan H (2019) Integrated supply chain scheduling models: a literature review. J Eng Sci Design 7:182–195

    Google Scholar 

  7. Chaudhry IA, Khan AA (2016) A research survey: review of flexible job shop scheduling techniques. Int Trans Oper Res 23:551–591

    Article  Google Scholar 

  8. Chen Z (2010) Integrated production and outbound distribution scheduling. Rev Extens 58:130–148. https://doi.org/10.1287/opre.1080.0688

    Article  Google Scholar 

  9. Chen ZL, Vairaktarakis GL (2005) Integrated scheduling of production and distribution operations. Manag Sci 51:614–628. https://doi.org/10.1287/mnsc.1040.0325

    Article  Google Scholar 

  10. Cheraghalipour A, Paydar MM, Hajiaghaei-Keshteli M (2019) Designing and solving a bi-level model for rice supply chain using the evolutionary algorithms. Comput Electron Agric 162:651–668. https://doi.org/10.1016/j.compag.2019.04.041

    Article  Google Scholar 

  11. Dai M, Tang D, Giret A, Salido MA (2019) Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robot Comput Integr Manuf 59:143–157. https://doi.org/10.1016/j.rcim.2019.04.006

    Article  Google Scholar 

  12. Demir Y, İşleyen SK (2014) An effective genetic algorithm for flexible job-shop scheduling with overlapping in operations. https://doi.org/10.1080/00207543.2014.889328

  13. Donoso Y, Fabregat R (2016) Multi-objective optimization in computer networks using metaheuristics

  14. Fadavi M, Sahraeian R, Rohaninejad M (2017) A new model for integrated lot sizing and scheduling in flexible job shop problem. J Ind Syst Eng 10:72–91

    Google Scholar 

  15. Faruk Ö, Pardalos PM (2017) Minimizing average lead time for the coordinated scheduling problem in a two-stage supply chain with multiple customers and multiple manufacturers. Comput Ind Eng 114:244–257. https://doi.org/10.1016/j.cie.2017.10.018

    Article  Google Scholar 

  16. Ganji M, Kazemipoor H, Hadji Molana SM, Sajadi SM (2020) A green multi-objective integrated scheduling of production and distribution with heterogeneous fleet vehicle routing and time windows. J Clean Prod 259:120824. https://doi.org/10.1016/j.jclepro.2020.120824

    Article  Google Scholar 

  17. Gharaei A, Jolai F (2018) A multi-agent approach to the integrated production scheduling and distribution problem in multi-factory supply chain. Appl Soft Comput 65:577–589. https://doi.org/10.1016/J.ASOC.2018.02.002

    Article  Google Scholar 

  18. Gharaei A, Jolai F (2019) A Pareto approach for the multi-factory supply chain scheduling and distribution problem. Oper Res. https://doi.org/10.1007/s12351-019-00536-7

    Article  Google Scholar 

  19. Gholami F, Paydar MM, Hajiaghaei-Keshteli M, Cheraghalipour A (2019) A multi-objective robust supply chain design considering reliability. J Ind Prod Eng 36:385–400. https://doi.org/10.1080/21681015.2019.1658136

    Article  Google Scholar 

  20. Golpîra H, Tirkolaee EB (2019) Stable maintenance tasks scheduling: a bi-objective robust optimization model. Comput Ind Eng 137:106007. https://doi.org/10.1016/j.cie.2019.106007

    Article  Google Scholar 

  21. Hall NG, Potts CN (2003) Supply chain scheduling: batching and delivery. Oper Res 51:566–584

    Article  Google Scholar 

  22. Hamid S, Pasandideh R, Taghi S, Niaki A, Asadi K (2015) Expert systems with applications optimizing a bi-objective multi-product multi-period three echelon supply chain network with warehouse reliability. Expert Syst Appl 42:2615–2623. https://doi.org/10.1016/j.eswa.2014.11.018

    Article  Google Scholar 

  23. Hamidinia A, Khakabimamaghani S, Mazdeh MM, Jafari M (2012) A genetic algorithm for minimizing total tardiness/earliness of weighted jobs in a batched delivery system q. Comput Ind Eng 62:29–38. https://doi.org/10.1016/j.cie.2011.08.014

    Article  Google Scholar 

  24. Hassanzadeh A, Rasti-barzoki M, Khosroshahi H (2016) Two new meta-heuristics for a bi-objective supply chain scheduling problem in flow-shop environment. Appl Soft Comput 49:335–351

    Article  Google Scholar 

  25. Isaloo F, Paydar MM (2020) Optimizing a robust bi-objective supply chain network considering environmental aspects: a case study in plastic injection industry. Int J Manag Sci Eng Manag 15:26–38. https://doi.org/10.1080/17509653.2019.1592720

    Article  Google Scholar 

  26. Kazemi H, Mazdeh MM, Rostami M (2017) The two stage assembly flow-shop scheduling problem with batching and delivery. Eng Appl Artif Intell 63:98–107. https://doi.org/10.1016/j.engappai.2017.05.004

    Article  Google Scholar 

  27. Khalilpourazari S, Khamseh AA (2019) Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: a comprehensive study with real world application. Ann Oper Res 283:355–393. https://doi.org/10.1007/s10479-017-2588-y

    Article  Google Scholar 

  28. Manoj UV, Gupta JND, Gupta SK, Sriskandarajah C (2008) Supply chain scheduling: just-in-time environment. Ann Oper Res 161:53–86

    Article  Google Scholar 

  29. Mazdeh MM, Rostami M (2014) A branch-and-bound algorithm for two-machine flow-shop scheduling problems with batch delivery costs. Int J Syst Sci Oper Logist 1:94–104. https://doi.org/10.1080/23302674.2014.942408

    Article  Google Scholar 

  30. Mazdeh MM, Hamidinia A, Karamouzian A (2011) A mathematical model for weighted tardy jobs scheduling problem with a batched delivery system. Int J Ind Eng Comput 2:491–498. https://doi.org/10.5267/j.ijiec.2011.04.003

    Article  Google Scholar 

  31. Min C, Soo B (2017) Rule-based meta-heuristics for integrated scheduling of unrelated parallel machines, batches, and heterogeneous delivery trucks. Appl Soft Comput J 53:457–476. https://doi.org/10.1016/j.asoc.2016.12.038

    Article  Google Scholar 

  32. Mohammadi S, Al-e-Hashem SMJM, Rekik Y (2020) An integrated production scheduling and delivery route planning with multi-purpose machines: a case study from a furniture manufacturing company. Int J Prod Econ 219:347–359. https://doi.org/10.1016/j.ijpe.2019.05.017

    Article  Google Scholar 

  33. Pei J, Liu X, Pardalos PM (2014) Application of an effective modified gravitational search algorithm for the coordinated scheduling problem in a two-stage supply chain, pp 335–348. https://doi.org/10.1007/s00170-013-5263-8

  34. Pei J, Liu X, Fan W, Pardalos PM, Lu S (2019) A hybrid BA-VNS algorithm for coordinated serial-batching scheduling with deteriorating jobs, financial budget, and resource constraint in multiple manufacturers. Omega 82:55–69. https://doi.org/10.1016/j.omega.2017.12.003

    Article  Google Scholar 

  35. Pinedo M (2012) Scheduling. Springer, Berlin

    Google Scholar 

  36. Rafiei H, Safaei F, Rabbani M (2018) Integrated production-distribution planning problem in a competition-based four-echelon supply chain. Comput Ind Eng 119:85–99. https://doi.org/10.1016/j.cie.2018.02.031

    Article  Google Scholar 

  37. Rahimi E, Paydar MM, Mahdavi I, Jouzdani J (2018) A robust optimization model for multi-objective multi-period supply chain planning under uncertainty considering quantity discounts. J Ind Prod Eng 1015:1–15. https://doi.org/10.1080/21681015.2018.1441195

    Article  Google Scholar 

  38. Rasti-barzoki M, Hejazi SR (2013) Minimizing the weighted number of tardy jobs with due date assignment and capacity-constrained deliveries for multiple customers in supply chains. Eur J Oper Res 228:345–357. https://doi.org/10.1016/j.ejor.2013.01.002

    Article  Google Scholar 

  39. Rostami M, Kheirandish O, Ansari N (2015) Minimizing maximum tardiness and delivery costs with batch delivery and job release times. Appl Math Model 39:4909–4927. https://doi.org/10.1016/j.apm.2015.03.052

    Article  Google Scholar 

  40. Rostami M, Nikravesh S, Shahin M (2018) Minimizing total weighted completion and batch delivery times with machine deterioration and learning effect: a case study from wax production. Oper Res. https://doi.org/10.1007/s12351-018-0373-6

    Article  Google Scholar 

  41. Steiner G, Zhang R (2009) Approximation algorithms for minimizing the total weighted number of late jobs with late deliveries in two-level supply chains, pp 565–574. https://doi.org/10.1007/s10951-009-0109-9

  42. Tamannaei M, Rasti-Barzoki M (2019) Mathematical programming and solution approaches for minimizing tardiness and transportation costs in the supply chain scheduling problem. Comput Ind Eng 127:643–656. https://doi.org/10.1016/j.cie.2018.11.003

    Article  Google Scholar 

  43. Türkyılmaz A, Bulkan S (2015) A hybrid algorithm for total tardiness minimisation in flexible job shop: genetic algorithm with parallel VNS execution. Int J Prod Res. https://doi.org/10.1080/00207543.2014.962113

    Article  Google Scholar 

  44. Türkyılmaz A, Şenvar Ö, Ünal İ, Bulkan S (2020) A research survey: heuristic approaches for solving multi objective flexible job shop problems. J Intell Manuf. https://doi.org/10.1007/s10845-020-01547-4

    Article  Google Scholar 

  45. Ullrich CA (2013) Integrated machine scheduling and vehicle routing with time windows. Eur J Oper Res 227:152–165. https://doi.org/10.1016/j.ejor.2012.11.049

    Article  Google Scholar 

  46. Vo-Duy T, Duong-Gia D, Ho-Huu V, Vu-Do HC, Nguyen-Thoi T (2017) Multi-objective optimization of laminated composite beam structures using NSGA-II algorithm. Compos Struct 168:498–509. https://doi.org/10.1016/J.COMPSTRUCT.2017.02.038

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Zeynep Ceylan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ceylan, Z., Tozan, H. & Bulkan, S. A coordinated scheduling problem for the supply chain in a flexible job shop machine environment. Oper Res Int J (2021). https://doi.org/10.1007/s12351-020-00615-0

Download citation

Keywords

  • Supply chain
  • Coordination
  • Flexible job shop scheduling
  • Multi-objective
  • MODM