Advertisement

Annals of Operations Research

, Volume 275, Issue 2, pp 685–714 | Cite as

Supply chain scheduling in a collaborative manufacturing mode: model construction and algorithm design

  • Liang TangEmail author
  • Zhihong Jin
  • Xuwei Qin
  • Ke Jing
Original Research
  • 198 Downloads

Abstract

In collaborative manufacturing, the supply chain scheduling problem becomes more complex according to both multiple product demands and multiple production modes. Aiming to obtain a reasonable solution to this complexity, we analyze the characteristics of collaborative manufacturing and design some elements, including production parameters, order parameters, and network parameters. We propose four general types of collaborative manufacturing networks and then construct a supply chain scheduling model composed of the processing costs, inventory costs, and two penalty costs of the early completion costs and tardiness costs. In our model, by considering the urgency of different orders, we design a delivery time window based on the least production time and slack time. Additionally, due to the merit of continuously processing orders belonging to the same product type, we design a production cost function by using a piecewise function. To solve our model efficiently, we present a hybrid ant colony optimization (HACO) algorithm. More specifically, the Monte Carlo algorithm is incorporated into our HACO algorithm to improve the solution quality. We also design a moving window award mechanism and dynamic pheromone update strategy to improve the search efficiency and solution performance. Computational tests are conducted to evaluate the performance of the proposed method.

Keywords

Collaborative manufacturing network HACO algorithm Supply chain scheduling Monte Carlo Moving window 

Notes

Acknowledgements

This work was supported by Grant 71301108, 71201106, 71472034, 71572023 from the National Natural Science Foundation of China, and Grant 18YJC630061 from the Humanity and Social Science Youth Foundation of Ministry of Education of China.

References

  1. Agnetic, A., Mirchandani, P. B., Pacciarelli, D., & Pacifici, A. (2004). Scheduling problems with two competing agents. Operations Research, 52, 229–242.Google Scholar
  2. Agnetis, A., Aloulou, M. A., & Fu, L. L. (2014). Coordination of production and interstage batch delivery with outsourced distributuion. European Journal of Operational Research, 238(1), 130–142.Google Scholar
  3. Agnetis, A., Hall, N. G., & Pacciarelli, D. (2006). Supply chain scheduling: Sequence coordination. Discrete Applied Mathematics, 154(15), 2044–2063.Google Scholar
  4. Asadzadeh, L. (2015). A local search genetic algorithm for the job shop scheduling problem with intelligent agents. Computers & Industrial Engineering, 85, 376–383.Google Scholar
  5. Averbakh, I. (2010). On-line integrated production–distribution scheduling problems with capacitated deliveries. European Journal of Operational Research, 200(2), 377–384.Google Scholar
  6. Aydinliyim, T., & Vairaktarakis, G. L. (2010). Coordination of outsourced operations to minimize weighted flow time and capacity booking costs. Manufacturing & Service Operations Management, 12(2), 236–255.Google Scholar
  7. Babiceanu, R. F., Chen, F. F., & Sturges, R. H. (2005). Real-time holonic scheduling of material handling operations in a dynamic manufacturing environment. Robotics and Computer-Integrated Manufacturing, 21(4–5), 328–337.Google Scholar
  8. Baker, K. R., & Smith, J. C. (2003). A multiple-criterion model for machine scheduling. Journal of Scheduling, 6, 7–16.Google Scholar
  9. Blazewicz, J., Echker, K. H., Pesch, E., Schmidt, G., & Weglarz, J. (2001). Scheduling computer and manufacturing processes (2nd ed.). New Jersey: Springer.Google Scholar
  10. Blum, C. (2005). Beam-ACO: Hybridizing ant colony optimization with beam search: An application to open shop scheduling. Computers & Operations Research, 32(6), 1565–1591.Google Scholar
  11. Blum, C., & Sampels, M. (2004). An ant colony optimization algorithm for shop scheduling problems. Journal of Mathematical Modelling and Algorithms, 3(3), 285–308.Google Scholar
  12. Bose, S., & Pekny, J. F. (2000). A model predictive framework for planning and scheduling problems: A case study of consumer goods supply chain. Computers & Chemical Engineering, 24, 329–335.Google Scholar
  13. Chaudhry, I. A., & Khan, A. A. (2016). A research survey: Review of flexible job shop scheduling techniques. International Transactions in Operational Research, 23(3), 551–591.Google Scholar
  14. Chen, Z.-L. (2010). Integrated production and outbound distribution scheduling: Review and extensions. Operations Research, 58(1), 130–148.Google Scholar
  15. Chen, Z. L., & Pundoor, G. (2006). Order assignment and scheduling in a supply chain. Operations Research, 54(3), 555–572.Google Scholar
  16. Chen, Z. L., & Vairaktarakis, G. L. (2005). Integrated scheduling of production and distribution operations. Management Science, 51(4), 614–628.Google Scholar
  17. Denkena, B., Battino, A., & Woelk, P. O. (2005). Intelligent software agents as a basis for collaborative manufacturing systems. First IPROMS Virtual Conference (pp. 17–22). Oxford: Elsevier Ltd.Google Scholar
  18. Dorigo, M. (1992). Optimization, Learning and Natural Algorithms. Ph.D. Thesis, Dip. Elettronica. Google Scholar
  19. Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Cambridge: MIT Press.Google Scholar
  20. Feng, Q., Fan, B. Q., Li, D. S., & Shang, W. P. (2015). Two-agent scheduling with rejection on a single machine. Applied Mathematical Modelling, 39(3–4), 1183–1193.Google Scholar
  21. Fu, B., Huo, Y., & Zhao, H. (2012). Coordinated scheduling of production and delivery with production window and delivery capacity constraints. Theoretical Computer Science, 422, 39–51.Google Scholar
  22. Gong, H., & Tang, L. (2011). Two-machine flowshop scheduling with intermediate transportation under job physical space consideration. Computer & Operations Research, 38(9), 1267–1274.Google Scholar
  23. Gou, L., Luh, P. B., & Kyoya, Y. (1998). Holonic manufacturing scheduling: Architecture, cooperation mechanism, and implementation. Computers in Industry, 37(3), 213–231.Google Scholar
  24. Hall, N. G., & Potts, C. N. (2003). Supply chain scheduling: Batching and delivery. Operations Research, 51, 566–584.Google Scholar
  25. Heinonen, J., & Pettersson, F. (2007). Hybrid ant colony optimization and visibility studies applied to a job-shop scheduling problem. Applied Mathematics and Computation, 187(2), 989–998.Google Scholar
  26. Huang, Y. M., & Lin, J. C. (2011). A new bee colony optimization algorithm with idle-time-based filtering scheme for open shop-scheduling problems. Expert Systems with Applications, 38(5), 5438–5447.Google Scholar
  27. Huang, R. H., & Yang, C. L. (2008). Ant colony system for job shop scheduling with time windows. The International Journal of Advanced Manufacturing Technology, 39(1–2), 151–157.Google Scholar
  28. Huang, R. H., Yang, C. L., & Cheng, W. C. (2013). Flexible job shop scheduling with due window—a two-pheromone ant colony approach. International Journal of Production Economics, 141(2), 685–697.Google Scholar
  29. Indriago, C., Cardin, O., Bellenguez-Morineau, O., Rakoto, N., & Castagna, P. (2015). Performance evaluation of holonic-based online scheduling for a switch arrival system. IFAC-PapersOnLine, 48(3), 1105–1110.Google Scholar
  30. Jana, T. K., Bairagi, B., Paul, S., Sarkar, B., & Saha, J. (2013). Dynamic schedule execution in an agent based holonic manufacturing system. Journal of Manufacturing Systems, 32(4), 801–816.Google Scholar
  31. Karimi, N., & Davoudpour, H. (2015). A branch and bound method for solving multi-factory supply chain scheduling with batch delivery. Expert System with Applications, 42(1), 238–245.Google Scholar
  32. Kolisch, R. (2000). Integration of assembly and fabrication for make-to-order production. International Journal of Production Economics, 68, 287–306.Google Scholar
  33. Kolisch, R., & Hess, K. (2000). Efficient methods for scheduling make-to-order assemblies under resource, assembly area and part availability constraints. International Journal of Production Research, 38(1), 207–228.Google Scholar
  34. Lee, C. Y., & Chen, Z. L. (2001). Machine scheduling with transportation considerations. Journal of Scheduling, 4, 3–24.Google Scholar
  35. Lee, Y., Jeong, C., & Moon, C. (2002). Advanced planning and scheduling with outsourcing in manufacturing supply chain. Computers & Industrial Engineering, 43(1), 351–374.Google Scholar
  36. Lei, D. M. (2015). Variable neighborhood search for two-agent flow shop scheduling problem. Computers & Industrial Engineering, 80, 125–131.Google Scholar
  37. Leung, J. Y. T., & Chen, Z. L. (2013). Integrated production and distribution with fixed delivery departure dates. Operations Research Letters, 41(3), 290–293.Google Scholar
  38. Li, X., Peng, Z., Du, B., Guo, J., Xu, W., & Zhuang, K. (2017). Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems. Computers & Industrial Engineering, 113, 10–26.Google Scholar
  39. Li, C. L., & Vairaktarakis, G. (2007). Coordinating production and distribution of jobs with bundling operations. IIE Transaction, 39(2), 203–215.Google Scholar
  40. Moon, C., Kim, J., & Hur, S. (2002). Integrated process planning and scheduling with minimizing total tardiness in multi-plants supply chain. Computers & Industrial Engineering, 43(1), 331–349.Google Scholar
  41. Mor, B., & Mosheiov, G. (2010). Scheduling problems with two competing agents to minimize minmax and minsum earliness measures. European Journal of Operational Research, 206(3), 540–546.Google Scholar
  42. Nouri, H. E., Driss, O. B., & Ghédira, K. (2015). Hybrid metaheuristics within a holonic multiagent model for the flexible job shop problem. Procedia Computer Science, 60, 83–92.Google Scholar
  43. Nouri, H. E., Driss, O. B., & Ghédira, K. (2016). A classification schema for the job shop scheduling problem with transportation resources: State-of-the-art review. In Proceedings of the 5th computer science on-line conference, artificial intelligence perspectives in intelligent systems (Vol 1, pp. 1–11). Springer International Publishing.Google Scholar
  44. Nouri, H. E., Driss, O. B., & Ghédira, K. (2016b). Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic multiagent model. Computers & Industrial Engineering, 102, 488–501.Google Scholar
  45. Panahi, H., & Tavakkoli-Moghaddam, R. (2011). Solving a multi-objective open shop scheduling problem by a novel hybrid ant colony optimization. Expert Systems with Applications, 38(3), 2817–2822.Google Scholar
  46. Rossi, A. (2014). Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships. International Journal of Production Economics, 153, 253–267.Google Scholar
  47. Sawik, T. (2002). Monolithic vs. hierarchical balancing and scheduling of a flexible assembly line. European Journal of Operational Research, 143, 115–124.Google Scholar
  48. Sawik, T. (2014a). Joint supplier selection and scheduling of customer orders under disruption risks: Single versus dual sourcing. Omega, 43, 83–95.Google Scholar
  49. Sawik, T. (2014b). Optimization of cost and service level in the presence of supply chain disruption risks: Single vs. multiple sourcing. Computers & Operations Research, 51, 11–20.Google Scholar
  50. Selvarajah, E., & Zhang, R. (2014). Supply chain scheduling at the manufacturer to minimize inventory holding and delivery cost. International Journal of Production Economics, 147, 117–124.Google Scholar
  51. Shen, L., Dauzère-Pérès, S., & Neufeld, J. S. (2018). Solving the flexible job shop scheduling problem with sequence-dependent setup times. European Journal of Operational Research, 265(2), 503–516.Google Scholar
  52. Shen, W., Hao, Q., Yoon, H. J., & Norrie, D. H. (2006). Applications of agent-based systems in intelligent manufacturing: An updated review. Advanced Engineering Informatics, 20(4), 415–431.Google Scholar
  53. Shiau, Y. R., Lee, W. C., Kung, Y. S., & Wang, J. Y. (2016). A lower bound for minimizing the total completion time of a three-agent scheduling problem. Information Sciences, 340–341, 305–320.Google Scholar
  54. Shyu, S. J., Lin, B. M. T., & Yin, P. Y. (2004). Application of ant colony optimization for no-wait flowshop scheduling problem to minimize the total completion time. Computers & Industrial Engineering, 47(2), 181–193.Google Scholar
  55. Song, X. Y., Chang, C. G., & Cao, Y. (2006). Study on the combination of genetic algorithms and ant colony algorithms for solving fuzzy job shop scheduling problems. IMACS Multi conference on Computational Engineering in Systems Applications, 29(7), 1904–1909.Google Scholar
  56. Tang, L., Jing, K., & He, J. (2013). An improved ant colony optimisation algorithm for three-tier supply chain scheduling based on networked manufacturing. International Journal of Production Research, 51(13), 3945–3962.Google Scholar
  57. Thiruvady, D., Singh, G., Ernst, A. T., & Meyer, B. (2013). Constraint-based ACO for a shared resource constrained scheduling problem. International Journal of Production Economics, 141(1), 230–242.Google Scholar
  58. Ullrich, C. A. (2013). Integrated machine scheduling and vehicle routing with time windows. European Journal of Operational Research, 227(1), 152–165.Google Scholar
  59. Wang, D. J., Yin, Y. Q., Xu, J. Y., Wu, W. H., Cheng, S. R., & Wu, C. C. (2015). Some due date determination scheduling problems with two agents on a single machine. International Journal of Production Economics, 168, 81–90.Google Scholar
  60. Xu, R., Chen, H., & Li, X. (2013). A bi-objective scheduling problem on batch machines via a Pareto-based ant colony system. International Journal of Production Economics, 145(1), 371–386.Google Scholar
  61. Yan, J., Ye, K., Wang, H., & Hua, Z. (2010). Ontology of collaborative manufacturing: Alignment of service-oriented framework with service dominant logic. Expert Systems with Applications, 37(3), 2222–2231.Google Scholar
  62. Yeung, W. K., Choi, T. M., & Cheng, T. C. E. (2010). Optimal scheduling of a single-supplier single-manufacturer supply chain with common due windows. IEEE Transactions on Automatic Control, 55(12), 2767–2777.Google Scholar
  63. Yeung, W. K., Choi, T. M., & Cheng, T. C. E. (2011). Supply chain scheduling and coordination with dual delivery modes and inventory storage cost. International Journal of Production Economics, 132, 223–229.Google Scholar
  64. Yimer, A., & Demirli, K. (2010). A genetic approach to two-phase optimization of dynamic supply chain scheduling. Computers & Industrial Engineering, 58(3), 411–422.Google Scholar
  65. Yin, Y. Q., Cheng, S. R., Cheng, T. C. E., Wang, D. J., & Wu, C. C. (2015). Just-in-time scheduling with two competing agents on unrelated parallel machines. Omega.  https://doi.org/10.1016/j.omega.2015.09.010.Google Scholar
  66. You, D. M., & Chen, J. (2003). Application of ant system algorithm in multi-object traveling salesman problem. Mini-Micro Systems, 24(10), 1808–1811.Google Scholar
  67. Zegordi, S. H., Abadi, I. N. K., & Nia, M. A. B. (2010). A novel genetic algorithm for solving production and transportation scheduling in a two-stage supply chain. Computers & Industrial Engineering, 58(3), 373–381.Google Scholar
  68. Zhang, R., Song, S., & Wu, C. (2013). A hybrid artificial bee colony algorithm for the job shop scheduling problem. International Journal of Production Economics, 141(1), 167–178.Google Scholar
  69. Zhong, W., Chen, Z. L., & Chen, M. (2010). Integrated production and distribution scheduling with committed delivery dates. Operations Research Letters, 38(2), 133–138.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Transportation Engineering, Dalian Maritime UniversityDalianChina
  2. 2.School of Business AdministrationNortheastern UniversityShenyangChina
  3. 3.School of Maritime Economics and ManagementDalian Maritime UniversityDalianChina

Personalised recommendations