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Hybrid Hyper-heuristic Algorithm for Integrated Production and Transportation Scheduling Problem in Distributed Permutation Flow Shop

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Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14086))

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Abstract

Researching the integrated production and transportation scheduling problem (IPTSP) is a great way to improve the overall benefits of companies, especially global ones, in the supply chain. In this study, we propose a novel hybrid hyper-heuristic algorithm (H_HHA) to solve a little-studied integrated distributed permutation flow-shop problem (DFSP) and multi-depot vehicle routing problem (IDFS_MDVRP) with the aim of minimizing the total delivery time. The H_HHA consists of a genetic algorithm (GA) and a hyper-heuristic algorithm (HHA). We employ five pre-designed heuristic operations in the low-level heuristics (LLHs) to improve local search performance, while the GA is used to improve the high-level heuristics (HLS) performance of the HHA. The interactions of the LLHs and HLS lead to the improved overall performance of the H_HHA. The simulation experiments and statistical results demonstrate the effectiveness of our proposed H_HHA in addressing the problem.

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References

  1. Chen, Z.: Integrated production and outbound distribution scheduling: review and extensions. Oper. Res. 58(1), 130–148 (2010)

    Article  MATH  Google Scholar 

  2. Moons, S., Ramaekers, K., Caris, A., Arda, Y.: Integrating production scheduling and vehicle routing decisions at the operational decision level: a review and discussion. Comput. Ind. Eng. 104, 224–245 (2017)

    Article  Google Scholar 

  3. He, P., Li, K., Kumar, P.N.R.: An enhanced branch-and-price algorithm for the integrated production and transportation scheduling problem. Int. J. Prod. Res. 60(6), 1874–1889 (2022)

    Article  Google Scholar 

  4. Reddy, N.S., Ramamurthy, D.V., Rao, K.P., Lalitha, M.P.: Practical simultaneous scheduling of machines, agvs, tool transporter and tools in a multi machine fms using symbiotic organisms search algorithm. Int. J. Comput. Integr. Manuf. 34(2), 153–174 (2021)

    Article  Google Scholar 

  5. Reddy, N.S., Ramamurthy, D.V., Lalitha, M.P., Rao, K.P.: Minimizing the total completion time on a multi-machine FMS using flower pollination algorithm. Soft. Comput. 26(3), 1437–1458 (2022)

    Article  Google Scholar 

  6. Li, W., Han, D., Gao, L., Li, X., Li, Y.: Integrated production and transportation scheduling method in hybrid flow shop. Chin. J. Mech. Eng. 35(1), 1–20 (2022)

    Article  Google Scholar 

  7. Meinecke, C., Scholz-Reiter, B.: A heuristic for the integrated production and distribution scheduling problem. World Acad. Sci. Eng. Technol. Int. J. Mech. Aerosp. Indus. Mech. Manufact. Eng. 8, 280–287 (2014)

    Google Scholar 

  8. Belo-Filho, M.A.F., Amorim, P., Almada-Lobo, B.: An adaptive large neighbourhood search for the operational integrated production and distribution problem of perishable products. Int. J. Prod. Res. 53(20), 6040–6058 (2015)

    Article  Google Scholar 

  9. Ramezanian, R., Mohammadi, S., Cheraghalikhani, A.: Toward an integrated modeling approach for production and delivery operations in flow shop system: trade-off between direct and routing delivery methods. J. Manuf. Syst. 44, 79–92 (2017)

    Article  Google Scholar 

  10. Liu, H., Guo, Z., Zhang, Z.: A hybrid multi-level optimisation framework for integrated production scheduling and vehicle routing with flexible departure time. Int. J. Prod. Res. 59(21), 6615–6632 (2021)

    Article  Google Scholar 

  11. Ghasemkhani, A., Tavakkoli-Moghaddam, R., Rahimi, Y., Shahnejat-Bushehri, S., Tavakkoli-Moghaddam, H.: Integrated production-inventory-routing problem for multi-perishable products under uncertainty by meta-heuristic algorithms. Int. J. Prod. Res. 60(9), 2766–2786 (2022)

    Article  Google Scholar 

  12. Kumar, R., Ganapathy, L., Gokhale, R., Tiwari, M.K.: Quantitative approaches for the integration of production and distribution planning in the supply chain: a systematic literature review. Int. J. Prod. Res. 58(11), 3527–3553 (2020)

    Article  Google Scholar 

  13. Naderi, B., Ruiz, R.: The distributed permutation flowshop scheduling problem. Comput. Oper. Res. 37(4), 754–768 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  14. Park, J., Mei, Y., Nguyen, S., Chen, G., Zhang, M.: An investigation of ensemble combination schemes for genetic programming based hyper-heuristic approaches to dynamic job shop scheduling. Appl. Soft Comput. 63, 72–86 (2018)

    Article  Google Scholar 

  15. Lin, J.: Backtracking search based hyper-heuristic for the flexible job-shop scheduling problem with fuzzy processing time. Eng. Appl. Artif. Intell. 77, 186–196 (2019)

    Article  Google Scholar 

  16. Song, H., Lin, J.: A genetic programming hyper-heuristic for the distributed assembly permutation flow-shop scheduling problem with sequence dependent setup times. Swarm Evol. Comput. 60, 100807 (2021)

    Article  MathSciNet  Google Scholar 

  17. Zhao, F., Di, S., Wang, L., Xu, T., Zhu, N., Jonrinaldi: A self-learning hyper-heuristic for the distributed assembly blocking flow shop scheduling problem with total flowtime criterion. Eng. Appl. Artif. Intell. 116, 105418 (2022)

    Google Scholar 

  18. Li, Z.C., Qian, B., Hu, R., Chang, L.L., Yang, J.B.: An elitist nondominated sorting hybrid algorithm for multi-objective flexible job-shop scheduling problem with sequence-dependent setups. Knowl.-Based Syst. 173, 83–112 (2019)

    Article  Google Scholar 

  19. Multiple Depot VRP Instances Vehicle Routing Problem. http://neo.lcc.uma.es/vrp/vrp-instances/multiple-depot-vrp-instances/. Accessed 13 June 2022

  20. Nawaz, M., Enscore, E.E., Ham, I.: A heuristic algorithm for the M-machine, N-job flow-shop sequencing problem. Omega 11(1), 91–95 (1983)

    Article  Google Scholar 

  21. Feng, X., Xu, Z.: Integrated production and transportation scheduling on parallel batch-processing machines. IEEE Access 7, 148393–148400 (2019)

    Article  Google Scholar 

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Acknowledgments

This study was partially supported by the National Natural Science Foundation of China (62173169, 61963022), and the Basic Research Key Project of Yunnan Province (202201AS070030).

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Authors and Affiliations

  1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

    Wenbo Chen, Bin Qian, Rong Hu, Sen Zhang & Yijun Wang

Authors
  1. Wenbo Chen
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  2. Bin Qian
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  3. Rong Hu
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  4. Sen Zhang
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  5. Yijun Wang
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Corresponding author

Correspondence to Bin Qian .

Editor information

Editors and Affiliations

  1. Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Chen, W., Qian, B., Hu, R., Zhang, S., Wang, Y. (2023). Hybrid Hyper-heuristic Algorithm for Integrated Production and Transportation Scheduling Problem in Distributed Permutation Flow Shop. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14086. Springer, Singapore. https://doi.org/10.1007/978-981-99-4755-3_8

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  • DOI: https://doi.org/10.1007/978-981-99-4755-3_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4754-6

  • Online ISBN: 978-981-99-4755-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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