Open Access
| Issue |
RAIRO-Oper. Res.
Volume 59, Number 5, September-October 2025
|
|
|---|---|---|
| Page(s) | 2657 - 2681 | |
| DOI | https://doi.org/10.1051/ro/2025050 | |
| Published online | 12 September 2025 | |
- J. Lohmer and R. Lasch, Production planning and scheduling in multi-factory production networks: a systematic literature review. Int. J. Prod. Res. 59 (2021) 2028–2054. [Google Scholar]
- S.S. Moghadam, A. Aghsami and M. Rabbani, A hybrid NSGA-II algorithm for the closed-loop supply chain network design in e-commerce. RAIRO-Oper. Res. 55 (2021) 1643–1674. [Google Scholar]
- X. Zhao, L. Zheng, Y. Wang and Y. Zhang, Services-oriented intelligent milling for thin-walled parts based on time-varying information model of machining system. Int. J. Mech. Sci. 219 (2022) 107125. [Google Scholar]
- A.-H.H. Bacar and S.C. Rawhoudine, An attractors-based particle swarm optimization for multiobjective capacitated vehicle routing problem. RAIRO-Oper. Res. 55 (2021) 2599–2614. [Google Scholar]
- B. Zhang, G. Wang, Y. Yang and S. Zhang, Solving the order planning problem at the steelmaking shops by considering logistics balance on the plant-wide process. IEEE Access 7 (2019) 139938–139956. [Google Scholar]
- U. Koç, A. Toptal and I. Sabuncuoglu, Coordination of inbound and outbound transportation schedules with the production schedule. Comput. Ind. Eng. 103 (2017) 178–192. [Google Scholar]
- N. Bagheri Rad and J. Behnamian, Recent trends in distributed production network scheduling problem. Artif. Intell. Rev. 55 (2022) 2945–2995. [Google Scholar]
- A. Gharaei and F. Jolai, A pareto approach for the multi-factory supply chain scheduling and distribution problem. Oper. Res. 21 (2021) 2333–2364. [Google Scholar]
- J. Behnamian and S.M. Taghi Fatemi Ghomi, Multi-objective multi-factory scheduling. RAIRO-Oper. Res. 55 (2021) S1447–S1467. [Google Scholar]
- L. Chen, J. Wang and W. Yang, A single machine scheduling problem with machine availability constraints and preventive maintenance. Int. J. Prod. Res. 59 (2021) 2708–2721. [Google Scholar]
- S. Wang, R. Wu, F. Chu and J. Yu, An exact decomposition method for unrelated parallel machine scheduling with order acceptance and setup times. Comput. Ind. Eng. 175 (2023) 108899. [Google Scholar]
- B. Liao, S. Lu, T. Jiang and X. Zhu, A variable neighborhood search and mixed-integer programming models for a distributed maintenance service network scheduling problem. Int. J. Prod. Res. 62 (2024) 7466–7485. [Google Scholar]
- L. Cai, W. Li, Y. Luo and L. He, Real-time scheduling simulation optimisation of job shop in a production-logistics collaborative environment. Int. J. Prod. Res. 61 (2023) 1373–1393. [Google Scholar]
- A. Boudjemline, I.A. Chaudhry, A.F. Rafique, I.A.Q. Elbadawi, M. Aichouni and M. Boujelbene, Multi-objective flexible job shop scheduling using genetic algorithms. Tehnički vjesnik 29 (2022) 1706–1713. [Google Scholar]
- Y. Lee, J.M. Pinto and L.G. Papageorgiou, Optimisation frameworks for integrated planning with allocation of transportation resources for industrial gas supply chains. Comput. Chem. Eng. 164 (2022) 107897. [CrossRef] [Google Scholar]
- Y. Yan, X. Di and Y. Zhang, Optimization-driven distribution of relief materials in emergency disasters. Complex Intell. Syst. (2021) 1–8. [Google Scholar]
- W. Tan, X. Yuan, J. Wang, H. Xu and L. Wu, Multi-objective teaching–learning-based optimization algorithm for carbon-efficient integrated scheduling of distributed production and distribution considering shared transportation resource. J. Clean. Prod. 406 (2023) 137061. [Google Scholar]
- Y. Zhao, Q. Deng, L. Zhang, W. Han and F. Li, Optimal spare parts production–distribution scheduling considering operational utility on customer equipment. Expert Syst. App. 214 (2023) 119204. [Google Scholar]
- E. Gheisariha, F. Etebari, B. Vahdani and R. Tavakkoli-Moghaddam, A holistic, integrated supply-production–distribution problem in the dairy industry under uncertain supply and demand. Comput. Ind. Eng. 181 (2023) 109296. [Google Scholar]
- Y. Dan and G. Liu, Integrated scheduling optimization of production and transportation for precast component with delivery time window. Eng. Constr. Arch. Manage. 31 (2024) 3335–3355. [Google Scholar]
- X.T. Sun, S.H. Chung and F.T.S. Chan, Integrated scheduling of a multi-product multi-factory manufacturing system with maritime transport limits. Transp. Res. Part E: Logistics Transp. Rev. 79 (2015) 110–127. [Google Scholar]
- X.T. Sun, S.H. Chung, F.T.S. Chan and Z. Wang, The impact of liner shipping unreliability on the production–distribution scheduling of a decentralized manufacturing system. Transp. Res. Part E: Logistics Transp. Rev. 114 (2018) 242–269. [Google Scholar]
- C.M. Joo and B.S. Kim, Rule-based meta-heuristics for integrated scheduling of unrelated parallel machines, batches and heterogeneous delivery trucks. Appl. Soft Comput. 53 (2017) 457–476. [Google Scholar]
- L. Tang, Z. Jin, X. Qin and K. Jing, Supply chain scheduling in a collaborative manufacturing mode: model construction and algorithm design. Ann. Oper. Res. 275 (2019) 685–714. [Google Scholar]
- W. Wang, S. Wang and J. Su, Integrated production and transportation scheduling in e-commerce supply chain with carbon emission constraints. J. Theor. Appl. Electron. Commer. Res. 16 (2021) 2554–2570. [Google Scholar]
- İ. Karaoğlan and S.E. Kesen, The coordinated production and transportation scheduling problem with a time-sensitive product: a branch-and-cut algorithm. Int. J. Prod. Res. 55 (2017) 536–557. [Google Scholar]
- H. Mokhtari and A. Hasani, A multi-objective model for cleaner production-transportation planning in manufacturing plants via fuzzy goal programming. J. Manuf. Syst. 44 (2017) 230–242. [Google Scholar]
- F. Marandi and S.M.T. Fatemi Ghomi, Integrated multi-factory production and distribution scheduling applying vehicle routing approach. Int. J. Prod. Res. 57 (2019) 722–748. [Google Scholar]
- H. Pang, Modeling and optimizing of distributed multi-factory production and transportation coordinative scheduling problem, in 2019 Chinese Control And Decision Conference (CCDC). IEEE (2019) 3848–3853. [Google Scholar]
- J. Yang, F. Guo, L. Luo and X. Ye, Bilevel mixed-integer nonlinear programming for integrated scheduling in a supply chain network. Cluster Comput. 22 (2019) 15517–15532. [Google Scholar]
- I.S. Lee, A coordinated scheduling of production-and-delivery under dynamic delivery cost environments. Comput. Ind. Eng. 81 (2015) 22–35. [Google Scholar]
- G. Dwivedi, S. Chakraborty, Y.K. Agarwal and R.K. Srivastava, Simultaneous production and transportation problem: a case of additive manufacturing. Transp. Sci. 57 (2023) 741–755. [Google Scholar]
- T. Azad, H.F. Rahman, R.K. Chakrabortty and M.J. Ryan, Optimization of integrated production scheduling and vehicle routing problem with batch delivery to multiple customers in supply chain. Memetic Comput. 14 (2022) 355–376. [Google Scholar]
- X. Zou, L. Liu, K. Li and W. Li, A coordinated algorithm for integrated production scheduling and vehicle routing problem. Int. J. Prod. Res. 56 (2018) 5005–5024. [Google Scholar]
- P. Feng, Y. Liu, F. Wu and C. Chu, Two heuristics for coordinating production planning and transportation planning. Int. J. Prod. Res. 56 (2018) 6872–6889. [Google Scholar]
- F. Marandi and S.M.T. Fatemi Ghomi, Network configuration multi-factory scheduling with batch delivery: a learning-oriented simulated annealing approach. Comput. Ind. Eng. 132 (2019) 293–310. [Google Scholar]
- B. Zhou, R. Zhou, Y. Gan, F. Fang and Y. Mao, Multi-robot multi-station cooperative spot welding task allocation based on stepwise optimization: an industrial case study. Rob. Comput.-Integr. Manuf. 73 (2022) 102197. [Google Scholar]
- S. Aminzadegan, M. Tamannaei and M. Rasti-Barzoki, Multi-agent supply chain scheduling problem by considering resource allocation and transportation. Comput. Ind. Eng. 137 (2019) 106003. [Google Scholar]
- Z. Fan, S. Li and Z. Gao, Multiobjective sustainable order allocation problem optimization with improved genetic algorithm using priority encoding. Math. Prob. Eng. 2019 (2019) 8218709. [Google Scholar]
- Z. Sadeghian, E. Akbari, H. Nematzadeh and H. Motameni, A review of feature selection methods based on meta-heuristic algorithms. J. Exper. Theor. Artif. Intell. 37 (2025) 1–51. [Google Scholar]
- K. Guo, M. Yang and H. Zhu, Application research of improved genetic algorithm based on machine learning in production scheduling. Neural Comput. App. 32 (2020) 1857–1868. [Google Scholar]
- Z. Liang, M. Liu, P. Zhong and C. Zhang, Application research of a new neighbourhood structure with adaptive genetic algorithm for job shop scheduling problem. Int. J. Prod. Res. 61 (2023) 362–381. [Google Scholar]
- J. Zhu, Solving capacitated vehicle routing problem by an improved genetic algorithm with fuzzy c-means clustering. Sci. Program. 2022 (2022) 8514660. [Google Scholar]
- M.B. Gawali and S.K. Shinde, Task scheduling and resource allocation in cloud computing using a heuristic approach. J. Cloud Comput. 7 (2018) 1–16. [Google Scholar]
- Y. Tian, Case study data. Mendeley Data. V1. DOI: 10.17632/23bdfd928f.1 (2023). [Google Scholar]
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