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2000
Volume 3, Issue 1
  • ISSN: 2950-3779
  • E-ISSN: 2950-3787

Abstract

Introduction

The research focuses on cost optimization in the traditional vehicle routing optimization problem. However, in the actual optimization scenarios, such as port industrial parks, decision-makers often focus on service time and customer demands under uncertain environments.

Methods

Meanwhile, increasing environmental sustainability and effective resource utilization further drive the focus on eco-friendly transportation. In this context, this study refines a hazmat vehicle routing problem with fuzzy time windows under fuzzy demands for the port industrial park. Moreover, to address the above problems, the main contributions of this study are as follows. Firstly, inspired by the sharing economy theory, a novel mode is proposed through intermediate bulk container sharing as a transportation medium in the port industrial park. Secondly, a bi-objective fuzzy chance-constrained programming model is proposed for transportation cost and service satisfaction, considering fuzzy demands and fuzzy time windows.

Results

Thirdly, the non-dominated sorting genetic algorithm II is used to solve the proposed problem. Additionally, the effectiveness of the model is validated using the Solomon benchmark with different scales, where transportation costs are 11545, 28611, and 61192, and average satisfaction is 0.885, 0.880, and 0.928.

Conclusion

The results indicate that the algorithm reduces transportation costs and enhances service satisfaction. The study enriches the relevant research on vehicle routing problems and provides a theoretical basis for practical distribution strategies.

© 2024 Bentham Science Publishers
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2024-12-24
2026-02-21

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References

  1. KhattakA.J. RedepenningH.H. HaqueM.S. Risk assessment of hazardous materials transportation for small and tribal communities in Nebraska.Transp. Res. Rec.20242678429530710.1177/03611981231184195
     [Google Scholar]
  2. RahmanifarG. MohammadiM. SherafatA. Hajiaghaei-KeshteliM. FuscoG. ColombaroniC. Heuristic approaches to address vehicle routing problem in the Iot-based waste management system.Expert Syst. Appl.202322011970810.1016/j.eswa.2023.119708
     [Google Scholar]
  3. FanL. LiuC. DaiB. LiJ. WuZ. GuoY. Electric vehicle routing problem considering energy differences of charging stations.J. Clean. Prod.202341813818410.1016/j.jclepro.2023.138184
     [Google Scholar]
  4. SterzikS. KopferH. YunW-Y. Reducing hinterland transportation costs through container sharing.Flex. Serv. Manuf. J.2015272-338240210.1007/s10696‑012‑9167‑y
     [Google Scholar]
  5. HuangD. ZhaoG. A shared container transportation mode in the yangtze river.Sustainability (Basel)20191110288610.3390/su11102886
     [Google Scholar]
  6. TangY. ChenS. FengY. ZhuX. Optimization of multi- period empty container repositioning and renting in China Railway Express based on container sharing strategy.Eur. Trans. Res. Rev.20211314210.1186/s12544‑021‑00498‑y
     [Google Scholar]
  7. DiaoX. FanH. RenX. LiuC. Multi-depot open vehicle routing problem with fuzzy time windows.J. Intell. Fuzzy Syst.202140142743810.3233/JIFS‑191968
     [Google Scholar]
  8. SyafrizalW. SugihartiE. Electric vehicle routing problem with fuzzy time windows using genetic algorithm and tabu search.J. Adv. Info. Sys. Technol.20234220522110.15294/jaist.v4i2.62314
     [Google Scholar]
  9. QamsariA.S.N. Hosseini-MotlaghS.M. GhannadpourS.F. A column generation approach for an inventory routing problem with fuzzy time windows.Oper. Res.20222221157120710.1007/s12351‑020‑00593‑3
     [Google Scholar]
  10. YangT. WangW. WuQ. Fuzzy demand vehicle routing problem with soft time windows.Sustainability (Basel)2022149565810.3390/su14095658
     [Google Scholar]
  11. FanH. RenX. ZhangY. ZhenZ. FanH. A chaotic genetic algorithm with variable neighborhood search for solving time-dependent green VRPTW with fuzzy demand.Symmetry (Basel)20221410211510.3390/sym14102115
     [Google Scholar]
  12. YuV.F. RediA.A.N.P. HidayatY.A. WibowoO.J. A simulated annealing heuristic for the hybrid vehicle routing problem.Appl. Soft Comput.20175311913210.1016/j.asoc.2016.12.027
     [Google Scholar]
  13. BulaG.A. Murat AfsarH. GonzálezF.A. ProdhonC. VelascoN. Bi-objective vehicle routing problem for hazardous materials transportation.J. Clean. Prod.201920697698610.1016/j.jclepro.2018.09.228
     [Google Scholar]
  14. JiangP. MenJ. XuH. ZhengS. KongY. ZhangL. A variable neighborhood search-based hybrid multiobjective evolutionary algorithm for HazMat heterogeneous vehicle routing problem with time windows.IEEE Syst. J.20201434344435510.1109/JSYST.2020.2966788
     [Google Scholar]
  15. SubramanyamA. RepoussisP.P. GounarisC.E. Robust optimization of a broad class of heterogeneous vehicle routing problems under demand uncertainty.INFORMS J. Comput.202032366168110.1287/ijoc.2019.0923
     [Google Scholar]
  16. HoleczekN. Analysis of different risk models for the hazardous materials vehicle routing problem in urban areas.Cleaner Environmen. Sys.2021210002210.1016/j.cesys.2021.100022
     [Google Scholar]
  17. ZhangQ. WangS. ZhenL. Yard truck retrofitting and deployment for hazardous material transportation in green ports.Ann. Oper. Res.2022•••13210.1007/s10479‑021‑04507‑0
     [Google Scholar]
  18. JafariM.J. EbrahimnejadS. RahbariM. MohamadiA. Time-dependent location-routing problem for hazmat transportation with stop en route: A case study for fossil fuels distribution.Int. J. Shipp. Transp. Logist.2023161/2549510.1504/IJSTL.2023.128550
     [Google Scholar]
  19. SrivastavaG. SinghA. MallipeddiR. NSGA-II with objective-specific variation operators for multiobjective vehicle routing problem with time windows.Expert Syst. Appl.202117611477910.1016/j.eswa.2021.114779
     [Google Scholar]
  20. BarmaP.S. DuttaJ. MukherjeeA. KarS. A bi-objective latency based vehicle routing problem using hybrid GRASP-NSGAII algorithm.Int. J. Manag. Sci. Eng. Manag.202318319020710.1080/17509653.2022.2076168
     [Google Scholar]
  21. YinN. Multiobjective optimization for vehicle routing optimization problem in low-carbon intelligent transportation.IEEE Trans. Intell. Transp. Syst.20232411131611317010.1109/TITS.2022.3193679
     [Google Scholar]
  22. AntkiewiczM. MyszkowskiP.B. Balancing Pareto Front exploration of Non-dominated Tournament Genetic Algorithm (B-NTGA) in solving multi-objective NP-hard problems with constraints.Inf. Sci.202466712040010.1016/j.ins.2024.120400
     [Google Scholar]
  23. MaH.L. WongC.W.H. LeungL.C. ChungS.H. Facility sharing in business-to-business model: A real case study for container terminal operators in Hong Kong port.Int. J. Prod. Econ.202022110748310.1016/j.ijpe.2019.09.004
     [Google Scholar]
  24. YuenK.F. OngK.W. ZhouY. WangX. Social media engagement of stakeholders in the oil and gas sector: Social presence, triple bottom line and source credibility theory.J. Clean. Prod.202338213537510.1016/j.jclepro.2022.135375
     [Google Scholar]
  25. JalotaH. MandalP.K. ThakurM. MittalG. A novel approach to incorporate investor’s preference in fuzzy multi-objective portfolio selection problem using credibility measure.Expert Syst. Appl.202321211858310.1016/j.eswa.2022.118583
     [Google Scholar]
  26. ZhengW. DoerrB. Runtime analysis for the NSGA-II: Proving, quantifying, and explaining the inefficiency for many objectives.IEEE Trans. Evol. Comput.20232851442145410.1109/TEVC.2023.3320278
     [Google Scholar]
  27. ZhangZ. ChengX. XingZ. GuiX. Pareto multi-objective optimization of metro train energy-saving operation using improved NSGA-II algorithms.Chaos Solitons Fractals202317611418310.1016/j.chaos.2023.114183
     [Google Scholar]
  28. SharmaK. SinghV.P. EbrahimnejadA. ChakrabortyD. Solving a multi-objective chance constrained hierarchical optimization problem under intuitionistic fuzzy environment with its application.Expert Syst. Appl.202321711959510.1016/j.eswa.2023.119595
     [Google Scholar]
  29. ZhangM. WangN. HeZ. JiangB. Vehicle routing optimization for hazmat shipments considering catastrophe avoidance and failed edges.Transp. Res., Part E Logist. Trans. Rev.202115010233710.1016/j.tre.2021.102337
     [Google Scholar]
  30. KamsopaK. SethananK. JamrusT. CzwajdaL. Hybrid genetic algorithm for multi-period vehicle routing problem with mixed pickup and delivery with time window, heterogeneous fleet, duration time and rest area.Eng. J. (N.Y.)20212510718610.4186/ej.2021.25.10.71
     [Google Scholar]
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A Novel Optimization Method for Vehicle Routing Problem with Fuzzy Time Windows through Intermediate Bulk Container Sharing under Fuzzy Demands Environment
Curr. Comp. Sci. 3, e29503779353744 (2024); https://doi.org/10.2174/0129503779353744241209110530
/content/journals/cucs/10.2174/0129503779353744241209110530
/content/journals/cucs/10.2174/0129503779353744241209110530
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  • Article Type:     Research Article
Keyword(s): fuzzy demands; fuzzy time windows; intermediate bulk container sharing; non-dominated sorting genetic algorithm II; port industrial park; Vehicle routing problem
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