A DECISION MODEL FOR PRODUCTION LOGISTICS PLANNING CONSIDERING ELECTRIC VEHICLE USAGE
DOI:
https://doi.org/10.11113/jm.v49.747Keywords:
Logistics Production, Inventory Routing Problem, Electric Vehicle, OptimizationAbstract
Logistics in production involves the movement of materials/parts to the workstation to ensure an efficient and effective production process. This study is motivated by the practices of production logistics within a manufacturing company and aims to minimize delays and total costs in transferring materials/parts to the workstation by utilizing electric vehicles (EV). The problem is formulated as an EV multi-period routing problem (EV-MPVRP), taking into account factors such as vehicle capacity, limited operation time, restricted distance, and charging time for EV. The results demonstrate that the proposed method effectively reduces delays and costs associated with the movement of materials/parts. Additionally, incorporating inventory into the production plan can lead to a decrease in both travel distance and charging time.
References
P. Nyhuis and H.-P. Wiendahl, Fundamentals of Production Logistics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. doi: 10.1007/978-3-540-34211-3.
E. Michlowicz, ‘Logistics in Production Processes’, Journal of Machine Engineering, vol. 13, no. 4, pp. 5–17, 2013.
E. Flores-García, D. Hoon Kwak, Y. Jeong, and M. Wiktorsson, ‘Machine learning in smart production logistics: a review of technological capabilities’, Int J Prod Res, vol. 63, no. 5, pp. 1898–1932, Mar. 2025, doi: 10.1080/00207543.2024.2381145.
M. Li and G. Q. Huang, ‘Production-intralogistics synchronization of industry 4.0 flexible assembly lines under graduation intelligent manufacturing system’, Int J Prod Econ, vol. 241, p. 108272, Nov. 2021, doi: 10.1016/j.ijpe.2021.108272.
M. Thürer, S. S. Li, and T. Qu, ‘Digital Twin Architecture for Production Logistics: The Critical Role of Programmable Logic Controllers (PLCs)’, Procedia Comput Sci, vol. 200, pp. 710–717, 2022, doi: 10.1016/j.procs.2022.01.269.
K. Bänsch et al., ‘Energy-aware decision support models in production environments: A systematic literature review’, Comput Ind Eng, vol. 159, p. 107456, Sep. 2021, doi: 10.1016/j.cie.2021.107456.
Y. Zhu et al., ‘Production logistics digital twins: Research profiling, application, challenges and opportunities’, Robot Comput Integr Manuf, vol. 84, p. 102592, Dec. 2023, doi: 10.1016/j.rcim.2023.102592.
B.-H. Zhou and C.-Y. Shen, ‘Multi-objective optimization of material delivery for mixed model assembly lines with energy consideration’, J Clean Prod, vol. 192, pp. 293–305, Aug. 2018, doi: 10.1016/j.jclepro.2018.04.251.
X. Zheng, F. Gao, and X. Tong, ‘Research on Green Vehicle Path Planning of AGVs with Simultaneous Pickup and Delivery in Intelligent Workshop’, Symmetry (Basel), vol. 15, no. 8, p. 1505, Jul. 2023, doi: 10.3390/sym15081505.
K. Biel and C. H. Glock, ‘Systematic literature review of decision support models for energy-efficient production planning’, Comput Ind Eng, vol. 101, pp. 243–259, Nov. 2016, doi: 10.1016/j.cie.2016.08.021.
B. Zhou and Z. Zhao, ‘A hybrid fuzzy-neural-based dynamic scheduling method for part feeding of mixed-model assembly lines’, Comput Ind Eng, vol. 163, p. 107794, Jan. 2022, doi: 10.1016/j.cie.2021.107794.
S. Xing, Z. Shao, W. Shao, J. Chen, and D. Pi, ‘Joint scheduling of hybrid flow-shop with limited automatic guided vehicles: A hierarchical learning-based swarm optimizer’, Comput Ind Eng, vol. 198, p. 110686, Dec. 2024, doi: 10.1016/j.cie.2024.110686.
G. Gündüz Mengübaş, K. Sörensen, and M. Kotan, ‘Tow train routing in narrow aisles: A grid-based approach with blocking constraints’, Eng Appl Artif Intell, vol. 141, p. 109839, Feb. 2025, doi: 10.1016/j.engappai.2024.109839.
S. Lu, Y. Hu, and S. Qu, ‘Joint optimization of tow-trains dispatch and conflict-free route planning in mixed-model assembly lines’, Procedia CIRP, vol. 97, pp. 253–259, 2021, doi: 10.1016/j.procir.2020.05.234.
S. Emde and M. Gendreau, ‘Scheduling in-house transport vehicles to feed parts to automotive assembly lines’, Eur J Oper Res, vol. 260, no. 1, pp. 255–267, Jul. 2017, doi: 10.1016/j.ejor.2016.12.012.
B. Xia, M. Zhang, Y. Gao, J. Yang, and Y. Peng, ‘Design for Optimally Routing and Scheduling a Tow Train for Just-in-Time Material Supply of Mixed-Model Assembly Lines’, Sustainability, vol. 15, no. 19, p. 14567, Oct. 2023, doi: 10.3390/su151914567.
H. Diefenbach, S. Emde, and C. H. Glock, ‘Multi-depot electric vehicle scheduling in in-plant production logistics considering non-linear charging models’, Eur J Oper Res, vol. 306, no. 2, pp. 828–848, Apr. 2023, doi: 10.1016/j.ejor.2022.06.050.
V. F. Mourgaya M., ‘The periodic Vehicle routing problem: classification and heuristic’, RAIRO - Operations Research, vol. 40, no. 2, pp. 169–194, Oct. 2006, [Online]. Available: http://eudml.org/doc/249770
M. Mourgaya and F. Vanderbeck, ‘Column generation based heuristic for tactical planning in multi-period vehicle routing’, Eur J Oper Res, vol. 183, no. 3, pp. 1028–1041, Dec. 2007, doi: 10.1016/j.ejor.2006.02.030.
S. M. J. Mirzapour Al-e-hashem and Y. Rekik, ‘Multi-product multi-period Inventory Routing Problem with a transshipment option: A green approach’, Int J Prod Econ, vol. 157, pp. 80–88, Nov. 2014, doi: 10.1016/j.ijpe.2013.09.005.
V. Cacchiani, V. C. Hemmelmayr, and F. Tricoire, ‘A set-covering based heuristic algorithm for the periodic vehicle routing problem’, Discrete Appl Math (1979), vol. 163, pp. 53–64, Jan. 2014, doi: 10.1016/j.dam.2012.08.032.
J. Schönberger, ‘Multi-Period Vehicle Routing with Limited Period Load’, IFAC-PapersOnLine, vol. 49, no. 2, pp. 24–29, 2016, doi: 10.1016/j.ifacol.2016.03.005.
D. G. Mogale, A. Dolgui, R. Kandhway, S. K. Kumar, and M. K. Tiwari, ‘A multi-period inventory transportation model for tactical planning of food grain supply chain’, Comput Ind Eng, vol. 110, pp. 379–394, Aug. 2017, doi: 10.1016/j.cie.2017.06.008.
B. Messaoudi, A. Oulamara, and N. Rahmani, ‘Multiple Periods Vehicle Routing Problems: A Case Study’, 2019, pp. 83–98. doi: 10.1007/978-3-030-16711-0_6.
M. Mahjoob, S. S. Fazeli, S. Milanlouei, L. S. Tavassoli, and M. Mirmozaffari, ‘A modified adaptive genetic algorithm for multi-product multi-period inventory routing problem’, Sustainable Operations and Computers, vol. 3, pp. 1–9, 2022, doi: 10.1016/j.susoc.2021.08.002.
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