Abstract
The green capacitated vehicle routing problem (GCVRP) has attracted the attention of many researchers recently, due to the increasing global climate issues. This study presents an interactive fuzzy approach for solving green capacitated vehicle routing problem with imprecise travel time for each vehicle and supplier demands. Triangular fuzzy numbers are proposed for modeling uncertainty, and optimization problem is considered as a bi-objective possibilistic mixed-integer programming (PMIP) model. Possibilistic mixed-integer programming and a fuzzy analytical hierarchical process approach (FAHP) are combined to optimize two objective functions: (1) minimum total fuel consumption and (2) maximum total green score. In the first objective function, the fuel consumption ratio model is used. In this model, the fuel consumption is considered as function of travel time and total load of the vehicle. In the second objective function, suppliers are evaluated in terms of environmental factors with the fuzzy AHP method. The normalized weights are assigned to suppliers as a green score. A conciliating solution is obtained by solving this bi-objective mixed integer programming model. The proposed model and solution approach is applied for an automotive company in Turkey. According to the results obtained, a suggestion for a vehicle routing is proposed.







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Erdoğdu K, Karabulut K (2022) Bi-objective green vehicle routing problem. Int Trans Oper Res 29:1602–1626. https://doi.org/10.1111/itor.13044
McCollum D, Yang C (2009) Achieving deep reductions in US transport greenhouse gas emissions: scenario analysis and policy implications. Energy Policy 37(12):5580–5596. https://doi.org/10.1016/j.enpol.2009.08.038
U.S. EPA (U.S. Environmental Protection Agency) (2021) Inventory of U.S. greenhouse gas emissions and sinks: 1990–2019. www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks
Xiao Y, Zhao Q, Kaku I, Xu Y (2012) Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Comput Oper Res 39(7):1419–1431. https://doi.org/10.1016/j.cor.2011.08.013
Bektaş D, Bektaş T, Laporte G (2014) A review of recent research on green road freight transportation. Eur J Oper Res 237(3):775–793. https://doi.org/10.1016/j.ejor.2013.12.033
Erdoğan S, Miller-Hooks E (2012) A green vehicle routing problem. Trans Res Part E: Logist Trans Rev 48(1):100–114. https://doi.org/10.1016/j.tre.2011.08.001
Koç Ç, Karaoglan I (2016) The green vehicle routing problem: a heuristic based exact solution approach. Appl Soft Comput 39:154–164. https://doi.org/10.1016/j.asoc.2015.10.064
Poonthalir G, Nadarajan R (2018) A Fuel Efficient Green Vehicle Routing Problem with varying speed constraint (F-GVRP). Expert Systems with Applications 100(131–144) https://doi.org/10.1016/j.eswa.2018.01.052
Kazemian I, Aref S (2017) A green perspective on capacitated time-dependent vehicle routing problem with time windows. Int J Supply Chain Inventory Manag 2(1):20–38. https://doi.org/10.1504/IJSCIM.2017.10007322
Werners B, Kondratenko Y (2017) Alternative fuzzy approaches for efficiently solving the capacitated vehicle routing problem in conditions of uncertain demands. Complex Systems: Solutions and Challenges in Economics Management and Engineering. Splinger. pp. 521–543. https://doi.org/10.1007/978-3-319-69989-9_31
Wang R, Zhou J, Yi X et al (2019) Solving the green-fuzzy vehicle routing problem using a revised hybrid intelligent algorithm. J Ambient Intell Human Comput 10:321–332. https://doi.org/10.1007/s12652-018-0703-9
Gupta P, Govindan K, Kumar M, Khaitan M, Khaitan A (2021) Multiobjective capacitated green vehicle routing problem with fuzzy time distances and demands split into bags. Int J Prod Res. https://doi.org/10.1080/00207543.2021.1888392
Singh VP, Sharma K (2021) Capacitated vehicle routing problem with interval type-2 fuzzy demands. In Advances in Mechanical Engineering. Springer Singapore 83–89. https://doi.org/10.1007/978-981-15-3639-7_11
DincYalçın G, Erginel N (2022) An Adapted Fuzzy Multi-Objective Programming Algorithm for Vehicle Routing. Univ J Operations Manag 1(1):56–74. https://doi.org/10.37256/ujom.1120221144
Yang T, Wang W, Wu Q (2022) Fuzzy demand vehicle routing problem with soft time windows. Sustainability 14:56–58
Azarkish M, Aghaeipour Y (2022) A fuzzy bi-objective mathematical model for multi-depot electric vehicle location routing problem with time windows and simultaneous delivery and pick-up. Asian J Basic Sci Res 4(2):01–03. https://doi.org/10.38177/AJBSR.2022.4201
Eskandari MJ, Aliahmadi A, Khaleghi GHH (2010) A robust optimisation approach for the milk run problem with time windows with inventory uncertainty: an auto industry supply chain case study. Int J Rapid Manuf 1(2):334–347. https://doi.org/10.1504/IJRAPIDM.2010.034254
Özgen D, Önüt S, Gülsün B, Tuzkaya UF, Tuzkaya G (2008) A two-phase possibilistic linear programming methodology for multi-objective supplier evaluation and order allocation problems. Inf Sci 178(2):485–500. https://doi.org/10.1016/j.ins.2007.08.002
Wang RC, Liang TF (2005) Applying possibilistic linear programming to aggregate production planning. Int J Prod Econ 98(3):328–341. https://doi.org/10.1016/j.ijpe.2004.09.011
Torabi SA, Hassini E (2008) An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets Syst 159(2):193–214. https://doi.org/10.1016/j.fss.2007.08.010
Lai YJ, Hwang CL (1992) A new approach to some possibilistic linear programming problems. Fuzzy Sets Syst 49:121–133. https://doi.org/10.1016/0165-0114(92)90318-X
Zimmermann HJ (1978) Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst 1:45–55. https://doi.org/10.1016/0165-0114(78)90031-3
Amin SH, Zhang G (2013) An integrated model for closed-loop supply chain configuration and supplier selection: multi-objective approach. Expert Syst Appl 39(8):6782–6791. https://doi.org/10.1016/j.eswa.2011.12.056
Gupta S, Soni U, Kumar G (2019) Green supplier selection using multi-criterion decision making under fuzzy environment: a case study in automotive industry. Comput Ind Eng 136:663–680. https://doi.org/10.1016/j.cie.2019.07.038
Çalık A (2021) A novel Pythagorean fuzzy AHP and fuzzy TOPSIS methodology for green supplier selection in the Industry 4.0 era. Soft Comput 25:2253–2265. https://doi.org/10.1007/s00500-020-05294-9
Amindoust A, Ahmed S, Saghafinia A, Bahreininejad A (2012) Sustainable supplier selection: a ranking model based on fuzzy inference system. Appl Soft Comput 12(6):1668–1677. https://doi.org/10.1016/j.asoc.2012.01.023
Govindan K, Khodaverdi R, Jafarian A (2013) A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. J Clean Prod 47:345–354. https://doi.org/10.1016/j.jclepro.2012.04.014
Bai C, Sarkis J (2010) Integrating sustainability into supplier selection with grey system and rough set methodologies. Int J Prod Econ 124(1):252–264. https://doi.org/10.1016/j.ijpe.2009.11.023
Hashemi SH, Karimi A, Tavana M (2015) An integrated green supplier selection approach with analytic network process and improved grey relational analysis. Int J Prod Econ 159:178–191. https://doi.org/10.1016/j.ijpe.2014.09.027
Eren E, Tuzkaya UR (2021) Safe distance-based vehicle routing problem: Medical waste collection case study in COVID-19 pandemic. Comput Ind Eng. https://doi.org/10.1016/j.cie.2021.107328
Aydınalp Z, Özgen D (2022) Solving vehicle routing problem with time windows using meta heuristic approaches. Int J Intell Comput Cybern. https://doi.org/10.1108/IJICC-01-2022-0021
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All authors contributed to the study conception and design. The manuscript was written by Zeynep Aydinalp Birecik. Doğan Özgen supervised the study. All authors read and approved the final manuscript.
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Aydinalp Birecik, Z., Özgen, D. An interactive possibilistic programming approach for green capacitated vehicle routing problem. Neural Comput & Applic 35, 9253–9265 (2023). https://doi.org/10.1007/s00521-022-08180-7
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DOI: https://doi.org/10.1007/s00521-022-08180-7