A New Hybrid Whale Optimization Algorithm for Green Vehicle Routing Problem

References

• Abdel-Basset, M., Manogaran, G., El-Shahat, D., Mirjalili, S., (2018). A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem. Future Generation Computer Systems, 85, 129–145. https://doi.org/https://doi.org/10.1016/j.future.2018.03.020
   Web of Science ®Google Scholar
• Abdoli, B., MirHassani, S. A., & Hooshmand, F. (2017). Model and algorithm for bi-fuel vehicle routing problem to reduce GHG emissions. Environmental Science and Pollution Research, 24(27), 21610–21624. https://doi.org/https://doi.org/10.1007/s11356-017-9740-8
   PubMed Web of Science ®Google Scholar
• Afshar-Bakeshloo, M., et al. (2016). A green vehicle routing problem with customer satisfaction criteria. Journal of Industrial Engineering International, 12(4), 529–544. https://doi.org/https://doi.org/10.1007/s40092-016-0163-9
   Google Scholar
• Ali, I. M., Essam, D., & Kasmarik, K. (2019). A novel differential evolution mapping technique for generic combinatorial optimization problems. Applied Soft Computing, 80, 297–309. https://doi.org/https://doi.org/10.1016/j.asoc.2019.04.017
   Web of Science ®Google Scholar
• Baldacci, R., Hadjiconstantinou, E., & Mingozzi, A. (2004). An exact algorithm for the Capacitated vehicle Routing problem based on a Two-Commodity Network Flow Formulation. Operations Research, 52(5), 723–738. https://doi.org/https://doi.org/10.1287/opre.1040.0111
   Web of Science ®Google Scholar
• Bruglieri, M., Mancini, S., & Pisacane, O. (2019). More efficient formulations and valid inequalities for the Green vehicle Routing problem. Transportation Research Part C: Emerging Technologies, 105, 283–296. https://doi.org/https://doi.org/10.1016/j.trc.2019.05.040
   Web of Science ®Google Scholar
• Christofides, N., & Eilon, S. (1969). An algorithm for the vehicle-dispatching problem. Journal of the Operational Research Society, 20(3), 309–318. https://doi.org/https://doi.org/10.1057/jors.1969.75
   Google Scholar
• Cinar, D., Gakis, K., & Pardalos, P. M. (2015). Reduction of CO 2 emissions in cumulative multi-trip vehicle routing problems with limited duration. Environmental Modeling & Assessment, 20(4), 273–284. https://doi.org/https://doi.org/10.1007/s10666-014-9434-2
   Web of Science ®Google Scholar
• Dantzig, G. B., & Ramser, J. H. (1959). The Truck Dispatching problem. Management Science, 6(1), 80–91. https://doi.org/https://doi.org/10.1287/mnsc.6.1.80
   Web of Science ®Google Scholar
• de Oliveira da Costa, P. R., et al. (2018). A Genetic algorithm for a Green Vehicle Routing problem. Electronic Notes in Discrete Mathematics, 64, 65–74. https://doi.org/https://doi.org/10.1016/j.endm.2018.01.008
   Google Scholar
• Eguia, I., et al. (2013). Environmental issues in vehicle routing problems. Sustainability Appraisal: Quantitative Methods and Mathematical Techniques for Environmental Performance Evaluation. Springer, 215–241. https://doi.org/https://doi.org/10.1007/978-3-642-32081-1_10
   Google Scholar
• Ene, S., et al. (2016). A hybrid metaheuristic algorithm for the green vehicle routing problem with a heterogeneous fleet. International Journal of Vehicle Design, 71(1–4), 75–102. https://doi.org/https://doi.org/10.1504/IJVD.2016.078771
   Web of Science ®Google Scholar
• Erdoğan, S., & Miller-Hooks, E. (2012). A Green Vehicle Routing problem. Transportation Research Part E: Logistics and Transportation Review, 48(1), 100–114. https://doi.org/https://doi.org/10.1016/j.tre.2011.08.001
   Web of Science ®Google Scholar
• Eydi, A., & Alavi, H. (2018). Vehicle Routing problem in Reverse Logistics with Split Demands of customers and fuel consumption Optimization. Arabian Journal for Science and Engineering, 44(3), 1–11. https://doi.org/https://doi.org/10.1007/s13369-018-3311-2
   Web of Science ®Google Scholar
• Felipe, Á, et al. (2014). A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transportation Research Part E: Logistics Transportation Review, 71, 111–128. https://doi.org/https://doi.org/10.1016/j.tre.2014.09.003
   Web of Science ®Google Scholar
• Franco, C., López-Santana, E. R., & Figueroa-García, J. C. (2017). Solving the Interval Green Inventory Routing Problem Using Optimization and Genetic Algorithms. ed. Workshop on Engineering Applications, 556–564.
   Google Scholar
• Garside, A. K., Sulistyani, X., & Utama, D. M. (2016). Penentuan Rute Distribusi LPG dengan Pendekatan Model Matematis. ed. Prosiding SENTRA (Seminar Teknologi dan Rekayasa), 12–18.
   Google Scholar
• Gaskell, T. J. (1967). Bases for vehicle fleet scheduling. Journal of the Operational Research Society, 18(3), 281–295. https://doi.org/https://doi.org/10.1057/jors.1967.44
   Google Scholar
• Ibrahim, M. F., Putri, M. M., & Utama, D. M. (2020). A literature review on reducing carbon emission from supply chain system: Drivers, barriers, performance indicators, and practices. IOP Conference Series: Materials Science and Engineering, 722, 012034. https://doi.org/https://doi.org/10.1088/1757-899X/722/1/012034
   Google Scholar
• Jabali, O., et al. (2012). Analysis of travel times and CO2 emissions in time-dependent vehicle routing. Production and Operations Management, 21(6), 1060–1074. https://doi.org/https://doi.org/10.1111/j.1937-5956.2012.01338.x
   Web of Science ®Google Scholar
• Jabir, E., Panicker, V. V., & Sridharan, R. (2017). Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing problem. Transportation Research Part D: Transport and Environment, 57, 422–457. https://doi.org/https://doi.org/10.1016/j.trd.2017.09.003
   Web of Science ®Google Scholar
• Karagul, K., et al. (2019). A Simulated Annealing Algorithm Based Solution Method for a Green Vehicle Routing problem with fuel consumption. In T. Paksoy, G.-W. Weber, & S. Huber (Eds.), Lean and Green Supply Chain Management: Optimization Models and algorithms (pp. 161–187). Springer International Publishing.
   Google Scholar
• Khalilpourazari, S., Pasandideh, S. H. R., & Ghodratnama, A. (2019). Robust possibilistic programming for multi-item EOQ model with defective supply batches: Whale Optimization and Water Cycle algorithms. Neural Computing and Applications, 31(10), 6587–6614. https://doi.org/https://doi.org/10.1007/s00521-018-3492-3
   Web of Science ®Google Scholar
• Kirci, P. (2016). On the performance of Tabu search algorithm for the vehicle routing problem with time windows. Ed. 2016 IEEE 4th International Conference on future Internet of Things and Cloud Workshops (FiCloudW), 22-24 Aug. 2016. 351–354.
   Google Scholar
• Koç, Ç, & Karaoglan, I. (2016). The green vehicle routing problem: A heuristic based exact solution approach. Applied Soft Computing, 39, 154–164. https://doi.org/https://doi.org/10.1016/j.asoc.2015.10.064
   Web of Science ®Google Scholar
• Kuo, Y. (2010). Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem. Computers & Industrial Engineering, 59(1), 157–165. https://doi.org/https://doi.org/10.1016/j.cie.2010.03.012
   Web of Science ®Google Scholar
• Kuo, Y., & Wang, C. C. (2011). Optimizing the VRP by minimizing fuel consumption. Management of Environmental Quality: An International Journal, 22(4), 440–450. https://doi.org/https://doi.org/10.1108/14777831111136054
   Google Scholar
• Kwon, Y.-J., et al. (2013). Heterogeneous fixed fleet vehicle routing considering carbon emission. Transportation Research Part D: Transport and Environment, 23, 81–89. https://doi.org/https://doi.org/10.1016/j.trd.2013.04.001
   Web of Science ®Google Scholar
• Li, J., & Zhang, J. (2014). Study on the effect of carbon emission trading mechanism on logistics distribution routing decisions. Syst. Eng. Theory Pract, 34, 1779–1787.
   Google Scholar
• Masudin, I., et al. (2019). Capacitated Vehicle Routing problems: Nearest Neighbour vs. Tabu search. International Journal of Computer Theory and Engineering, 11(4), 76–79. https://doi.org/https://doi.org/10.7763/IJCTE.2019.V11.1246
   Google Scholar
• Mirjalili, S., & Lewis, A. (2016). The Whale Optimization algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/https://doi.org/10.1016/j.advengsoft.2016.01.008
   Web of Science ®Google Scholar
• Niu, Y., et al. (2018). Optimizing the green open vehicle routing problem with time windows by minimizing comprehensive routing cost. Journal of Cleaner Production, 171, 962–971. https://doi.org/https://doi.org/10.1016/j.jclepro.2017.10.001
   Web of Science ®Google Scholar
• Normasari, N. M. E., Yu, V. F., & Bachtiyar, C. (2019). A simulated annealing heuristic for the capacitated green vehicle routing problem. Mathematical Problems in Engineering, 2019, 2358258. https://doi.org/https://doi.org/10.1155/2019/2358258
   Web of Science ®Google Scholar
• Norouzi, N., Sadegh-Amalnick, M., & Tavakkoli-Moghaddam, R. (2017). Modified particle swarm optimization in a time-dependent vehicle routing problem: Minimizing fuel consumption. Optimization Letters, 11(1), 121–134. https://doi.org/https://doi.org/10.1007/s11590-015-0996-y
   Web of Science ®Google Scholar
• Oesterle, J., & Bauernhansl, T. (2016). Exact Method for the Vehicle Routing problem with Mixed Linehaul and Backhaul customers, Heterogeneous Fleet, time Window and Manufacturing capacity. Procedia CIRP, 41, 573–578. https://doi.org/https://doi.org/10.1016/j.procir.2015.12.040
   Google Scholar
• Osama, S., et al. (2017). Forecast of wind speed based on whale optimization algorithm. ed. Proceedings of the 1st International Conference on Internet of Things and Machine Learning, Liverpool city, United Kingdom, October 17 – 18, 2017. 1–9.
   Google Scholar
• Pardalos, P. M., et al. (1995). Parallel search for combinatorial optimization: Genetic algorithms, simulated annealing, tabu search and GRASP. ed. Parallel Algorithms for Irregularly Structured Problems, 1995// 1995 Berlin, Heidelberg, 317–331.
   Google Scholar
• 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/https://doi.org/10.1016/j.eswa.2018.01.052
   Web of Science ®Google Scholar
• Pop, P. C., Matei, O., & Sitar, C. P. (2013). An improved hybrid algorithm for solving the generalized vehicle routing problem. Neurocomputing, 109, 76–83. https://doi.org/https://doi.org/10.1016/j.neucom.2012.03.032
   Web of Science ®Google Scholar
• Sbihi, A., & Eglese, R. W. (2007). Combinatorial optimization and green logistics. 4OR, 5(2), 99–116. https://doi.org/https://doi.org/10.1007/s10288-007-0047-3
   Google Scholar
• Schröder, M., & Cabral, P. (2019). Eco-friendly 3D-Routing: A GIS based 3D-Routing-model to estimate and reduce CO2-emissions of distribution transports. Computers, Environment and Urban Systems, 73, 40–55. https://doi.org/https://doi.org/10.1016/j.compenvurbsys.2018.08.002
   Web of Science ®Google Scholar
• Shen, L., et al. (2018). Multi-depot open vehicle routing problem with time windows based on carbon trading. International Journal of Environmental Research and Public Health, 15(9), 2025. https://doi.org/https://doi.org/10.3390/ijerph15092025
   PubMed Web of Science ®Google Scholar
• Shuai, Y., Yunfeng, S., & Kai, Z. (2019). An effective method for solving multiple travelling salesman problem based on NSGA-II. Systems Science & Control Engineering, 7(2), 108–116. https://doi.org/https://doi.org/10.1080/21642583.2019.1674220
   Web of Science ®Google Scholar
• Stellingwerf, H. M., et al. (2018). Reducing CO2 emissions in temperature-controlled road transportation using the LDVRP model. Transportation Research Part D: Transport and Environment, 58, 80–93. https://doi.org/https://doi.org/10.1016/j.trd.2017.11.008
   Web of Science ®Google Scholar
• Stodola, P. (2020). Hybrid ant colony optimization algorithm applied to the multi-depot vehicle routing problem. Natural Computing, 19(2), 463–475. https://doi.org/https://doi.org/10.1007/s11047-020-09783-6
   Web of Science ®Google Scholar
• Udin, K., Gui, R., & Rahmawan, A. (2017). Green vehicle routing problem with heterogeneous fleet and time windows. ed. Proceedings of the 6th International Conference on Software and Computer Applications, Bangkok, Thailand, February 26-28, 2017. 223–227.
   Google Scholar
• Utama, D. M. (2019). An effective Hybrid Sine Cosine algorithm to minimize carbon emission on Flow-shop scheduling sequence Dependent Setup. Jurnal Teknik Industri, 20(1), 62–72. https://doi.org/https://doi.org/10.22219/JTIUMM.Vol20.No1.62-72
   Google Scholar
• Utama, D. M., et al. (2020a). The vehicle routing problem for perishable goods: A systematic review. Cogent Engineering, 7(1), 1816148. https://doi.org/https://doi.org/10.1080/23311916.2020.1816148
   Web of Science ®Google Scholar
• Utama, D. M., et al. (2020b). An effective hybrid ant lion algorithm to minimize mean tardiness on permutation flow shop scheduling problem. International Journal of Advances in Intelligent Informatics, 6(1), 23–35. https://doi.org/https://doi.org/10.26555/ijain.v6i1.385
   Google Scholar
• Utama, D. M., et al. (2020c). The hybrid whale optimization algorithm: A new metaheuristic algorithm for energy-efficient on flow shop with dependent sequence setup. Journal of Physics: Conference Series, 1569, 022094. https://doi.org/https://doi.org/10.1088/1742-6596/1569/2/022094
   Google Scholar
• Wang, R., et al. (2018). Solving the green-fuzzy vehicle routing problem using a revised hybrid intelligent algorithm. Journal of Ambient Intelligence and Humanized Computing, 10(2), 1–12. https://doi.org/https://doi.org/10.1007/s12652-018-0703-9
   Google Scholar
• Xiao, Y., & Konak, A. (2015). A simulating annealing algorithm to solve the green vehicle routing & scheduling problem with hierarchical objectives and weighted tardiness. Applied Soft Computing, 34, 372–388. https://doi.org/https://doi.org/10.1016/j.asoc.2015.04.054
   Web of Science ®Google Scholar
• Xiao, Y., & Konak, A. (2016). The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion. Transportation Research Part E: Logistics and Transportation Review, 88, 146–166. https://doi.org/https://doi.org/10.1016/j.tre.2016.01.011
   Web of Science ®Google Scholar
• Xiao, Y., & Konak, A. (2017). A genetic algorithm with exact dynamic programming for the green vehicle routing & scheduling problem. Journal of Cleaner Production, 167, 1450–1463. https://doi.org/https://doi.org/10.1016/j.jclepro.2016.11.115
   Web of Science ®Google Scholar
• Yan, Z., et al. (2018). An Ameliorative Whale Optimization algorithm for Multi-objective optimal Allocation of Water Resources in Handan, China. Water, 10(1), 87. https://doi.org/https://doi.org/10.3390/w10010087
   Web of Science ®Google Scholar
• Yasin, M., & Vincent, F. Y. (2013). A simulated annealing heuristic for the green vehicle routing problem. ed. Proceedings of the institute of industrial engineers Asian conference 2013, Taipe, Taiwan, 19–22 July 2013. 1261–1269.
   Google Scholar
• Zhang, J., et al. (2015). Vehicle routing problem with fuel consumption and carbon emission. International Journal of Production Economics, 170, 234–242. https://doi.org/https://doi.org/10.1016/j.ijpe.2015.09.031
   Web of Science ®Google Scholar
• Zhang, S., et al. (2019). Ant colony algorithm for routing alternate fuel vehicles in multi-depot vehicle routing problem. Decision Science in Action. Springer, 251–260.
   Google Scholar