Abousleiman, R., & Rawashdeh, O. (2015). Energy consumption model of an electric vehicle. 2015 IEEE transportation electrification conference and expo (ITEC), (pp. 1–5).
Affi, M., Derbel, H., & Jarboui, B. (2018). Variable neighbourhood search algorithm for the green vehicle routing problem. International Journal of Industrial Engineering Computations, 9, 195–204.
Agency, E. E. (2020). Annual European Union greenhouse gas inventory 1990–2018 and inventory report 2020. Annual European Union greenhouse gas inventory 1990–2018 and inventory report 2020. Retrieved from https://www.eea.europa.eu/publications/european-union-greenhouse-gas-inventory-2020
Andelmin, J., & Bartolini, E. (2019). A multi-start local search heuristic for the green vehicle routing problem based on a multigraph reformulation. Computers & Operations Research, 109, 43–63.
Asghari, M., Fathollahi-Fard, A. M., Mirzapour Al-e-hashem, S. M., & Dulebenets, M. A. (2022). Transformation and Linearization Techniques in Optimization: A State-of-the-Art Survey. Mathematics, 10, 283.
Bektaş, T., & Laporte, G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological, 45, 1232–1250.
Bruglieri, M., Pezzella, F., Pisacane, O., & Suraci, S. (2015). A variable neighbourhood search branching for the electric vehicle routing problem with time windows. Electronic Notes in Discrete Mathematics, 47, 221–228.
Chen, Y., Wu, G., Sun, R., Dubey, A., Laszka, A., & Pugliese, P. (2020). A review and outlook of energy consumption estimation models for electric vehicles. arXiv preprint arXiv:2003.12873.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26, 29–41.
Erdoğan, S., & Miller-Hooks, E. (2012). A green vehicle routing problem. Transportation research part E: logistics and transportation review, 48, 100–114.
Felipe, Á., Ortuño, M. T., Righini, G., & Tirado, G. (2014). A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transportation Research Part E: Logistics and Transportation Review, 71, 111–128.
Froger, A., Mendoza, J. E., Jabali, O., & Laporte, G. (2019). Improved formulations and algorithmic components for the electric vehicle routing problem with nonlinear charging functions. Computers & Operations Research, 104, 256–294.
Gendreau, M., Ghiani, G., & Guerriero, E. (2015). Time-dependent routing problems: A review. Computers & operations research, 64, 189–197.
Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers & operations research, 13, 533–549.
Goeke, D., & Schneider, M. (2015). Routing a mixed fleet of electric and conventional vehicles. European Journal of Operational Research, 245, 81–99.
Golden, B. L., Raghavan, S., Wasil, E. A., & others. (2008). The vehicle routing problem: latest advances and new challenges (Vol. 43). Springer.
Hansen, P., Mladenović, N., & Urošević, D. (2006). Variable neighbourhood search and local branching. Computers & Operations Research, 33, 3034–3045.
Hiermann, G., Hartl, R. F., Puchinger, J., & Vidal, T. (2019). Routing a mix of conventional, plug-in hybrid, and electric vehicles. European Journal of Operational Research, 272, 235–248.
Hiermann, G., Puchinger, J., Ropke, S., & Hartl, R. F. (2016). The electric fleet size and mix vehicle routing problem with time windows and recharging stations. European Journal of Operational Research, 252, 995–1018.
Ichoua, S., Gendreau, M., & Potvin, J.-Y. (2003). Vehicle dispatching with time-dependent travel times. European journal of operational research, 144, 379–396.
IEA. (2019). Global Energy & CO2 Status Report 2019. Global Energy & CO2 Status Report 2019. Retrieved from https://www.iea.org/reports/global-energy-co2-status-report-2019
Keskin, M., & Çatay, B. (2016). Partial recharge strategies for the electric vehicle routing problem with time windows. Transportation research part C: emerging technologies, 65, 111–127.
Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220, 671–680.
Kovač, N., Davidović, T., & Stanimirović, Z. (2018). Variable neighbourhood search methods for the dynamic minimum cost hybrid berth allocation problem. Information Technology and Control, 47, 471–488.
Li, Y., Lim, M. K., Tan, Y., Lee, Y., & Tseng, M.-L. (2020). Sharing economy to improve routing for urban logistics distribution using electric vehicles. Resources, Conservation and Recycling, 153, 104585.
Macrina, G., Di Puglia Pugliese, L., Guerriero, F., & Laporte, G. (2019a). The green mixed fleet vehicle routing problem with partial battery recharging and time windows. Computers & Operations Research, 101, 183–199.
Macrina, G., Laporte, G., Guerriero, F., & Di Puglia Pugliese, L. (2019b). An energy-efficient green-vehicle routing problem with mixed vehicle fleet, partial battery recharging and time windows. European Journal of Operational Research, 276, 971–982.
Malandraki, C., & Daskin, M. S. (1992). Time dependent vehicle routing problems: Formulations, properties and heuristic algorithms. Transportation science, 26, 185–200.
Matijević, L. (2022). Variable Neighbourhood Search for Multi-label Feature Selection. International Conference on Mathematical Optimization Theory and Operations Research, (pp. 94–107).
Mavrovouniotis, M., Ellinas, G., & Polycarpou, M. (2018). Ant colony optimization for the electric vehicle routing problem. 2018 IEEE Symposium series on computational intelligence (SSCI), (pp. 1234–1241).
Mavrovouniotis, M., Li, C., Ellinas, G., & Polycarpou, M. (2019). Parallel ant colony optimization for the electric vehicle routing problem. 2019 IEEE Symposium Series on Computational Intelligence (SSCI), (pp. 1660–1667).
Mladenović, N., & Hansen, P. (1997). Variable neighbourhood search. Computers & operations research, 24, 1097–1100.
Montoya, A., Guéret, C., Mendoza, J. E., & Villegas, J. G. (2017). The electric vehicle routing problem with nonlinear charging function. Transportation Research Part B: Methodological, 103, 87–110.
Normasari, N. M., Yu, V. F., Bachtiyar, C., & others. (2019). A simulated annealing heuristic for the capacitated green vehicle routing problem. Mathematical Problems in Engineering, 2019.
Peng, B., Zhang, Y., Gajpal, Y., & Chen, X. (2019). A memetic algorithm for the green vehicle routing problem. Sustainability, 11, 6055.
Ren, X., Huang, H., Feng, S., & Liang, G. (2020). An improved variable neighbourhood search for bi-objective mixed-energy fleet vehicle routing problem. Journal of Cleaner Production, 275, 124155.
Ropke, S., & Pisinger, D. (2006). An adaptive large neighbourhood search heuristic for the pickup and delivery problem with time windows. Transportation science, 40, 455–472.
Schneider, M., Stenger, A., & Goeke, D. (2014). The electric vehicle-routing problem with time windows and recharging stations. Transportation science, 48, 500–520.
Toth, P., & Vigo, D. (2002). The vehicle routing problem. SIAM.
Toth, P., & Vigo, D. (2014). Vehicle routing: problems, methods, and applications. SIAM.
Urazel, B., & Keski̇n, K. (2021). A Hybrid Solution Approach for Electric Vehicle Routing Problem with Soft Time-Windows. El-Cezeri, 8, 994–1006.
Wang, L., & Lu, J. (2019). A memetic algorithm with competition for the capacitated green vehicle routing problem. IEEE/CAA Journal of Automatica Sinica, 6, 516–526.
Wang, L., Gao, S., Wang, K., Li, T., Li, L., & Chen, Z. (2020). Time-dependent electric vehicle routing problem with time windows and path flexibility. Journal of Advanced Transportation, 2020.
Wang, Y., Assogba, K., Fan, J., Xu, M., Liu, Y., & Wang, H. (2019). Multi-depot green vehicle routing problem with shared transportation resource: Integration of time-dependent speed and piecewise penalty cost. Journal of Cleaner Production, 232, 12–29.
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.
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.
Yavuz, M., & Çapar, I. (2017). Alternative-fuel vehicle adoption in service fleets: Impact evaluation through optimization modeling. Transportation Science, 51, 480–493.
Yazdani, M., Amiri, M., & Zandieh, M. (2010). Flexible job-shop scheduling with parallel variable neighbourhood search algorithm. Expert Systems with Applications, 37, 678–687.
Zhang, R., Guo, J., & Wang, J. (2020a). A time-dependent electric vehicle routing problem with congestion tolls. IEEE Transactions on Engineering Management.
Zhang, S., Gajpal, Y., & Appadoo, S. S. (2018a). A meta-heuristic for capacitated green vehicle routing problem. Annals of Operations Research, 269, 753–771.
Zhang, S., Gajpal, Y., Appadoo, S. S., & Abdulkader, M. M. (2018b). Electric vehicle routing problem with recharging stations for minimizing energy consumption. International Journal of Production Economics, 203, 404–413.
Zhang, S., Zhang, W., Gajpal, Y., & Appadoo, S. S. (2019). Ant colony algorithm for routing alternate fuel vehicles in multi-depot vehicle routing problem. In Decision science in action (pp. 251–260). Springer.
Zhang, W., Gajpal, Y., Appadoo, S., Wei, Q., & others. (2020b). Multi-depot green vehicle routing problem to minimize carbon emissions. Sustainability, 12, 3500.
Zhang, X., Yao, J., Liao, Z., & Li, J. (2018c). The electric vehicle routing problem with soft time windows and recharging stations in the reverse logistics. International Conference on Management Science and Engineering Management, (pp. 171–182).