带时间窗和部分充电的双目标电动汽车路径问题的混合元启发式求解方法

Farbod Zahedi, H. Kia, M. Khalilzadeh
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引用次数: 0

摘要

目的车辆路径问题(VRP)在过去几十年中得到了广泛的研究,以降低物流成本和提高服务水平。此外,许多研究人员已经意识到绿色物流系统设计在减少环境污染和实现可持续发展方面的重要性。设计/方法/方法本文建立了具有时间窗和部分充电的电容式电动VRP的双目标数学模型。第一个目标是最小化路线以降低与车辆相关的成本,而第二个目标是基于软时间窗口最小化到达车辆段的延迟。针对这一问题,提出了一种包括非支配排序遗传算法(NSGA-II)和基于教学的优化算法(TLBO)的混合元启发式算法,称为NSGA-II-TLBO。Taguchi方法用于调整算法的参数。基于时间、平均理想距离(MID)、多样性、,结果表明,该混合算法在间距和同时实现两个目标的比率(RAS)方面的性能优于间距和RAS指数,p值<0.04。然而,MOSA和NSGA-II算法在中央处理器(CPU)时间指数方面具有更好的性能。此外,在MID和多样性指数方面,两种算法之间没有显著差异。最后,通过灵敏度分析,研究了模型参数变化对结果的影响。独创性/价值在本研究中,通过提出一个考虑充电可能性的时间窗的电容车辆路径问题的双目标数学模型,来解决环境友好的交通系统。
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A hybrid metaheuristic approach for solving a bi-objective capacitated electric vehicle routing problem with time windows and partial recharging
PurposeThe vehicle routing problem (VRP) has been widely investigated during last decades to reduce logistics costs and improve service level. In addition, many researchers have realized the importance of green logistic system design in decreasing environmental pollution and achieving sustainable development.Design/methodology/approachIn this paper, a bi-objective mathematical model is developed for the capacitated electric VRP with time windows and partial recharge. The first objective deals with minimizing the route to reduce the costs related to vehicles, while the second objective minimizes the delay of arrival vehicles to depots based on the soft time window. A hybrid metaheuristic algorithm including non-dominated sorting genetic algorithm (NSGA-II) and teaching-learning-based optimization (TLBO), called NSGA-II-TLBO, is proposed for solving this problem. The Taguchi method is used to adjust the parameters of algorithms. Several numerical instances in different sizes are solved and the performance of the proposed algorithm is compared to NSGA-II and multi-objective simulated annealing (MOSA) as two well-known algorithms based on the five indexes including time, mean ideal distance (MID), diversity, spacing and the Rate of Achievement to two objectives Simultaneously (RAS).FindingsThe results demonstrate that the hybrid algorithm outperforms terms of spacing and RAS indexes with p-value <0.04. However, MOSA and NSGA-II algorithms have better performance in terms of central processing unit (CPU) time index. In addition, there is no meaningful difference between the algorithms in terms of MID and diversity indexes. Finally, the impacts of changing the parameters of the model on the results are investigated by performing sensitivity analysis.Originality/valueIn this research, an environment-friendly transportation system is addressed by presenting a bi-objective mathematical model for the routing problem of an electric capacitated vehicle considering the time windows with the possibility of recharging.
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CiteScore
6.50
自引率
3.20%
发文量
30
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