Delivery routing problem of pure electric vehicle with multi-objective pick-up and delivery integration

Abstract

With the growth of people’s environmental awareness and the encouragement of government policies, the use of electric vehicles in logistics distribution is gradually increasing. In order to solve the dual demand of customers’ simultaneous pick-up and delivery in the “last kilometer logistics”, an electric vehicle routing problem with simultaneous pick-up and delivery and time window (EVRPSPDTW) is considered from the perspective of multi-objective distribution in this paper. Firstly, a decision-making model based on distribution cost and power consumption function is established. In this model, distribution cost includes transportation cost, vehicle use cost, penalty cost of not arriving on time and charging cost. Power consumption function is the energy loss caused by air resistance, tire rolling friction and transmission system. Secondly, a multi-objective genetic algorithm (NSGA-II) optimization solution with fast nondominated ranking and elite strategy is designed, and in view of the shortcomings of traditional NSGA-II, it is proposed to complete population initialization through greedy algorithm and random rules, introduce adaptive cross-mutation strategy in the chromosome crossing and mutation stage, and design three different neighborhood operators in mutation operation based on variant fitness function. Finally, the sensitivity analysis of traffic congestion coefficient further proves the effectiveness of the proposed model and the improved algorithm.

Challenges and Opportunities for Applying Meta-Heuristic Methods in Vehicle Routing Problems: A Review

“…The use of intelligent solution initialization methods and efficient representation can also help reduce processing time and enable exploration of the search space on a larger scale. For example, greedy algorithms and random rules are used to generate the initial population in a Genetic Algorithm to solve VRPPDs [29].…”

Multi-Objective Routing Optimization in Electric and Flying Vehicles: A Genetic Algorithm Perspective

“…Cai et al [46] focused on solving the multi-objective vehicle routing problem (VRP) with simultaneous pick-up and delivery and time window constraints (Mo-EVRPSDPTW). This study proposed a mathematical model that aims to minimize total logistics cost and maximize average customer satisfaction.…”

“…This flexibility enables decision makers to select solutions based on specific priorities and constraints, enhancing the practicality and effectiveness of the optimization process. It is worth noting that several studies propose future research directions to further enhance the optimization models and techniques [8,46,49,53]. Suggestions include incorporating additional concepts and policies, considering nonlinear charging and queuing time, integrating clustering algorithms, addressing pickup operations, and exploring dynamic factors like traffic congestion and demand variability.…”

Masalah Antar-Jemput Barang Menggunakan Armada Kendaraan Listrik dengan Kapasitas Angkut dan Kapasitas Baterai Berbeda

“…Dengan demikian, karena kompleksitas yang melekat dan tantangan komputasi, sebagian besar model VRP diselesaikan secara heuristik (Liu et al [9]). Yilmaz & Kalayci [24] menggunakan metode VNS (variable neighborhood search) untuk menyelesaikan EVRPPD secara heuristik, Xu et al [22] menerapkan versi adaptif dari metode heuristik yang serupa untuk menyelesaikan EVRPPD berskala besar, sedangkan Cai et al [2] menggunakan algoritme genetik multiobjektif untuk menyelesaikan EVRPPD dengan jendela waktu (time windows). Masalah terakhir yang sama diselesaikan oleh Erdelic & Caric [4] menggunakan metode metaheuristik ALNS (adaptive large neighborhood search).…”