Customer‐Oriented Vehicle Routing Problem with Environment Consideration: Two‐Phase Optimization Approach and Heuristic Solution

Abstract

With the fastest consumer demand growth, the increasing customer’s demands trend to multivarieties and small-batch and the customer requires an efficient distribution planning. How to plan the vehicle route to meet customer satisfaction of mass distribution as well as reduce the fuel consumption and emission has become a hot topic. This paper proposes a two-phase optimization method to handle the vehicle routing problem, considering the customer demands and time windows coupled with multivehicles. The first phase of the optimization method provides a fuzzy hierarchical clustering method for customer grouping. The second phase formulates the optimization en-group vehicle routing problem model and a genetic algorithm to account for vehicle routing optimization within each group so that fuel consumption and emissions are minimized. Finally, we provide some numerical examples. Results show that the two-phase optimization method and the designed algorithm are efficient.

A Review of Heuristics and Hybrid Methods for Green Vehicle Routing Problems considering Emissions

“…+ere are other works with applications of heuristics to solve GVRPs, such as ALNS for multicompartment vehicles for city logistics and G-VRPTW [107,108], GA for the effect of governmental time window policy on the routing planning decisions of cold chain distribution companies [109], fuzzy hierarchical clustering method and GA for the customer-oriented routing problem with consideration of the environment [110], and a TS and VNS for VRP in the home health care sector called VRPTW with synchronization, precedence, and fuel consumption constraints (VRPTW-SPFC) [111].…”

“…Chen and Shi [133] proposed a set of instances for MCVRPTW based on the Solomon's benchmark with groups of instances with 25, 50, and 100 customers. +e • LRPLCCC, Leng et al [33] Liu et al [28] • JD-GVRP, Liu et al [28] Wei et al [21] • Green DRASS with time-varying speeds, Wei et al [ • G-VRPTW, Eshtehadi et al [107] Eskandarpour et al [65] • HeVRPMD, Eskandarpour et al [65] Hooshmand and MirHassani [114] • TDGVRP-AF, Hooshmand and MirHassani [114] Koc et al [55] • Variant of PRP, Koç et al [55] Meng et al [110] • Customer-oriented routing problem with environment consideration, Meng et al [110] Molina et al [123] • Eco-WCVRP, Molina et al [123] Rezaei et al [98] • G-VRPTW, Rezaei et al [98] Wang et al [37] • MD-GVRP, Wang et al [37] de Oliveira da Costa et al [18] • GVRP, de Oliveira da Costa et al [18] Kargari Esfand Abad et al [62] • Pickup and delivery PRP variant considering integration and consolidation shipments in cross-docking, Kargari Esfand Abad et al…”

An Algorithm to Solve Heterogeneous Vehicle Routing Problem With Second Trip

“…Mole [11] took the lead in analyzing the approach based on the idea of 'cluster-first route-second', whose key is to use the cluster algorithm to divide the points first, and then treat every cluster as a separate traveling salesman problem (TSP), which can effectively reduce the computational complexity and is suitable for large-scale VRPs. For example, either K-means algorithm [12] or hierarchical clustering methods [13] can be used as the realization of 'cluster-first'. In addition, the metaheuristic method draws inspiration from some random phenomena in nature, which can also be applied to solve VRP.…”

Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty

“…However, the literature needs a comprehensive exploration of route planning under dynamic road conditions caused by flooding. Additionally, the distribution patterns of disaster-affected nodes add complexity to the routing process, which requires further study [9,[25][26][27].…”