文献类型：期刊文献

英文题名：A two-phase algorithm for the dynamic time-dependent green vehicle routing problem in decoration waste collection

作者：Wang, Wubin[1];Li, Yashuai[1,4];Yan, Hongbin[2];Zhao, Wencong[1];Zhao, Qiuhong[1,3];Luo, Kaiping[1,4]

机构：[1]Beihang Univ, Sch Econ & Management, 37 Xueyuan Rd, Beijing 100191, Peoples R China;[2]East China Univ Sci & Technol, Sch Business, Meilong Rd 130, Shanghai 200237, Peoples R China;[3]Beijing Key Lab Emergency Support Simulat Technol, Beijing 100191, Peoples R China;[4]Beihang Univ, Key Lab Complex Syst Anal & Management & Decis, Minist Educ, Beijing 100191, Peoples R China

年份：2025

卷号：262

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：;EI(收录号：20244417278534);WOS:【SCI-EXPANDED(收录号:WOS:001401314200001)】；

基金：Acknowledgments This study was supported by the Beijing Natural Science Foundation under Grant Number 9212012.

语种：英文

外文关键词：Dynamic vehicle routing; Customer forecasting; Time-dependent green vehicle routing; Decoration waste; Waiting time strategy

摘要：Transportation activities associated with construction waste generate substantial carbon emissions, an issue that is attracting increasing environmental concern. To raise construction waste transportation efficiency and reduce carbon emission, we address the dynamic time-dependent green vehicle routing problem for decoration waste collection (DTDGVRP-DWC). In the existing literature, many deterministic approaches to the dynamic vehicle routing problem are myopic, as they only react to already arrived requests. In this paper, we propose a stochastic sampling method to tackle with uncertain customer request in the real world. The DTDGVRP-DWC is formulated as an 0-1 programming. In the new model, we consider urban traffic congestion and variable speeds over time, and factors that significantly influence both carbon emissions and fuel costs. To quickly solve the complicated optimization problem, we develop a two-phase algorithm. In the first phase, we embed a competitive simulated annealing algorithm to determine visitation schedules for anticipated and early-request customers. In the second phase, we propose an event-trigger mechanism to decompose the dynamic problem into a series of static sub-problems, and an effective heuristic to solve each sub-problem. Computational results show that as long as the prediction accuracy exceeds a threshold, the forecast routing method consistently performs better than reactive routing. A real-world case demonstrates that an early commitment waiting time strategy benefits timely service requirements, whereas a late or hybrid waiting time strategy takes overall efficiency and customer satisfaction into account.