A low-carbon, fixed-tour scheduling problem with time windows in a time-dependent traffic environment

Siyue Zhang, Zhenghan Zhou, Rui Luo, Runze Zhao, Yiyong Xiao, Yuchun Xu

Research output: Contribution to journalArticlepeer-review

Abstract

Traffic congestion is a major concern in urban transportation in supply chain management. Road-based logistic companies can mitigate their Carbon dioxide (CO2) emissions effectively by optimising their operation. In this study, we observed a low-carbon, fixed-tour scheduling problem with time windows (LC-FTSP-TW) that is designed to consider the factors that can minimise the greenhouse-gas emissions of logistics systems. Through better planning of the delivery times, we delineated a system to control the schedules of two vehicle types: fossil-fuel-powered and electric-powered vehicles. We formulated the LC-FTSP-TW as a mixed-integer linear programming model that can take into consideration time-varying traffic conditions, customer time windows, and vehicle energy-consumption functions. The proposed model was observed to be convenient for practical use, as it could be solved directly using commercial optimisation toolboxes, such as CPLEX and Gurobi, with continuous optimal results. In addition, we developed an efficient dynamic programming algorithm for solving large-sized problems with discrete optimal results. Computational experiments were conducted on a group of test instances to verify the proposed model and algorithm, which demonstrated considerable reductions in CO2 emissions compared to non-optimised solutions for both the tested fossil-fuel-powered and electric-powered vehicles.
Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalInternational Journal of Production Research
Early online date15 Dec 2022
DOIs
Publication statusE-pub ahead of print - 15 Dec 2022

Keywords

  • Dynamic programming
  • carbon dioxide emissions
  • green supply chain
  • mixed integer linear programming
  • optimisation

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