Time-Dependent Vehicle Routing Optimization Incorporating Pollution Reduction Using Hybrid Gray Wolf Optimizer and Neural Networks

Zhongneng Ma, Ching-Tsung Jen and Adel Aazami ()
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Zhongneng Ma: School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
Ching-Tsung Jen: Department of Global Business, Chinese Culture University, Taipei 111396, Taiwan
Adel Aazami: Institute for Transport and Logistics Management, Department of Global Business and Trade, Vienna University of Economics and Business, Vienna 1020, Austria

Sustainability, 2025, vol. 17, issue 11, 1-27

Abstract: Road transport is a major contributor to air pollution, necessitating sustainable solutions for urban logistics. This study presents a time-dependent vehicle routing problem (VRP) model aimed at minimizing fuel consumption and greenhouse gas emissions while addressing stochastic customer demands. By incorporating key environmental factors such as road gradients, vehicle load, temperature, wind direction, and asphalt type, the proposed model provides a comprehensive approach to reducing transportation-related pollutants. To solve the computationally complex problem, a hybrid algorithm combining the gray wolf optimizer (GWO) and the multilayer perceptron (MLP) neural network is introduced. The algorithm demonstrates superior performance, achieving an error rate of less than 2% for medium-scale problems and significantly reducing fuel and driver costs. Sensitivity analyses reveal the profound impact of environmental parameters, with wind speed and direction altering optimal routing in over 80% of cases for large-scale instances. This research advances green logistics by integrating dynamic environmental considerations into routing decisions, balancing economic objectives with sustainability. The proposed model and algorithm offer a scalable solution to real-world challenges, enabling policymakers and logistics planners to improve environmental outcomes while maintaining operational efficiency.

Keywords: green logistics; fuel consumption optimization; time-dependent routing; gray wolf optimizer; multilayer perceptron (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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