A green multi-period request assignment problem for road freight transport

Abstract

This paper addresses a multi-period pickup and delivery problem with time windows, where carriers must fulfill committed transport requests while deciding whether to accept additional requests to enhance their financial and environmental performance. Given the increasing focus on sustainability, the objective is to balance profitability and CO${}_2$e emissions. To tackle this bi-objective problem, we propose a mixed-integer linear programming formulation that accounts for heterogeneous vehicles and both hard and soft time windows. To efficiently solve large-scale instances, we introduce a Hybrid Adaptive Large Neighborhood Search (HALNS) algorithm, which integrates population-based Tabu Search with a mutation operator within an ALNS framework. The proposed HALNS is benchmarked against multiple existing methods to assess its effectiveness and efficiency. Computational experiments demonstrate that HALNS efficiently solves large-scale instances, outperforming existing approaches. In addition, our numerical analysis provides key managerial insights for companies that want to achieve environmentally sustainable transport operations. Our numerical results indicate that imposing stricter emission targets can reduce CO${}_2$e emissions by up to 40% while decreasing profits by approximately 21%. In contrast, increasing the size of the fleet leads to an increase in profits 15% and improves the performance of the delivery, but at the cost of higher emissions. Furthermore, relaxing the time window constraints improves operational flexibility, resulting in an increase in average profits of 5% while reducing emissions by approximately 7%. These findings highlight the trade-offs involved in sustainable logistics planning and offer actionable insights for managers.