The multi-objective green p-hub centre routeing problem with hub congestion: Mathematical formulation and hybrid meta-heuristic
研究枢纽拥堵对配送系统的影响，提出一个多目标混合整数线性规划模型，同时优化服务时间和碳排放，并设计混合元启发式算法求解，对物流网络规划者有用。
Congestion adversly affects the performance of hub distribution systems, leading to facility overutilisation, service delays, and increased carbon emissions. To design responsive and green hub-and-spoke networks, it is paramount to adequately address the congestion effects arising in located facilities, particularly due to the processing of substantial demand flows. This paper introduces the Multi-Objective Green p-Hub Centre Routeing Problem with Hub Congestion, and presents a MILP to formulate it. The model integrates vehicle routeing, speed selection, demand flow scheduling, and hub processing capacity decisions with strategic HLP decisions. The objectives are to minimise the latest service time and transportation-related CO2 emissions. Given the NP-Hard nature of the p-HCP and the challenges raised by partial Pareto frontiers in MO-MILPs with binary variables, exact methods can be computationally expensive. Therefore, we propose a collaborative hybrid NSGA-II/SAA meta-heuristic with a demand scheduling, and speed-based neighbourhood diversification procedures. Extensive computational experiments on adapted AP instances show that the parallel variant of our hybrid meta-heuristic outperforms both the sequential variant and a two-phase method using CPLEX, identifying a greater number of efficient solutions. Finally, the proposed model yields significantly lower processing and waiting times, and further reduces CO2 emission costs relative to model disregarding congestion effects.