Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low–Carbon

Abstract

Hazardous material transport accidents are events with low probability and high consequence risk. With the increase in the proportion of hazardous materials transported on domestic roads, increasing number of scholars have begun to study this field. In this study, a multi-objective hazardous materials transport route planning model considering road traffic resilience and low carbon, which considers the uncertainty of demand and time and is under the limit of the time window. It transports many types of hazardous materials from multiple suppliers to multiple retails with the three goals (transportation cost, risk and carbon emission). This model fills the gap in the research on hazardous materials transportation in the field of low carbon, and this is the first time that road traffic resilience is considered in the transport of hazardous materials as one of the weight factors of risk calculation. We designed a improved ant colony optimization algorithm (ACO) to obtain the pareto optimal solution set. We compare the improved ACO with genetic algorithm and simulated annealing algorithm. The results show that the improved ACO has better solution quality and solution space, which verifies the validity and reliability of the improved ACO.

Application of Improving ABC in Cold Chain Low Carbon Logistics Path Planning

“…They also proposed the future development direction of multi-criteria decision-making technology for green logistics and low-carbon transportation systems [11]. Wang Z et al established a multi-objective hazardous material transportation route planning model considering road traffic elasticity and low-carbon to address the increasing proportion of hazardous materials in domestic road transportation, filling the gap in research on hazardous material transportation in the low-carbon field [15]. Tao N et al established a mathematical model for optimizing the path of cold chain logistics delivery vehicles with the lowest comprehensive cost.…”

Optimization of Simultaneous Pickup and Delivery Vehicle Routing with Three-Dimensional Balanced Loading Constraints

“…With the in-depth study of practical problems, a large number of variant studies have been generated on the basis of the traditional VRP, such as the capacitated vehicle routing problem (CVRP) [6], vehicle routing problem with time window (VRPTW) [7], vehicle routing problem with simultaneous pickup and delivery (VRPSPD) [8]. At the same, a large amount of research attempts have been devoted to designing efficient optimization methods in order to improve the solving efficiency for this problem, such as nested and joint algorithms [9], genetic algorithms [10,11], large neighborhood search algorithms (LNS) [12], hybrid metaheuristic algorithm [13], ant colony optimization algorithm [14], grey wolf optimization algorithm [15] and so on. Abbaspour and Aghsami [16] conducted a comprehensive comparative analysis of the performance of 13 different meta-heuristic algorithms, including the dragonfly algorithm and the grasshopper optimization algorithm.…”