A Hybrid Heuristic Technique for Optimizing Intermodal Logistics Timed Transfer Systems

Abstract

Increasing integration of intermodal transport resources is a useful approach toward achieving ‘Green Logistics,’ which can effectively improve the utilization of existing transportation infrastructure, increase system resiliency, reduce storage requirements, and reduce greenhouse gas emissions, fuel consumption, and traffic congestion. We first develop an optimization model for coordinating vehicle schedules and cargo transfers at intermodal freight terminals, which is done primarily by optimizing coordinated service frequencies and slack times, while also considering loading and unloading, storage and cargo processing operations. The studied problem is formulated as a multi-hub multi-mode and multi-commodities network problem with nonlinear time value functions for shipped cargos. In order to solve the large-scale intermodal logistics timed transfer network problem, a hybrid technique combining sequential quadratic programming and genetic algorithms (GA-SQP) is developed in this study.