A Hybrid Pricing and Cutting Approach for the Multi-Shift Full Truckload Vehicle Routing Problem
This research provides a more efficient solution for large-scale multi-shift full truckload transportation problems, which is crucial for logistics companies aiming to reduce costs and improve service quality.
This paper addresses the multi-shift full truckload vehicle routing problem, a critical aspect of international trade, by developing a hybrid pricing and cutting approach combined with metaheuristics. The proposed method significantly outperforms previous MIP-based three-stage methods and existing metaheuristics in both computational time and solution quality on real-life and artificial benchmark problems.
Full truckload transportation (FTL) in the form of freight containers represents one of the most important transportation modes in international trade. Due to large volume and scale, in FTL, delivery time is often less critical but cost and service quality are crucial. Therefore, efficiently solving large scale multiple shift FTL problems is becoming more and more important and requires further research. In one of our earlier studies, a set covering model and a three-stage solution method were developed for a multi-shift FTL problem. This paper extends the previous work and presents a significantly more efficient approach by hybridising pricing and cutting strategies with metaheuristics (a variable neighbourhood search and a genetic algorithm). The metaheuristics were adopted to find promising columns (vehicle routes) guided by pricing and cuts are dynamically generated to eliminate infeasible flow assignments caused by incompatible commodities. Computational experiments on real-life and artificial benchmark FTL problems showed superior performance both in terms of computational time and solution quality, when compared with previous MIP based three-stage methods and two existing metaheuristics. The proposed cutting and heuristic pricing approach can efficiently solve large scale real-life FTL problems.