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Full truckload transportation (FTL) in the form of freight containersrepresents one of the most important transportation modes in internationaltrade. Due to large volume and scale, in FTL, delivery time is often lesscritical but cost and service quality are crucial. Therefore, efficientlysolving large scale multiple shift FTL problems is becoming more and moreimportant and requires further research. In one of our earlier studies, a setcovering model and a three-stage solution method were developed for amulti-shift FTL problem. This paper extends the previous work and presents asignificantly more efficient approach by hybridising pricing and cuttingstrategies with metaheuristics (a variable neighbourhood search and a geneticalgorithm). The metaheuristics were adopted to find promising columns (vehicleroutes) guided by pricing and cuts are dynamically generated to eliminateinfeasible flow assignments caused by incompatible commodities. Computationalexperiments on real-life and artificial benchmark FTL problems showed superiorperformance both in terms of computational time and solution quality, whencompared with previous MIP based three-stage methods and two existingmetaheuristics. The proposed cutting and heuristic pricing approach canefficiently solve large scale real-life FTL problems.

A Hybrid Pricing and Cutting Approach for the Multi-Shift Full Truckload Vehicle Routing Problem