  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.
