An effective evolutionary algorithm for the biobjective full truckload transportation service procurement problem

In truckload transportation service procurement, transit time is an important factor considered by shippers. In addition to transportation costs, transit time is also expected to be minimized so as to improve service quality and transportation efficiency. In this work, we study the full truckload transportation service procurement problem with transit time and develop a two-phase multiobjective evolutionary algorithm to explore the Pareto front of the problem. The algorithm encodes a chromosome of an individual using a set of winning carriers to reduce decision space. Each individual corresponds to a biobjective subproblem that has a set of solutions on its local Pareto front. The individuals are explored using fitness-based crossover and mutation operators in the first phase of the algorithm. The promising individuals that could produce nondominated solutions, and their local Pareto fronts are further explored by a sophisticated method in the second phase. Computational results show that the new fitness-based operators are effective and the solutions found by the algorithm can well approach the Pareto front.