An effective parallel improving tabu search algorithm for Heterogeneous Fixed Fleet Vehicle Routing Problem

One of the special variants of VRP model, the Heterogeneous Fixed Fleet Vehicle Routing Problem (HFFVRP) is discussed in this paper. The objective is to minimize the total delivery cost, including vehicle fixed cost and traveling variable cost. An effective parallel improving tabu search algorithm is developed to solve the model. Multiple neighborhood searching strategy and parallel improving technique are used to efficiently take advantage of the searching iterations. A waste function is introduced to evaluate the solution instead of directly calculating the total cost function. This change helps improve the convergent speed of the random searching process. Numerical experiments based on the typical CVRP and VFM instances are discussed, and the results show a satisfied performance in term of both quality and computational time.