Solution method for the location planning problem of logistics park with variable capacity

摘要

In this paper we study a logistics park location planning problem in which the capacity of the logistics park is determined by the sectors used to establish it in an open site. Since the size of each sector is not necessarily the same in every potential site, the capacity of the logistics park is thus variable, which makes this problem different from the traditional location problems in which the capacity of each facility is fixed. The task of this problem is to determine the location of the logistics parks, the sectors to be used to establish the logistics park in each open site, and the allocation of customers to the established logistics parks so as to minimize the total costs for establishing the logistics parks and supplying the demands of customers. The size mode is introduced to deal with the nonlinear establishment cost function and consequently this problem is formulated as an integer linear programming (ILP) model. Since CPLEX can only solve the ILP model with small-size problems, a tabu search (TS) hybrid with filter and fan (F&F) is presented to obtain near optimal solutions. In the hybrid algorithm, the TS is used to improve the solution by changing the allocation of customers to open sites while the F&F is used to further improve the solution by adjusting the status of sites (i.e., open or closed). In addition, an elite solution pool is constructed to store good solutions found in the searching history. Whenever the hybrid algorithm is trapped in local minima, a new start solution will be generated from the elite pool so as to improve the search diversity. To evaluate the performance of the proposed hybrid TS method, the column generation (CG) method with an acceleration strategy is developed to provide tight lower bounds. Computational results showed that the proposed hybrid algorithm can obtain optimal solutions for most of the small size problems and satisfactory near-optimal solutions with comparison to lower bounds for large size problems.