Abstract
The location selection of logistics distribution centers is a crucial issue in the modern urban logistics system. In order to achieve a more reasonable solution, an effective optimization algorithm is indispensable. In this paper, a new hybrid optimization algorithm named cuckoo search-differential evolution (CSDE) is proposed for logistics distribution center location problem. Differential evolution (DE) is incorporated into cuckoo search (CS) to improve the local searching ability of the algorithm. The CSDE evolves with a coevolutionary mechanism, which combines the Lévy flight of CS with the mutation operation of DE to generate solutions. In addition, the mutation operation of DE is modified dynamically. The mutation operation of DE varies under different searching stages. The proposed CSDE algorithm is tested on 10 benchmarking functions and applied in solving a logistics distribution center location problem. The performance of the CSDE is compared with several metaheuristic algorithms via the best solution, mean solution, and convergence speed. Experimental results show that CSDE performs better than or equal to CS, ICS, and some other metaheuristic algorithms, which reveals that the proposed CSDE is an effective and competitive algorithm for solving the logistics distribution center location problem.
Highlights
A green and well-developed city logistics system can reduce unnecessary transaction cost and improve economic efficiency
To make up the disadvantages of these two algorithms, this paper proposes an effective hybrid algorithm based on cuckoo search algorithm and differential evolution algorithm (CSDE)
From the formulation of cuckoo search-differential evolution (CSDE) to its implementation and comparison, it can be clearly seen that CSDE is a promising algorithm
Summary
A green and well-developed city logistics system can reduce unnecessary transaction cost and improve economic efficiency. Logistics distribution centers location problems are concerned with the optimal service or supply of a set of existing facilities whose location are fixed and known [2]. Sun and Gao et al [9] presented a bilevel programming model to find the optimal location for logistics distribution centers. CS algorithm has achieved satisfactory performance in solving benchmark unconstrained functions [20] and real-world problems, such as manufacturing scheduling [21], structural optimization [22], and so on. To extend its applications range, the proposed CSDE is used to solve several standard benchmarking functions and a typical logistics distribution center location problem.
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