Title :
Evolutionary algorithms using adaptive mutation for the selective pickup and delivery problem
Author :
Liao, Xin-Lan ; Ting, Chuan-Kang
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Chung Cheng Univ., Chiayi, Taiwan
Abstract :
The selective pickup and delivery problem (SPDP) is a novel variant of the pickup and delivery problem. This problem relaxes the constraint that all pickup nodes must be visited along the route. Specifically, the SPDP aims to find the shortest route that can supply delivery nodes with required commodities from some selected pickup nodes. Selection of pickup nodes is capable of reducing the transportation cost; on the other hand, it increases the search space and difficulty in resolving the SPDP. In this study, we propose an adaptive mutation that focuses on the selection of proper pickup nodes for the SPDP. Two evolutionary algorithms (EAs), namely genetic algorithm and memetic algorithm, for the SPDP are developed as well. Experimental results show that the proposed adaptive mutation can lead to better selection of pickup nodes for shorter routes, which validates its effectiveness on improving the two EAs for the SPDP.
Keywords :
genetic algorithms; order picking; transportation; EA; SPDP; adaptive mutation; delivery nodes; evolutionary algorithms; genetic algorithm; memetic algorithm; pickup nodes; selective pickup and delivery problem; transportation cost; Biological cells; Evolutionary computation; Genetic algorithms; Genetics; Space exploration; Vehicles;
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
DOI :
10.1109/CEC.2012.6252884