Optimizing Transportation Sequence in Warehouse with Genetic Algorithms

Abstract : Optimizing transportation sequence is crucial to reduce material handling costs in warehouse operations and thus in total logistics costs. Transportation sequence is the ordering of storage and retrieval jobs that a material handling device has to perform to finish an order list. In many studies, the optimization of transportation sequence has been simplified as an order-picking problem, and accordingly solved as a classical traveling salesman problem. However, transportation sequence is a double-cycle storage and retrieval problem (DCSRP) in itself, meaning that the combination of storage and retrieval jobs into double cycles has to be considered simultaneously. In this paper, we propose formulating the DCSRP as a permutation problem and applying several genetic algorithms to solve the formulated problem. Extensive computational experiments were performed to demonstrate the capability of the approach. The experimental analysis confirms that our approach could solve the problem efficiently on the one hand, and addresses the question of which genetic operators are best applied to the formulated DCSRP on the other hand.
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AETA 2013: Recent Advances in Electrical Engineering and Related Sciences, Dec 2013, Ho Chi Minh City, Vietnam. Springer, 2013, Lecture Notes in Electrical Engineering. 〈10.1007/978-3-642-41968-3_40〉
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Xuan-Thuong Tran, Thanh-Do Tran, Hwan-Seong Kim. Optimizing Transportation Sequence in Warehouse with Genetic Algorithms. AETA 2013: Recent Advances in Electrical Engineering and Related Sciences, Dec 2013, Ho Chi Minh City, Vietnam. Springer, 2013, Lecture Notes in Electrical Engineering. 〈10.1007/978-3-642-41968-3_40〉. 〈hal-01058036〉

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