Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An expert system framework for automated storage and retrieval system control
Computers and Industrial Engineering
Shared storage policies based on the duration stay of unit loads
Management Science
Re-marshaling export containers in port container terminals
ICC&IE Selected papers from the 22nd ICC&IE conference on Computers & industrial engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Complete solution of the eight-puzzle and the benefit of node ordering in IDA
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
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Determining the optimal storage assignment for loads in a unit-load warehouse has been extensively addressed in the facility logistics literature. However, the process of implementing a particular storage assignment given the current assignment of loads has not received much attention. Typically, unit-load warehouses use downtime or overtime to remove loads from their current location and move them to the suggested location. This study presents Rearrange-While-Working (RWW) as a strategy to optimize the process of rearranging a warehouse while serving a list of move requests. This study examines three scenarios: (1) one empty location in the warehouse and the material handling equipment (MHE) is idle; (2) one empty location in the warehouse under the RWW strategy; and (3) when there are multiple empty locations in the warehouse under RWW. In the first scenario, the MHE can make any movement desired as it is idle. For the other two scenarios it is assumed that the MHE is not idle so loads can only be moved when requested to perform a move request. Due to the complexity of the problems, several heuristics are proposed. Experimental results indicate that the proposed heuristics perform satisfactorily in terms of solution quality and computational time.