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Introducing the dynamic customer location-allocation problem.

Ankrah, Reginald; Lacroix, Benjamin; McCall, John; Hardwick, Andrew; Conway, Anthony


Reginald Ankrah

John McCall

Andrew Hardwick

Anthony Conway


In this paper, we introduce a new stochastic Location-Allocation Problem which assumes the movement of customers over time. We call this new problem Dynamic Customer Location-Allocation Problem (DC-LAP). The problem is based on the idea that customers will change locations over a defined horizon and these changes have to be taken into account when establishing facilities to service customers demands. We generate 1440 problem instances by varying the problem parameters of movement rate which determines the possible number of times a customer will change locations over the defined period, the number of facilities and the number of customers. We propose to analyse the characteristics of the instances generated by testing a search algorithm using the stochastic dynamic evaluation (based on the replication of customer movement scenarios) and a deterministic static evaluation (based on the assumption that customer will not move over time). We show that the dynamic approach obtains globally better results, but the performances are highly related to the parameters of the problem. Moreover, the dynamic approach involves a significantly high computational overhead.

Start Date Jun 10, 2019
Publication Date Aug 8, 2019
Publisher Institute of Electrical and Electronics Engineers
Pages 3157-3164
ISBN 9781728121543
Institution Citation ANKRAH, R., LACROIX, B., MCCALL, J., HARDWICK, A. and CONWAY, A. 2019. Introducing the dynamic customer location-allocation problem. In Proceedings of the 2019 Institute of Electrical and Electronics Engineers (IEEE) Congress on evolutionary computation (IEEE CEC 2019), 10-13 June 2019, Wellington, NZ. Piscataway: IEEE [online], pages 3157-3164. Available from:
Keywords Dynamic customer location-allocation problem; Static approach; Dynamic approach; Population-based incremental learning algorithm; Simulation model


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