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A holistic metric approach to solving the dynamic 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 dynamic variant of the Location-Allocation problem: Dynamic Location-Allocation Problem (DULAP). DULAP involves the location of facilities to service a set of customer demands over a defined horizon. To evaluate a solution to DULAP, we propose two holistic metric approaches: Static and Dynamic Approach. In the static approach, a solution is evaluated with the assumption that customer locations and demand remain constant over a defined horizon. In the dynamic approach, the assumption is made that customer demand, and demographic pattern may change over the defined horizon. We introduce a stochastic model to simulate customer population and distribution over time. We use a Genetic Algorithm and Population-Based Incremental Learning algorithm used in previous work to find robust and satisfactory solutions to DULAP. Results show the dynamic approach of evaluating a solution finds good and robust solutions.

Start Date Dec 11, 2018
Publication Date Dec 31, 2018
Publisher Springer (part of Springer Nature)
Pages 433-439
Series Title Lecture notes in computer science
Series Number 11311
Series ISSN 0302-9743
Book Title Artificial intelligence XXXV
ISBN 9783030041908
Institution Citation ANKRAH, R., LACROIX, B., MCCALL, J., HARDWICK, A. and CONWAY, A. 2018. A holistic metric approach to solving the dynamic location-allocation problem. In Bramer, M. and PETRIDIS, M. (eds.) Artificial intelligence xxxv: proceedings of the 38th British Computer Society's specialist group on artificial intelligence (SGAI) annual international artificial intelligence conference (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in artificial intelligence, 11311. Cham: Springer [online], pages 433-439. Available from:
Keywords Dynamic uncapacitated location-allocation problem; GA; PBIL; Holistic metric; Stochastic model


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