On the scarcity of observations when modelling random inputs and the quality of solutions to stochastic optimisation problems
On the scarcity of observations when modelling random inputs and the quality of solutions to stochastic optimisation problems
Most of the literature on supply chain management assumes that the demand distributions and their parameters are known with certainty. However, this may not be the case in practice since decision makers may have access to limited amounts of historical demand data only. In this case, treating the demand distributions and their parameters as the true distributions is risky, and it may lead to sub-optimal decisions. To demonstrate this, this paper considers an inventory-routing problem with stochastic demands, in which the retailers have access to limited amounts of historical demand data. We use simheuristic method to solve the optimisation problem and investigate the impact of the limited amount of demand data on the quality of the simheuristic solutions to the underlying optimisation problem. Our experiment illustrates the potential impact of input uncertainty on the quality of the solution provided by a simheuristic algorithm.
2105-2113
Corlu, Canan G.
ecb0f999-21d4-41e2-8cab-58a33706f09e
Panadero, Javier
70cf8175-0e95-4239-9800-2732f8cfbb62
Juan, Angel A.
681f726e-e136-4028-816e-927f41c326d3
Stephan Onggo, Bhakti
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
14 December 2020
Corlu, Canan G.
ecb0f999-21d4-41e2-8cab-58a33706f09e
Panadero, Javier
70cf8175-0e95-4239-9800-2732f8cfbb62
Juan, Angel A.
681f726e-e136-4028-816e-927f41c326d3
Stephan Onggo, Bhakti
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Corlu, Canan G., Panadero, Javier, Juan, Angel A. and Stephan Onggo, Bhakti
(2020)
On the scarcity of observations when modelling random inputs and the quality of solutions to stochastic optimisation problems.
Bae, K.-H., Feng, B., Kim, S., Lazarova-Molnar, S., Zheng, Z., Roeder, T. and Thiesing, R.
(eds.)
In Proceedings of the 2020 Winter Simulation Conference, WSC 2020.
vol. 2020-December,
IEEE.
.
(doi:10.1109/WSC48552.2020.9384100).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Most of the literature on supply chain management assumes that the demand distributions and their parameters are known with certainty. However, this may not be the case in practice since decision makers may have access to limited amounts of historical demand data only. In this case, treating the demand distributions and their parameters as the true distributions is risky, and it may lead to sub-optimal decisions. To demonstrate this, this paper considers an inventory-routing problem with stochastic demands, in which the retailers have access to limited amounts of historical demand data. We use simheuristic method to solve the optimisation problem and investigate the impact of the limited amount of demand data on the quality of the simheuristic solutions to the underlying optimisation problem. Our experiment illustrates the potential impact of input uncertainty on the quality of the solution provided by a simheuristic algorithm.
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Accepted/In Press date: 16 August 2020
Published date: 14 December 2020
Additional Information:
Funding Information:
This work has been partially funded by the IoF2020 project of the European Union (grant agreement no. 731884), the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21, RED2018-102642-T), and the Erasmus+ Program (2019-I-ES01-KA103-062602). We would like to thank the three reviewers for their constructive feedback.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
Venue - Dates:
2020 Winter Simulation Conference, WSC 2020, , Orlando, United States, 2020-12-14 - 2020-12-18
Identifiers
Local EPrints ID: 449885
URI: http://eprints.soton.ac.uk/id/eprint/449885
ISSN: 0891-7736
PURE UUID: 7dc0a7d4-cac5-41ef-b1e3-f20d9ef0be1a
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Date deposited: 23 Jun 2021 16:31
Last modified: 18 Mar 2024 03:50
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Contributors
Author:
Canan G. Corlu
Author:
Javier Panadero
Author:
Angel A. Juan
Editor:
K.-H. Bae
Editor:
B. Feng
Editor:
S. Kim
Editor:
S. Lazarova-Molnar
Editor:
Z. Zheng
Editor:
T. Roeder
Editor:
R. Thiesing
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