Solving an inventory-routing problem with stochastic demand using simheuristic
Solving an inventory-routing problem with stochastic demand using simheuristic
Supply chain operations have become more complex. Hence, in order to optimise supply chain operations, we often need to simplify the optimisation problem in such a way that it can be solved efficiently using either exact methods or metaheuristics. One common simplification is to assume all model inputs are deterministic. However, for some management decisions, considering the uncertainty in model inputs (e.g. demands, travel times, processing times) is essential. Otherwise, the results may be misleading and might lead to a wrong decision. This paper considers an example of a complex supply chain operation that can be viewed as an Inventory-Routing Problem with stochastic demands. We demonstrate how a simheuristic framework can be employed to solve the problem. Further, we illustrate the risks of not considering input uncertainty. The results show that simheuristic can produce a good result and ignoring the uncertainty in the model input may lead to sub-optimal results.
Association for Computing Machinery
Onggo, Bhakti Stephan
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Juan, Angel A.
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Panadero, Javier
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Corlu, Canan Gunes
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Agustin, Alba
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Onggo, Bhakti Stephan
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Juan, Angel A.
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Panadero, Javier
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Corlu, Canan Gunes
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Agustin, Alba
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Onggo, Bhakti Stephan, Juan, Angel A., Panadero, Javier, Corlu, Canan Gunes and Agustin, Alba
(2019)
Solving an inventory-routing problem with stochastic demand using simheuristic.
In Proceedings of the 2019 Winter Simulation Conference.
Association for Computing Machinery.
12 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
Supply chain operations have become more complex. Hence, in order to optimise supply chain operations, we often need to simplify the optimisation problem in such a way that it can be solved efficiently using either exact methods or metaheuristics. One common simplification is to assume all model inputs are deterministic. However, for some management decisions, considering the uncertainty in model inputs (e.g. demands, travel times, processing times) is essential. Otherwise, the results may be misleading and might lead to a wrong decision. This paper considers an example of a complex supply chain operation that can be viewed as an Inventory-Routing Problem with stochastic demands. We demonstrate how a simheuristic framework can be employed to solve the problem. Further, we illustrate the risks of not considering input uncertainty. The results show that simheuristic can produce a good result and ignoring the uncertainty in the model input may lead to sub-optimal results.
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2019_WSC_Onggo___IRP_Supply_Chain
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Accepted/In Press date: 2 June 2019
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Local EPrints ID: 433680
URI: http://eprints.soton.ac.uk/id/eprint/433680
PURE UUID: 89ddc9ed-5db8-4a98-a0fc-7102dbebe3fc
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Date deposited: 30 Aug 2019 16:30
Last modified: 16 Mar 2024 04:39
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Contributors
Author:
Angel A. Juan
Author:
Javier Panadero
Author:
Canan Gunes Corlu
Author:
Alba Agustin
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