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Robustness of inventory replenishment and customer selection policies for the dynamic and stochastic inventory-routing problem.pdf (297.6Kb)
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Title
Robustness of inventory replenishment and customer selection policies for the dynamic and stochastic inventory-routing problem
Robustness of inventory replenishment policies and client selection methods for the stochastic and dynamic inventory-routing problem
Author
Basagoiti, Rosa ccMondragon Unibertsitatea
Author (from another institution)
Roldán Nariño, Raul Fabian
Coelho, Leandro dos Santos
Research Group
Sistemas distribuidos de tiempo real
Published Date
2016
Publisher
Elsevier
Keywords
Inventory-routing problem
Inventory policies
Customer selection
Dynamic and stochastic IRP
Demand management
Abstract
When inventory management, distribution and routing decisions are determined simultaneously, implementing a vendor-managed inventory strategy, a difficult combinatorial optimization problem must be so ... [+]
When inventory management, distribution and routing decisions are determined simultaneously, implementing a vendor-managed inventory strategy, a difficult combinatorial optimization problem must be solved to determine which customers to visit, how much to replenish, and how to route the vehicles around them. This is known as the inventory-routing problem. We analyze a distribution system with one depot, one vehicle and many customers under the most commonly used inventory policy, namely the (s,S), for different values of s. In this paper we propose three different customer selection methods: big orders first, lowest storage first, and equal quantity discount. Each of these policies will select a different subset of customers to be replenished in each period. The selected customers must then be visited by a vehicle in order to deliver a commodity to satisfy the customers' demands. The system was analyzed using public benchmark instances of different sizes regarding the number of customers involved. We compare the quality and the robustness of our algorithms and detailed computational experiments show that our methods can significantly improve upon existing solutions from the literature. [-]
URI
http://hdl.handle.net/20.500.11984/5627
Publisher’s version
https://doi.org/10.1016/j.cor.2016.04.004
ISSN
0305-0548
Published at
Computers & Operations Research  Vol. 74. Pp. 14–20. October, 2016
Document type
Article
Version
Postprint – Accepted Manuscript
Rights
© 2016 Elsevier Ltd. All rights reserved.
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Open Access
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  • Articles - Engineering [332]

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