An Iterative Exact Algorithm over a Time-Expanded Network for the Transportation of Biomedical Samples

Daniel M. Ocampo-Giraldo (), Ana M. Anaya-Arenas () and Claudio Contardo ()
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Daniel M. Ocampo-Giraldo: Département d’analytique, opérations et technologies de l’information, Université du Quèbec à Montréal, Montreal, Quebec H2X 3X2, Canada; and CIRRELT, Montreal, Quebec H3T 2B2, Canada
Ana M. Anaya-Arenas: Département d’analytique, opérations et technologies de l’information, Université du Quèbec à Montréal, Montreal, Quebec H2X 3X2, Canada; and CIRRELT, Montreal, Quebec H3T 2B2, Canada
Claudio Contardo: CIRRELT, Montreal, Quebec H3T 2B2, Canada; and Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, Quebec H3G 1M8, Canada; and GERAD, Montreal, Quebec H3T 1N8, Canada

INFORMS Journal on Computing, 2025, vol. 37, issue 5, 1242-1266

Abstract: In this article we propose an iterative algorithm to address the optimization problem of distributing a set of multiple highly perishable commodities in a healthcare network. In the biomedical sample transportation problem, numerous commodities with short lifespans presume multiple transportation requests at the same facility in a day and restrict the maximum time to reach their destination. These two characteristics create an interdependency between the routing and the pickup decisions in time that is highly complex. To address these timing issues, we model this problem as a service network design problem over a time-expanded network. Our solution method aggregates the network at two levels. First, the commodities are aggregated and artificially consolidated, reducing the symmetry arising when multiple transportation requests are solicited within a short period of time. Second, the space-time nodes in the network are constructed dynamically, thus reducing the size of the mathematical model to be solved at each iteration. Moreover, the method creates auxiliary networks to calculate good-quality primal bounds to the problem. Our algorithm proves to be efficient to solve a set of real-life instances from the Quebec laboratory network under the management of the Ministère de la Santé et des Services sociaux (Ministry of Health and Social Services) with a detailed network of up to 2,377 periods and 277 transportation requests.

Keywords: time-expanded network; service network design; biomedical sample transportation problem; healthcare logistics; highly perishable products; blood transportation; interdependency; dynamic discretization algorithm (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:37:y:2025:i:5:p:1242-1266

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