Starita, Stefano, Scaparra, Maria Paola (2022) Improving supply system reliability against random disruptions: Strategic protection investment. Journal of the Operational Research Society, 73 (6). pp. 1307-1324. ISSN 0160-5682. (doi:10.1080/01605682.2021.1911605) (KAR id:88374)
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Official URL: https://doi.org/10.1080/01605682.2021.1911605 |
Abstract
Supply chains, as vital systems to the well-being of countries and economies, require systematic approaches to reduce their vulnerability. In this paper, we proposea non linear optimisation model to determine an effective distribution of protectiveresources among facilities in service and supply systems so as to reduce the probability of failure to which facilities are exposed in case of external disruptions. Thefailure probability of protected assets depends on the level of protection investmentsand the ultimate goal is to minimize the expected facility-customer transport ortravel costs to provide goods and services. A linear version of the model is obtainedby exploiting a specialized network flow structure. Furthermore, an efficient GRASPsolution algorithm is developed to benchmark the linearised model and resolve numerical difficulties. The applicability of the proposed model is demonstrated usingthe Toronto hospital network. Protection measures within this context correspondto capacity expansion investments and reduce the likelihood that hospitals are unable to satisfy patient demand during periods of high hospitalization (e.g., during apandemic). Managerial insights on the protection resource distribution are discussedand a comparison between probabilistic and worst-case disruptions is provided.
Item Type: | Article |
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DOI/Identification number: | 10.1080/01605682.2021.1911605 |
Uncontrolled keywords: | Disruption management; resource allocation; fortification; healthcare; Covid-19 |
Subjects: | H Social Sciences > H Social Sciences (General) |
Divisions: | Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems |
Depositing User: | Paola Scaparra |
Date Deposited: | 26 May 2021 12:39 UTC |
Last Modified: | 05 Nov 2024 12:54 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/88374 (The current URI for this page, for reference purposes) |
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