Optimal logistics scheduling with dynamic information in emergency response: case studies for humanitarian objectives

Cao, J., Han, H, Wang, Y.J. and Han, T.C. (2023) Optimal logistics scheduling with dynamic information in emergency response: case studies for humanitarian objectives. Advances in Production Engineering & Management, 18 (3). 381 - 395. ISSN 1854-6250

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Abstract

The mathematical model of infectious disease is a typical problem in mathematical modeling, and the common infectious disease models include the susceptible-infected (SI) model, the susceptible-infected-recovered model (SIR), the susceptible-infected-recovered-susceptible model (SIRS) and the susceptible-exposed-infected-recovered (SEIR) model. These models can be used to predict the impact of regional return to work after the epidemic. In this paper, we use the SEIR model to solve the dynamic medicine demand information in humanitarian relief phase. A multistage mixed integer programming model for the humanitarian logistics and transport resource is proposed. The objective functions of the model include delay cost and minimum running time in the time-space network. The model describes that how to distribute and deliver medicine resources from supply locations to demand locations with an efficient and lower-cost way through a transportation network. The linear programming problem is solved by the proposed Benders decomposition algorithm. Finally, we use two cases to calculate model and algorithm. The results of the case prove the validity of the model and algorithm.

Item Type:	Article
Official URL:	https://apem-journal.org/index.html
Additional Information:	© 2023 The Authors
Divisions:	LSE
Subjects:	Q Science > QA Mathematics R Medicine > RA Public aspects of medicine
Date Deposited:	04 Jan 2024 15:12
Last Modified:	22 Nov 2024 02:51
URI:	http://eprints.lse.ac.uk/id/eprint/121167

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