Advances in Production Engineering & Management
Volume 18 | Number 3 | September 2023 | pp 381–395
https://doi.org/10.14743/apem2023.3.480
Optimal logistics scheduling with dynamic information in emergency response: Case studies for humanitarian objectives
Cao, J.; Han, H.; Wang, Y.J.; Han, T.C.
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A B S T R A C T
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.
A R T I C L E I N F O
Keywords • Logistic; Humanitarian logistics; Optimization; Multi-objective; Dynamic information; Delay cost; Benders decomposition algorithm; Mixed integer programming; Ant colony optimization algo-rithm; Genetic algorithm
Corresponding author • Cao, J.
Article history • Received 15 December 2022, Revised 9 September 2023, Accepted 15 September 2023
Published on-line • 19 November 2023
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