RETRACTED ARTICLE: A new humanitarian relief logistic network for multi-objective optimization under stochastic programming A new humanitarian relief logistic network for multi-objective optimization under stochastic programming

Millions of affected people and thousands of victims are consequences of earthquakes, every year. Therefore, it is necessary to prepare a proper preparedness and response planning. The objectives of this paper are i ) minimizing the expected value of the total costs of relief supply chain, ii ) mini...

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Vydané v:Applied intelligence (Dordrecht, Netherlands) Ročník 52; číslo 12; s. 13729 - 13762
Hlavní autori: Ghasemi, Peiman, Goodarzian, Fariba, Abraham, Ajith
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.09.2022
Springer Nature B.V
Predmet:
Artificial Intelligence
Big Data-Driven Large-Scale Group Decision Making Under Uncertainty
Computer Science
Machines
Manufacturing
Mechanical Engineering
Processes
Distribution planning
Meta-heuristic algorithms
Humanitarian relief logistics
Simulation-optimization model
ISSN:0924-669X, 1573-7497
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Shrnutí:Millions of affected people and thousands of victims are consequences of earthquakes, every year. Therefore, it is necessary to prepare a proper preparedness and response planning. The objectives of this paper are i ) minimizing the expected value of the total costs of relief supply chain, ii ) minimizing the maximum number of unsatisfied demands for relief staff and iii ) minimizing the total probability of unsuccessful evacuation in routes. In this paper, a scenario based stochastic multi-objective location-allocation-routing model is proposed for a real humanitarian relief logistics problem which focused on both pre- and post-disaster situations in presence of uncertainty. To cope with demand uncertainty, a simulation approach is used. The proposed model integrates these two phases simultaneously. Then, both strategic and operational decisions (pre-disaster and post-disaster), fairness in the evacuation, and relief item distribution including commodities and relief workers, victim evacuation including injured people, corpses and homeless people are also considered simultaneously in this paper. The presented model is solved utilizing the Epsilon-constraint method for small- and medium-scale problems and using three metaheuristic algorithms for the large-scale problem (case study). Empirical results illustrate that the model can be used to locate the shelters and relief distribution centers, determine appropriate routes and allocate resources in uncertain and real-life disaster situations.
Bibliografia:ObjectType-Correction/Retraction-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-022-03776-x