Multiple allocation hub covering flow problem under uncertainty

Sener, Nazmi; Feyzıog̃lu, Orhan

doi:10.1007/s10479-022-04553-2

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Developed and developing countries have increased their attention to environmental and social issues along with economic ones as a result of population growth in recent years. However, the environmental and particularly the social and economic dimensions of multiperiod location problems, including HLPs, have been less examined [16,17]. As a result, different responses can be provided by simultaneously considering the dimensions of stability in multi-period HLPs and the coverage radius of hub facilities, so that decisionmakers can compare existing conditions and the different aspects of the problem with a great deal of freedom of action and select the most appropriate alternatives [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Presenting a Model for Locating and Allocating Multi-Period Hubs and Comparing It With a Multi-Objective Imperialist Competitive Algorithm

Chen,

Muda,

Salman

et al. 2023

Foundations of Computing and Decision Sciences

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Recently, air pollution has received much attention as a result of reflections on environmental issues. Accordingly, the hub location problem (HLP) seeks to find the optimal location of hub facilities and allocate points for them to meet the demands between source-destination pairs. Thus, in this study, decisions related to location and allocation in a hub network are reviewed and a multi-objective model is proposed for locating and allocating capacity-building facilities at different time periods over a planning horizon. The objective functions of the model presented in this study are to minimize costs, reduce air pollution by diminishing fuel consumption, and maximize job opportunities. In order to solve the given model, the General Algebraic Modeling System (GAMS) along with innovative algorithms are utilized. The results presented a multi-objective sustainable model for full-covering HLP, and provided access to a hub network with minimum transport costs, fuel consumption, and GHG (greenhouse gas) emissions, and maximum job opportunities in each planning horizon utilizing MOICA (multi-objective imperialist competitive algorithm) and GAMS to solve the proposed model. The study also assessed the performance of the proposed algorithms with the aid of the QM, MID, SM, and NSP indicators, acquired from comparing the proposed meta-heuristic algorithm based on some indicators, proving the benefit and efficiency of MOICA in all cases.

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