A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem

This study considers a multi-objective combined budget constrained Facility Location/Network Design Problem (FL/NDP), in which system uncertainty is considered. The most obvious practical examples of the problem are territorial designing and locating of academies, airline networks, and medical servi...

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Vydáno v:Scientia Iranica. Transaction E, Industrial engineering Ročník 25; číslo 3; s. 1750 - 1767
Hlavní autoři: Shishebori, D, Babadi, A Yousefi, Noormohammadzadeh, Z
Médium: Journal Article
Jazyk:angličtina
Vydáno: Tehran Sharif University of Technology 01.06.2018
Témata:
Algorithms
Costs
Customer satisfaction
Facilities
Fuzzy systems
Health services
Integer programming
Location of industry
Multiple objective analysis
Network design
Network reliability
Optimization
Robustness (mathematics)
Service centers
Uncertainty
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Shrnutí:This study considers a multi-objective combined budget constrained Facility Location/Network Design Problem (FL/NDP), in which system uncertainty is considered. The most obvious practical examples of the problem are territorial designing and locating of academies, airline networks, and medical service centers. In order to assure network reliability versus uncertainty, an efficient robust optimization approach is applied to model the proposed problem. The formulation is minimizing the total expected costs, including transshipment costs, Facility Location (FL) costs, and fixed cost of road/link utilization as well as minimizing the total penalties of uncovered demand nodes. Then, in order to consider several system uncertainties, the proposed model is changed to a fuzzy robust model by suitable approaches. An efficient sub-gradient based Lagrangian relaxation algorithm is applied. In addition, a practical example is studied. In the following, a series of experiments, including several test problems, is designed and solved to evaluate of the performance of the algorithm. The obtained results emphasize that considering practical factors (e.g. several uncertainties, system disruptions, and customer satisfaction) in modelling of the problem can lead to significant improvement of the system yield and, subsequently, more efficient utilization of the established network.
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DOI:10.24200/sci.2017.4447