A multi-objective optimization model for hub network design under uncertainty: An inexact rough-interval fuzzy approach

This article proposes a multi-objective mixed-integer model to optimize the location of hubs within a hub network design problem under uncertainty. The considered objectives include minimizing the maximum accumulated travel time, minimizing the total costs including transportation, fuel consumption...

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Bibliographic Details
Published in:Engineering optimization 2015-12, Vol.47 (12), p.1670-1688
Main Authors: Niakan, F., Vahdani, B., Mohammadi, M.
Format: Article
Language:English
Subjects:
Algorithms
Computer Aided Engineering
Computer Science
Costs
Emissions
Engineering Sciences
Fuzzy
Fuzzy logic
Heuristic
hub location
Hubs
invasive weed optimization
Mathematical models
Mechanical engineering
Mechanics
network design
Networks
Optimization
Programming
rough interval
Transportation
Uncertainty
uncertainty modelling
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Summary:This article proposes a multi-objective mixed-integer model to optimize the location of hubs within a hub network design problem under uncertainty. The considered objectives include minimizing the maximum accumulated travel time, minimizing the total costs including transportation, fuel consumption and greenhouse emissions costs, and finally maximizing the minimum service reliability. In the proposed model, it is assumed that for connecting two nodes, there are several types of arc in which their capacity, transportation mode, travel time, and transportation and construction costs are different. Moreover, in this model, determining the capacity of the hubs is part of the decision-making procedure and balancing requirements are imposed on the network. To solve the model, a hybrid solution approach is utilized based on inexact programming, interval-valued fuzzy programming and rough interval programming. Furthermore, a hybrid multi-objective metaheuristic algorithm, namely multi-objective invasive weed optimization (MOIWO), is developed for the given problem. Finally, various computational experiments are carried out to assess the proposed model and solution approaches.
ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2014.992891