A new approach to some possibilistic linear programming problems
Fuzzy Sets and Systems
Fuzzy multiple objective programming and compromise programming with Pareto optimum
Fuzzy Sets and Systems
Evaluating weapon system by Analytical Hierarchy Process based on fuzzy scales
Fuzzy Sets and Systems
Computers and Operations Research
A model and methodologies for the location problem with logistical components
Computers and Operations Research
Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach
Computers and Industrial Engineering - Supply chain management
Application of modified fuzzy ahp method to analyze bolting sequence of structural joints
Application of modified fuzzy ahp method to analyze bolting sequence of structural joints
A strategic model for supply chain design with logical constraints: formulation and solution
Computers and Operations Research
Fuzzy group decision-making for facility location selection
Information Sciences—Informatics and Computer Science: An International Journal
Strategic level three-stage production distribution planning with capacity expansion
Computers and Industrial Engineering
Modeling capacitated location-allocation problem with fuzzy demands
Computers and Industrial Engineering
Information Sciences: an International Journal
Developing a fuzzy analytic hierarchy process (AHP) model for behavior-based safety management
Information Sciences: an International Journal
A genetic algorithm approach for multi-objective optimization of supply chain networks
Computers and Industrial Engineering
Application of fuzzy minimum cost flow problems to network design under uncertainty
Fuzzy Sets and Systems
Computers and Operations Research
Computing efficient solutions to fuzzy multiple objective linear programming problems
Fuzzy Sets and Systems
Toward a generalized theory of uncertainty (GTU)--an outline
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
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The capacitated multi-facility location problem is a complex and imprecise decision-making problem which contains both quantitative and qualitative factors. In the literature, many objectives for optimizing many types of logistics networks are described: (i) minimization objectives such as cost, inventory, transportation time, environmental impact, financial risk and (ii) maximization objectives such as profit, customer satisfaction, and flexibility and robustness. However, only a few papers have considered quantitative and qualitative factors together with imprecise methodologies. Unlike traditional cost-based optimization techniques, the approach proposed here evaluates these factors together while considering various viewpoints. Decision-makers must deal both factors together to model complex structure of real-world applications. In this paper, a two-phase possibilistic linear programming approach and a fuzzy analytical hierarchical process approach have been combined to optimize two objective functions (''minimum cost'' and ''maximum qualitative factors benefit'') in a four-stage (suppliers, plants, distribution centers, customers) supply chain network in the presence of vagueness. The results and findings of this method are illustrated with a numerical example, and the advantages of this methodology are discussed in the conclusion.