On possibilistic linear programming
Fuzzy Sets and Systems
Multiple objective programming problems with possibilistic coefficients
Fuzzy Sets and Systems
The mean value of a fuzzy number
Fuzzy Sets and Systems - Fuzzy Numbers
The expected value of a fuzzy number
Fuzzy Sets and Systems
A new approach to some possibilistic linear programming problems
Fuzzy Sets and Systems
Information Sciences: an International Journal
Chance constrained programming with fuzzy parameters
Fuzzy Sets and Systems
Mathematics of Operations Research
Fuzzy Sets and Systems - Fuzzy mathematical programming
Robust Solutions to Uncertain Semidefinite Programs
SIAM Journal on Optimization
Operations Research
Multicriteria Optimization
A Robust Optimization Approach to Dynamic Pricing and Inventory Control with no Backorders
Mathematical Programming: Series A and B
Choosing robust solutions in discrete optimization problems with fuzzy costs
Fuzzy Sets and Systems
A closed-loop logistic model with a spanning-tree based genetic algorithm
Computers and Operations Research
Fuzzy optimization for supply chain planning under supply, demand and process uncertainties
Fuzzy Sets and Systems
Robust supply chain design under uncertain demand in agile manufacturing
Computers and Operations Research
A memetic algorithm for bi-objective integrated forward/reverse logistics network design
Computers and Operations Research
MRP with flexible constraints: A fuzzy mathematical programming approach
Fuzzy Sets and Systems
A multi-objective optimization for green supply chain network design
Decision Support Systems
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The importance of social responsibility of corporate and business units is increasingly emphasized by researchers and practitioners in recent years. Since supply chains play important roles in today's business environment, the issue of social responsibility should be considered carefully when designing and planning of supply chains to move towards sustainability. This paper addresses the problem of socially responsible supply chain network design under uncertain conditions. To this aim, first a bi-objective mathematical programming model is developed wherein its objective functions include minimizing the total cost and maximizing the supply chain social responsibility. Then, for coping with uncertain parameters effectively, a novel possibilistic programming approach, called robust possibilistic programming (RPP), is proposed. Several varieties of RPP models are developed and their differences, weaknesses, strengths and the most suitable conditions for being used are discussed. A real industrial case study is provided to illustrate the performance and applicability of the proposed RPP models in practice.