Seyed Ali Torabi

TL;DR: In this article, a robust optimization model for handling the inherent uncertainty of input data in a closed-loop supply chain network design problem is proposed, and the robust counterpart of the proposed mixed-integer linear programming model is presented by using the recent extensions in robust optimization theory.

TL;DR: In this article, a multi-objective possibilistic mixed integer linear programming model (MOPMILP) is proposed for integrating procurement, production and distribution planning considering various conflicting objectives simultaneously as well as the imprecise nature of some critical parameters such as market demands, cost/time coefficients and capacity levels.

TL;DR: A bi-objective possibilistic mixed integer programming model to deal with closed-loop supply chain network design problems and an interactive fuzzy solution approach is developed by combining a number of efficient solution approaches from the recent literature.

TL;DR: This paper addresses the problem of socially responsible supply chain network design under uncertain conditions by developing a bi-objective mathematical programming model, called robust possibilistic programming (RPP), and several varieties of RPP models are developed.

TL;DR: Computational results using real data reveal promising performance of the proposed SBPSP model in comparison with the existing relief network in Tehran and contributes to the literature on optimization based design of relief networks under mixed possibilistic-stochastic uncertainty.