S.A. Mansouri

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.

TL;DR: This study proposes a bi-objective mixed possibilistic, two-stage stochastic programming model to address supplier selection and order allocation problem to build the resilient supply base under operational and disruption risks.

TL;DR: A novel framework is proposed for integrated business continuity and disaster recovery planning for efficient and effective resuming and recovering of critical operations after being disrupted and developed a novel interactive augmented e-constraint method to find the final preferred compromise solution.

TL;DR: An effective multi-objective particle swarm optimization (MOPSO) algorithm to find a good approximation of Pareto frontier where total weighted flow time, total weighted tardiness, and total machine load variation are to be minimized simultaneously.

TL;DR: In this paper, a mixed integer linear programming model is proposed to make strategic as well as tactical decisions in a blood collection system over a multi-period planning horizon, where a robust possibilistic programming approach is applied to cope with the inherent epistemic uncertainty of the model's parameters.