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S. Meysam Mousavi

Researcher at Shahed University

Publications -  105
Citations -  2681

S. Meysam Mousavi is an academic researcher from Shahed University. The author has contributed to research in topics: Fuzzy logic & Fuzzy set. The author has an hindex of 28, co-authored 81 publications receiving 2120 citations. Previous affiliations of S. Meysam Mousavi include University of Tehran.

Papers
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Group decision making based on novel fuzzy modified TOPSIS method

TL;DR: This paper shows that the proposed fuzzy modified TOPSIS method is a suitable decision making tool for the manufacturing decisions with two examples for the robot selection and rapid prototyping process selection.
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A hybrid simulated annealing algorithm for location and routing scheduling problems with cross-docking in the supply chain

TL;DR: In this paper, a two-stage mixed-integer programming (MIP) model for the location of cross-docking centers and vehicle routing scheduling problems with cross-ding due to potential applications in the distribution networks is presented.
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A new design of the elimination and choice translating reality method for multi-criteria group decision-making in an intuitionistic fuzzy environment

TL;DR: A new extension of the ELECTRE, known as the elimination and choice translating reality method, for multi-criteria group decision-making problems based on intuitionistic fuzzy sets is designed and a new discordance intuitionistic index is introduced, which is extended from the concept of the fuzzy distance measure.
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Sustainable supplier selection by a new decision model based on interval-valued fuzzy sets and possibilistic statistical reference point systems under uncertainty

TL;DR: In this article, the authors introduce a sustainable supply chain management approach for evaluating and selecting appropriate sustainable suppliers. But, they do not consider the impact of environmental factors on the sustainability of the supply chain.
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A robust optimization approach for pollution routing problem with pickup and delivery under uncertainty

TL;DR: In this paper, a new mixed integer linear programming (MILP) approach is presented under uncertainty by taking green house emissions into consideration, and a robust counterpart of the MILP is introduced by applying the recent robust optimization theory.