M
Mehrdad Mohammadi
Researcher at Centre national de la recherche scientifique
Publications - 77
Citations - 2169
Mehrdad Mohammadi is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Medicine & Imperialist competitive algorithm. The author has an hindex of 21, co-authored 52 publications receiving 1500 citations. Previous affiliations of Mehrdad Mohammadi include ParisTech & Arts et Métiers ParisTech.
Papers
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Journal ArticleDOI
Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty
Mohammad Zhalechian,Reza Tavakkoli-Moghaddam,Reza Tavakkoli-Moghaddam,Behzad Zahiri,Mehrdad Mohammadi +4 more
TL;DR: Considering economic, environmental and social impacts, a new sustainable closed-loop location-routing-inventory model under mixed uncertainty is presented in this paper, where the environmental impacts of CO2 emissions, fuel consumption, wasted energy and the social impacts of created job opportunities and economic development are considered.
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Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study
TL;DR: In this article, a multi-objective integrated sustainable-resilient mixed integer linear programming model for designing a pharmaceutical supply chain network under uncertainty is presented, and a new fuzzy possibilistic-stochastic programming approach is developed.
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A bi-objective interval-stochastic robust optimization model for designing closed loop supply chain network with multi-priority queuing system
TL;DR: A bi-objective optimization model for designing a closed loop supply chain (CLSC) network under uncertainty in which the total costs and the maximum waiting times in the queue of products are considered to minimize.
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Sustainable hub location under mixed uncertainty
TL;DR: In this paper, a mixed possibilistic-stochastic programming approach is proposed to construct the crisp counterpart of the original HLP model and a simulated annealing (SA) and an imperialist competitive algorithm (ICA) with a new solution representation are developed to solve real-sized instances whose performances are compared with a proposed lower bound.
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Machine Learning at the service of Meta-heuristics for solving Combinatorial Optimization Problems: A state-of-the-art
Maryam Karimi-Mamaghan,Mehrdad Mohammadi,Patrick Meyer,Amir Mohammad Karimi-Mamaghan,El-Ghazali Talbi +4 more
TL;DR: This paper provides a review on the use of machine learning techniques in the design of different elements of meta-heuristics for different purposes including algorithm selection, fitness evaluation, initialization, evolution, parameter setting, and cooperation.