R
Reza Tavakkoli-Moghaddam
Researcher at University of Tehran
Publications - 724
Citations - 17195
Reza Tavakkoli-Moghaddam is an academic researcher from University of Tehran. The author has contributed to research in topics: Supply chain & Fuzzy logic. The author has an hindex of 56, co-authored 650 publications receiving 13200 citations. Previous affiliations of Reza Tavakkoli-Moghaddam include University of British Columbia & Education and Research Network.
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Robust cold standby redundancy allocation for nonrepairable series–parallel systems through Min-Max regret formulation and Benders’ decomposition method
TL;DR: In this paper, a redundancy allocation problem in series-parallel systems with a cold standby strategy is formulated through Min-Max regret criterion, which is commonly used to define robust solutions.
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A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities
TL;DR: To solve the novel p-mobile hub location–allocation problem, four meta-heuristic algorithms, namely multi-objective particle swarm optimization (MOPSO), a non-dominated sorting genetic algorithm (NSGA-II), a hybrid of k-medoids as a famous clustering algorithm and NSGA- II (KNSGA
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A Lagrangean decomposition approach for a novel two-echelon node-based location-routing problem in an offshore oil and gas supply chain
TL;DR: A Lagrangean decomposition method, which is a particular case oflagrangean relaxation, is presented to solve the problem of offshore oil and gas fleet composition mix periodic location-routing problem with time windows.
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Design of a fuzzy bi-objective reliable p-hub center problem
TL;DR: In order to solve large-sized instances of the presented mathematical model, two meta-heuristic algorithms, namely SA and self-adaptive DE (SADE) are developed and their performances are evaluated using a proposed lower bound approach.
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A novel hybrid approach combining electromagnetism-like method with Solis and Wets local search for continuous optimization problems
TL;DR: A novel hybrid approach for EM is presented by employing a well-known local search, called Solis and Wets, by utilizing an attraction-repulsion mechanism to move sample points towards optimality in continuous optimization problems.