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.
Papers
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A hybrid method for solving stochastic job shop scheduling problems
TL;DR: A hybrid method using a neural network approach to generate initial feasible solutions and then a simulated annealing algorithm to improve the quality and performance of the initial solutions in order to produce the optimal/near-optimal solution.
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Design of a pharmaceutical supply chain network under uncertainty considering perishability and substitutability of products
TL;DR: A novel robust possibilistic optimization approach is introduced and its performance is analyzed, and the two objective functions of the presented model seek to minimize the total cost and the maximum unmet demand.
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Soft computing based on new interval-valued fuzzy modified multi-criteria decision-making method
TL;DR: A new interval-valued fuzzy modified TOPSIS (IVFM-TOPSIS) method is proposed that can reflect both subjective judgment and objective information in real life situations and appraise the performance of alternatives against both subjective and objective criteria with multi-judges for decision-making problems.
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Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chance-constrained model
TL;DR: In this paper, a mathematical model is developed that integrates the network design decisions in both forward and reverse supply chain networks, and the model also utilizes an effective reliability approach to find a robust network design.
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Two hybrid meta-heuristic algorithms for a dual-channel closed-loop supply chain network design problem in the tire industry under uncertainty
Amir Mohammad Fathollahi-Fard,Maxim A. Dulebenets,Mostafa Hajiaghaei–Keshteli,Reza Tavakkoli-Moghaddam,Mojgan Safaeian,Hassan Mirzahosseinian +5 more
TL;DR: This work for the first time proposes a dual-channel, multi-product,Multi-period,multi-echelon closed-loop SCND under uncertainty for the tire industry, and hybridized with the genetic algorithm and simulated annealing to strengthen the diversification and intensification phases.