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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Journal Article
Application of genetic algorithm for designing cellular manufacturing system incrementally
TL;DR: Two heuristic methods based on multi-stage programming and genetic algorithm are proposed for incremental cell formation and the results show that the multi- stage programming solves small problems faster than exact algorithms such as branch and bound.
Book ChapterDOI
Sustainable Facility Location-Routing Problem for Blood Package Delivery by Drones with a Charging Station
TL;DR: In this paper, a multi-objective integrated facility location and drone routing problem in blood package delivery by considering sustainability factors is proposed, which aims to maximize the weight of selected points and the amount of demand coverage while minimizing the transferring and constructing facilities cost.
Journal Article
Solving a location-allocation problem by a fuzzy self-adaptive NSGA-II
TL;DR: A modified non-dominated sorting genetic algorithm (NSGA-II) with local search for a bi-objective location-allocation model to define the best places and capacity of the distribution centers as well as to allocate consumers in such a way that uncertain consumers demands are satisfied.
Proceedings ArticleDOI
Non-Preemptive Open Shop Scheduling Considering Machine Availability
TL;DR: This paper considers a non-preemptive open shop scheduling problem (OSSP), in which machines are not available to process jobs on known periodic interval times resulted from periodic service repair, rest period, and so on.
Journal Article
Performance Analysis of Dynamic and Static Facility Layouts in a Stochastic Environment
TL;DR: Two new quadratic assignment-based mathematical models corresponding to the dynamic and static approaches to cope with the stochastic dynamic (or multi-period) problem are developed.