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Ragheb Rahmaniani

Bio: Ragheb Rahmaniani is an academic researcher from École Polytechnique de Montréal. The author has contributed to research in topics: Variable neighborhood search & Facility location problem. The author has an hindex of 9, co-authored 13 publications receiving 547 citations. Previous affiliations of Ragheb Rahmaniani include Iran University of Science and Technology & Université de Montréal.

Papers
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Journal ArticleDOI
TL;DR: A state-of-the-art survey of the Benders Decomposition algorithm, emphasizing its use in combinatorial optimization and introducing a taxonomy of algorithmic enhancements and acceleration strategies based on the main components of the algorithm.

506 citations

Journal ArticleDOI
TL;DR: A mixed-integer model to optimize the location of facilities and the underlying transportation network at the same time to minimize the total transportation and operating costs is presented and a fix-and-optimize heuristic based on the evolutionary fire-fly algorithm is proposed.

49 citations

Journal ArticleDOI
TL;DR: This paper describes a Benders decomposition algorithm capable of efficiently solving large-scale instances of the well-known multicommodity capacitated network design problem with demand uncertainness.
Abstract: This paper describes a Benders decomposition algorithm capable of efficiently solving large-scale instances of the well-known multicommodity capacitated network design problem with demand uncertain...

49 citations

Journal ArticleDOI
TL;DR: The experimental results show that the hybrid PSO produces good solutions, is more efficient than the classical PSO, and is competitive with the best results from the literature.
Abstract: Location-allocation problems are a class of complicated optimization problems that determine the location of facilities and the allocation of customers to the facilities. In this paper, the uncapacitated continuous location-allocation problem is considered, and a particle swarm optimization approach, which has not previously been applied to this problem, is presented. Two algorithms including classical and hybrid particle swarm optimization algorithms are developed. Local optima of the problem are obtained by two local search heuristics that exist in the literature. These algorithms are combined with particle swarm optimization to construct an efficient hybrid approach. Many large-scale problems are used to measure the effectiveness and efficiency of the proposed algorithms. Our results are compared with the best algorithms in the literature. The experimental results show that the hybrid PSO produces good solutions, is more efficient than the classical PSO, and is competitive with the best results from the literature. Additionally, the proposed hybrid PSO found better solutions for some instances than did the best known solutions in the literature.

40 citations

Journal ArticleDOI
TL;DR: This paper presents a new approach to solving large-scale MILP problems that exploits either the primal or dual structures of the problems by exploiting the use of decomposition strategies.
Abstract: Many methods that have been proposed to solve large-scale MILP problems rely on the use of decomposition strategies. These methods exploit either the primal or dual structures of the problems by ap...

27 citations


Cited by
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Journal Article
TL;DR: In this paper, integer programming formulations for four types of discrete hub location problems are presented: the p-hub median problem, the uncapacitated hub location problem, p -hub center problems and hub covering problems.

727 citations

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TL;DR: A framework to classify different types of non-emergency and emergency HCFs in terms of location management is presented, and the literature based on the framework is reviewed and future research possibilities are analyzed.

322 citations

Journal ArticleDOI
TL;DR: A new evaluation system for the location selection of a CLC from a sustainability perspective using a fuzzy multi-attribute group decision making (FMAGDM) technique based on a linguistic 2-tuple to evaluate potential alternative CLC locations.
Abstract: City Logistics Centers (CLC) are an important part of the modern urban logistics system, and the selection of the location of a CLC has become a key problem in logistics and supply chain management. Integrating the economic, environmental, and social dimensions of sustainable development, this paper presents a new evaluation system for the location selection of a CLC from a sustainability perspective. A fuzzy multi-attribute group decision making (FMAGDM) technique based on a linguistic 2-tuple is used to evaluate potential alternative CLC locations. In this method, the linguistic evaluation values of all the evaluation criteria are transformed into linguistic 2-tuples. A new 2-tuple hybrid ordered weighted averaging (THOWA) operator is presented to aggregate the overall evaluation values of all experts into a collective evaluation value for each alternative, which is then used to rank and select alternative CLC locations. An application example is provided to validate the method developed and to highlight the implementation, practicality, and effectiveness by comparing with the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method.

198 citations

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TL;DR: This paper reviews local flexibility markets, which are currently being discussed and designed to provide trading platforms for local participants, including distribution system operators and aggregators, and summarizes the key elements, technologies and participants.

178 citations