The Experts below are selected from a list of 10317 Experts worldwide ranked by ideXlab platform
Luiz Satoru Ochi - One of the best experts on this subject based on the ideXlab platform.
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a hybrid heuristic based on iterated Local Search for multivehicle inventory routing problem
Electronic Notes in Discrete Mathematics, 2016Co-Authors: Edcarllos Santos, Luiz Satoru Ochi, Luidi Simonetti, Pedro Henrique GonzalezAbstract:Abstract We study a multivehicle inventory routing problem (MIRP) in which supplier delivers one type of product along a finite planning horizon, using a homogeneous fleet of vehicles. The main objective is to minimize the total cost of storage and transportation. In order to solve MIRP, we propose an algorithm based on iterated Local Search (ILS) metaheuristic, using a variable neighborhood descent with random neighborhood ordering (RVND) in the Local Search Phase. Moreover, we combined this algorithm with an exact procedure based on mathematical programming to solve specifically the inventory management as a subproblem. To validate our approach, computational tests were performed on 560 benchmark instances, achieving very competitive results in comparison to the best known algorithms.
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a hybrid iterated Local Search and variable neighborhood descent heuristic applied to the cell formation problem
Expert Systems With Applications, 2015Co-Authors: Ivan C Martins, Rian G S Pinheiro, Fabio Protti, Luiz Satoru OchiAbstract:We propose a new heuristic algorithm for the Cell Formation Problem.The algorithm is based on Iterated Local Search with Variable Neighborhood Descent.Our method finds several optimal solutions for benchmark instances from literature.Our method improves solutions for instances with unknown optimal values. The Cell Formation Problem is an NP-hard optimization problem that consists of grouping machines into cells dedicated to producing a family of product parts, so that each cell operates independently and inter-cellular movements are minimized. Due to its high computational complexity, several heuristic methods have been developed over the last decades. Hybrid methods based on adaptations of popular metaheuristic techniques have shown to provide good performance in terms of solution quality. This paper proposes a new approach for solving the Cell Formation Problem using the group efficacy objective function. Our method is based on the Iterated Local Search metaheuristic coupled with a variant of the Variable Neighborhood Descent method that uses a random ordering of neighborhoods in Local Search Phase. We consider two types of constraints on the minimum cell size, comparing them with several well-known algorithms in the literature. Computational experiments have been performed on 35 widely used benchmark instances with up to 40 machines and 100 parts. The proposed algorithm, besides obtaining solutions at least as good as any reported results, was able to find several optimal solutions and improve the group efficacy for some instances with unknown optima.
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an iterated Local Search heuristic for the heterogeneous fleet vehicle routing problem
Journal of Heuristics, 2013Co-Authors: Puca Huachi Vaz Penna, Anand Subramanian, Luiz Satoru OchiAbstract:This paper deals with the Heterogeneous Fleet Vehicle Routing Problem (HFVRP). The HFVRP is $\mathcal{NP}$ -hard since it is a generalization of the classical Vehicle Routing Problem (VRP), in which clients are served by a heterogeneous fleet of vehicles with distinct capacities and costs. The objective is to design a set of routes in such a way that the sum of the costs is minimized. The proposed algorithm is based on the Iterated Local Search (ILS) metaheuristic which uses a Variable Neighborhood Descent procedure, with a random neighborhood ordering (RVND), in the Local Search Phase. To the best of our knowledge, this is the first ILS approach for the HFVRP. The developed heuristic was tested on well-known benchmark instances involving 20, 50, 75 and 100 customers. These test-problems also include dependent and/or fixed costs according to the vehicle type. The results obtained are quite competitive when compared to other algorithms found in the literature.
Mauricio G C Resende - One of the best experts on this subject based on the ideXlab platform.
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an annotated bibliography of grasp part ii applications
International Transactions in Operational Research, 2009Co-Authors: Paola Festa, Mauricio G C ResendeAbstract:A greedy randomized adaptive Search procedure (GRASP) is a metaheuristic for combinatorial optimization. It is a multi-start or iterative process, in which each GRASP iteration consists of two Phases, a construction Phase, in which a feasible solution is produced, and a Local Search Phase, in which a Local optimum in the neighborhood of the constructed solution is sought. Since 1989, numerous papers on the basic aspects of GRASP, as well as enhancements to the basic metaheuristic, have appeared in the literature. GRASP has been applied to a wide range of combinatorial optimization problems, ranging from scheduling and routing to drawing and turbine balancing. This is the second of two papers with an annotated bibliography of the GRASP literature from 1989 to 2008. In the companion paper, algorithmic aspects of GRASP are surveyed. In this paper, we cover the literature where GRASP is applied to scheduling, routing, logic, partitioning, location, graph theory, assignment, manufacturing, transportation, telecommunications, biology and related fields, automatic drawing, power systems, and VLSI design.
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an annotated bibliography of grasp part i algorithms
International Transactions in Operational Research, 2009Co-Authors: Paola Festa, Mauricio G C ResendeAbstract:A greedy randomized adaptive Search procedure (GRASP) is a metaheuristic for combinatorial optimization. It is a multi-start or iterative process, in which each GRASP iteration consists of two Phases, a construction Phase, in which a feasible solution is produced, and a Local Search Phase, in which a Local optimum in the neighborhood of the constructed solution is sought. Since 1989, numerous papers on the basic aspects of GRASP, as well as enhancements to the basic metaheuristic have appeared in the literature. GRASP has been applied to a wide range of combinatorial optimization problems, ranging from scheduling and routing to drawing and turbine balancing. This is the first of two papers with an annotated bibliography of the GRASP literature from 1989 to 2008. This paper covers algorithmic aspects of GRASP.
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grasp an annotated bibliography
2002Co-Authors: Paola Festa, Mauricio G C ResendeAbstract:A greedy randomized adaptive Search procedure (GRASP) is a metaheuristic for combinatorial optimization. It is a multi-start or iterative process, in which each GRASP iteration consists of two Phases, a construction Phase, in which a feasible solution is produced, and a Local Search Phase, in which a Local optimum in the neighborhood of the constructed solution is sought. Since 1989, numerous papers on the basic aspects of GRASP, as well as enhancements to the basic metaheuristic have appeared in the literature. GRASP has been applied to a wide range of combinatorial optimization problems, ranging from scheduling and routing to drawing and turbine balancing. This paper is an annotated bibliography of the GRASP literature from 1989 to 2001.
Ivan C Martins - One of the best experts on this subject based on the ideXlab platform.
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a hybrid iterated Local Search and variable neighborhood descent heuristic applied to the cell formation problem
Expert Systems With Applications, 2015Co-Authors: Ivan C Martins, Rian G S Pinheiro, Fabio Protti, Luiz Satoru OchiAbstract:We propose a new heuristic algorithm for the Cell Formation Problem.The algorithm is based on Iterated Local Search with Variable Neighborhood Descent.Our method finds several optimal solutions for benchmark instances from literature.Our method improves solutions for instances with unknown optimal values. The Cell Formation Problem is an NP-hard optimization problem that consists of grouping machines into cells dedicated to producing a family of product parts, so that each cell operates independently and inter-cellular movements are minimized. Due to its high computational complexity, several heuristic methods have been developed over the last decades. Hybrid methods based on adaptations of popular metaheuristic techniques have shown to provide good performance in terms of solution quality. This paper proposes a new approach for solving the Cell Formation Problem using the group efficacy objective function. Our method is based on the Iterated Local Search metaheuristic coupled with a variant of the Variable Neighborhood Descent method that uses a random ordering of neighborhoods in Local Search Phase. We consider two types of constraints on the minimum cell size, comparing them with several well-known algorithms in the literature. Computational experiments have been performed on 35 widely used benchmark instances with up to 40 machines and 100 parts. The proposed algorithm, besides obtaining solutions at least as good as any reported results, was able to find several optimal solutions and improve the group efficacy for some instances with unknown optima.
Fabio Protti - One of the best experts on this subject based on the ideXlab platform.
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a hybrid iterated Local Search and variable neighborhood descent heuristic applied to the cell formation problem
Expert Systems With Applications, 2015Co-Authors: Ivan C Martins, Rian G S Pinheiro, Fabio Protti, Luiz Satoru OchiAbstract:We propose a new heuristic algorithm for the Cell Formation Problem.The algorithm is based on Iterated Local Search with Variable Neighborhood Descent.Our method finds several optimal solutions for benchmark instances from literature.Our method improves solutions for instances with unknown optimal values. The Cell Formation Problem is an NP-hard optimization problem that consists of grouping machines into cells dedicated to producing a family of product parts, so that each cell operates independently and inter-cellular movements are minimized. Due to its high computational complexity, several heuristic methods have been developed over the last decades. Hybrid methods based on adaptations of popular metaheuristic techniques have shown to provide good performance in terms of solution quality. This paper proposes a new approach for solving the Cell Formation Problem using the group efficacy objective function. Our method is based on the Iterated Local Search metaheuristic coupled with a variant of the Variable Neighborhood Descent method that uses a random ordering of neighborhoods in Local Search Phase. We consider two types of constraints on the minimum cell size, comparing them with several well-known algorithms in the literature. Computational experiments have been performed on 35 widely used benchmark instances with up to 40 machines and 100 parts. The proposed algorithm, besides obtaining solutions at least as good as any reported results, was able to find several optimal solutions and improve the group efficacy for some instances with unknown optima.
Rian G S Pinheiro - One of the best experts on this subject based on the ideXlab platform.
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a hybrid iterated Local Search and variable neighborhood descent heuristic applied to the cell formation problem
Expert Systems With Applications, 2015Co-Authors: Ivan C Martins, Rian G S Pinheiro, Fabio Protti, Luiz Satoru OchiAbstract:We propose a new heuristic algorithm for the Cell Formation Problem.The algorithm is based on Iterated Local Search with Variable Neighborhood Descent.Our method finds several optimal solutions for benchmark instances from literature.Our method improves solutions for instances with unknown optimal values. The Cell Formation Problem is an NP-hard optimization problem that consists of grouping machines into cells dedicated to producing a family of product parts, so that each cell operates independently and inter-cellular movements are minimized. Due to its high computational complexity, several heuristic methods have been developed over the last decades. Hybrid methods based on adaptations of popular metaheuristic techniques have shown to provide good performance in terms of solution quality. This paper proposes a new approach for solving the Cell Formation Problem using the group efficacy objective function. Our method is based on the Iterated Local Search metaheuristic coupled with a variant of the Variable Neighborhood Descent method that uses a random ordering of neighborhoods in Local Search Phase. We consider two types of constraints on the minimum cell size, comparing them with several well-known algorithms in the literature. Computational experiments have been performed on 35 widely used benchmark instances with up to 40 machines and 100 parts. The proposed algorithm, besides obtaining solutions at least as good as any reported results, was able to find several optimal solutions and improve the group efficacy for some instances with unknown optima.
