The Experts below are selected from a list of 99 Experts worldwide ranked by ideXlab platform
Rueymaw Chen - One of the best experts on this subject based on the ideXlab platform.
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particle swarm optimization with justification and designed mechanisms for resource constrained project scheduling Problem
Expert Systems With Applications, 2011Co-Authors: Rueymaw ChenAbstract:Research highlights? This study generated schedules by both forward and backward scheduling particle swarms. ? This work applies justification technique to further shorten the makespan of the yield schedule. ? To synchronize the justified schedules, a mapping scheme is adopted to modify the particle. ? To enhance the performance, the latest finish time heuristic is used in particles' initialization. The studied resource-constrained project scheduling Problem (RCPSP) is a classical well-known Problem which involves resource, precedence, and temporal constraints and has been applied to many applications. However, the RCPSP is confirmed to be an NP-Hard Combinatorial Problem. Restated, it is Hard to be solved in a reasonable time. Therefore, there are many metaheuristics-based schemes for finding near optima of RCPSP were proposed. The particle swarm optimization (PSO) is one of the metaheuristics, and has been verified being an efficient nature-inspired algorithm for many optimization Problems. For enhancing the PSO efficiency in solving RCPSP, an effective scheme is suggested. The justification technique is combined with PSO as the proposed justification particle swarm optimization (JPSO), which includes other designed mechanisms. The justification technique adjusts the start time of each activity of the yielded schedule to further shorten the makespan. Moreover, schedules are generated by both forward scheduling particle swarm and backward scheduling particle swarm in this work. Additionally, a mapping scheme and a modified communication mechanism among particles with a designed gbest ratio (GR) are also proposed to further improve the efficiency of the proposed JPSO. Simulation results demonstrate that the proposed JPSO provides an effective and efficient approach for solving RCPSP.
Ruben Ruiz - One of the best experts on this subject based on the ideXlab platform.
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iterated greedy local search methods for unrelated parallel machine scheduling
European Journal of Operational Research, 2010Co-Authors: Luis Fanjulpeyro, Ruben RuizAbstract:This work deals with the parallel machine scheduling Problem which consists in the assignment of n jobs on m parallel machines. The most general variant of this Problem is when the processing time depends on the machine to which each job is assigned to. This case is known as the unrelated parallel machine Problem. Similarly to most of the literature, this paper deals with the minimization of the maximum completion time of the jobs, commonly referred to as makespan (Cmax). Many algorithms and methods have been proposed for this Hard Combinatorial Problem, including several highly sophisticated procedures. By contrast, in this paper we propose a set of simple iterated greedy local search based metaheuristics that produce solutions of very good quality in a very short amount of time. Extensive computational campaigns show that these solutions are, most of the time, better than the current state-of-the-art methodologies by a statistically significant margin.
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two new robust genetic algorithms for the flowshop scheduling Problem
Omega-international Journal of Management Science, 2006Co-Authors: Ruben Ruiz, Concepcion Maroto, Javier AlcarazAbstract:The flowshop scheduling Problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic algorithms are not suitable for this Hard, Combinatorial Problem unless hybridization is used. This work proposes new genetic algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known algorithms. The optimization criterion considered is the minimization of the total completion time or makespan (Cmax). We show a robust genetic algorithm and a fast hybrid implementation. These algorithms use new genetic operators, advanced techniques like hybridization with local search and an efficient population initialization as well as a new generational scheme. A complete evaluation of the different parameters and operators of the algorithms by means of a Design of Experiments approach is also given. The algorithm's effectiveness is compared against 11 other methods, including genetic algorithms, tabu search, simulated annealing and other advanced and recent techniques. For the evaluations we use Taillard's well known standard benchmark. The results show that the proposed algorithms are very effective and at the same time are easy to implement.
Bryan A Norman - One of the best experts on this subject based on the ideXlab platform.
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multi objective tabu search using a multinomial probability mass function
European Journal of Operational Research, 2006Co-Authors: Sadan Kulturelkonak, Alice E Smith, Bryan A NormanAbstract:A tabu search approach to solve multi-objective Combinatorial optimization Problems is developed in this paper. This procedure selects an objective to become active for a given iteration with a multinomial probability mass function. The selection step eliminates two major Problems of simple multi-objective methods, a priori weighting and scaling of objectives. Comparison of results on an NP-Hard Combinatorial Problem with a previously published multi-objective tabu search approach and with a deterministic version of this approach shows that the multinomial approach is effective, tractable and flexible.
Javier Alcaraz - One of the best experts on this subject based on the ideXlab platform.
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two new robust genetic algorithms for the flowshop scheduling Problem
Omega-international Journal of Management Science, 2006Co-Authors: Ruben Ruiz, Concepcion Maroto, Javier AlcarazAbstract:The flowshop scheduling Problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic algorithms are not suitable for this Hard, Combinatorial Problem unless hybridization is used. This work proposes new genetic algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known algorithms. The optimization criterion considered is the minimization of the total completion time or makespan (Cmax). We show a robust genetic algorithm and a fast hybrid implementation. These algorithms use new genetic operators, advanced techniques like hybridization with local search and an efficient population initialization as well as a new generational scheme. A complete evaluation of the different parameters and operators of the algorithms by means of a Design of Experiments approach is also given. The algorithm's effectiveness is compared against 11 other methods, including genetic algorithms, tabu search, simulated annealing and other advanced and recent techniques. For the evaluations we use Taillard's well known standard benchmark. The results show that the proposed algorithms are very effective and at the same time are easy to implement.
Hachemi Bennaceur - One of the best experts on this subject based on the ideXlab platform.
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when constraint programming and local search solve the scheduling Problem of electricite de france nuclear power plant outages
Lecture Notes in Computer Science, 2006Co-Authors: Mohand Ou Idir Khemmoudj, Marc Porcheron, Hachemi BennaceurAbstract:The French nuclear park comprises 58 nuclear reactors distributed through the national territory on 19 geographical sites. They must be repeatedly stopped, for refueling and maintenance. The scheduling of these outages has to comply with various constraints, regarding safety, maintenance logistic, and plant operation, whilst it must contribute to the producer profit maximization. This industrial Problem appears to be a Hard Combinatorial Problem that conventional methods used up to now by Electricite de France (mainly based on Mixed Integer Programming) fail to solve properly. We present in this paper a new approach for modeling and solving this Problem, combining Constraint Programming (CP) and Local Search. CP is used to find solutions to the outage scheduling Problem, while Local Search is used to improve solutions with respect to a heuristic cost criterion. It leads to find solutions as good as with the conventional approaches, but taking into account all the constraints and in very reduced computing time.
