Érika Yesenia Ávila-Melgar

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In this paper, a solution is presented to the unrelated parallel machines problem that minimizes the total weighted completion time. Simulated annealing is applied to the problem, which is modeled as a Weighted Bipartite Matching Problem. Experimental results with benchmarks are presented, evaluating the efficiency and efficacy of the algorithm. It is then(More)
This paper presents an experimental study of the solutions space generated by the mathematical model of the Water Distribution Network Design Problem by using Two-Looped network benchmarks to find the feasible solutions space. It shows how the performance of a typical Evolutionary Algorithm (EA) can be improved by considering the importance of working with(More)
This paper presents a parallel hybrid evolutionary algorithm executed in a grid environment. The algorithm executes local searches using simulated annealing within a Genetic Algorithm to solve the job shop scheduling problem. Experimental results of the algorithm obtained in the “Tarantula MiniGrid” are shown. Tarantula was implemented by linking two(More)
In this paper, an evolutionary algorithm, called EA-WDND, is developed to optimize water distribution network design for real instances. The evolutionary algorithm uses the Epanet Solver which, while not an optimizer, helps to evaluate the operational constraints of mass conservation, energy conservation, pressure in nodes (nodal heads) of the network, and(More)
In this paper an analogy of the Job Shop Scheduling Problem to the Hydraulic Networks Problem is presented by mapping this model of scheduling, using as a base the disjunctive graph model. The mapping carried out allows visualization of the Hydraulic Networks problem as an NP-complete model with constraints defined in the Job Shop Scheduling Problem. The(More)
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