2020 |
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1. | Bernábe-Loranca, Beatriz; Estrada-Analco, Martin; González-Velázquez, Rogelio; Martínez-Guzman, Gerardo; Ruiz-Vanoye, Jorge A. Location-Allocation Problem: A Methodology with VNS Metaheuristic Book Chapter In: Intelligent Systems Design and Applications, vol. 941, pp. 1015-1024, Springer, Cham, 2020, ISBN: 978-3-030-16659-5. Abstract | Links | BibTeX | Tags: meta-heuristics algorithms @inbook{bc19, In this work, we present beginnings of a methodology that allows the establishment of relationships between the location of the facilities and the clients’ allocation with a dense demand. The use of this application lets us know the optimal location of production facilities, warehouses or distribution centers in a geographical space. We also solve the customers’ dense demand for goods or services; this is, finding the proper location of the facilities in a populated geographic territory, where the population has a demand for services in a constant basis. Finding the location means obtaining the decimal geographical coordinates where the facility should be located, such that the transportation of products or services costs the least. The implications and practical benefits of the results of this work have allowed an enterprise to design an efficient logistics plan in benefit of its supply chain. Firstly, the territory must be partitioned by a heuristic method, due do the combinatory nature of the partitioning. After this process, the best partition is selected with the application of factorial experiment design and the surface response methodology. Once the territory has been partitioned into k zones, where the center of each zone is the distribution center, we apply the continuous dense demand function and solve the location-allocation problem for an area where the population has a dense demand for services. |
2011 |
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2. | Ruiz-Vanoye, Jorge A.; Díaz-Parra, Ocotlán Similarities between meta-heuristics algorithms and the science of life Journal Article In: Central European Journal of Operations Research, vol. 19, no. 4, pp. 445–466, 2011, ISSN: 445–466. Abstract | Links | BibTeX | Tags: meta-heuristics algorithms, Papers in the Science Citation Index Expanded @article{JCR9, In this paper, we show the functional similarities between Meta-heuristics and the aspects of the science of life (biology): (a) Meta-heuristics based on gene transfer: Genetic algorithms (natural evolution of genes in an organic population), Transgenic Algorithm (transfers of genetic material to another cell that is not descending); (b) Meta-heuristics based on interactions among individual insects: Ant Colony Optimization (on interactions among individuals insects, Ant Colonies), Firefly algorithm (fireflies of the family Lampyridze), Marriage in honey bees Optimization algorithm (the process of reproduction of Honey Bees), Artificial Bee Colony algorithm (the process of recollection of Honey Bees); and (c) Meta-heuristics based on biological aspects of alive beings: Tabu Search Algorithm (Classical Conditioning on alive beings), Simulated Annealing algorithm (temperature control of spiders), Particle Swarm Optimization algorithm (social behavior and movement dynamics of birds and fish) and Artificial Immune System (immunological mechanism of the vertebrates). |
Publications
2020 |
|
1. | Location-Allocation Problem: A Methodology with VNS Metaheuristic Book Chapter In: Intelligent Systems Design and Applications, vol. 941, pp. 1015-1024, Springer, Cham, 2020, ISBN: 978-3-030-16659-5. |
2011 |
|
2. | Similarities between meta-heuristics algorithms and the science of life Journal Article In: Central European Journal of Operations Research, vol. 19, no. 4, pp. 445–466, 2011, ISSN: 445–466. |