Ciencias Exactas y Ciencias de la Salud

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Tailoring metaheuristics for designing thermodynamic-optimal water based cooling devices for microelectronic thermal management applications
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06) Pérez Espinosa, Guillermo; TERASHIMA MARIN, HUGO; 65879; Terashima Marín, Hugo; emipsanchez; Ortiz Bayliss, José Carlos; Aviña Cervantes, Juan Gabriel; Escuela de Ingeniería y Ciencias; Campus Monterrey; Cruz Duarte, Jorge Mario
Heat sinks provide a common and straightforward alternative to dealing with the Microelectronic Thermal Management (MTM) problem due to their simplicity of fabrication, low cost, and reliability of heat dissipation. The MTM problem is highly relevant in today's electronics industry, as new electronic devices' miniaturization and enhanced performance have increased their heat power generation. So, regarding the second law of thermodynamics, an optimal heat sink design can guarantee that the microelectronic components operate without jeopardizing their life span and performance. To solve this challenging problem, Metaheuristics~(MHs) have shown to be excellent alternatives due to their reliability, flexibility, and simplicity. Nevertheless, no single MH guarantees an overall outstanding performance. Thus, the motivation for this work is to open ample room for practitioners to find the proper solver to deal with a given problem without requiring extensive knowledge of heuristic-based optimization. This work studies the feasibility of implementing a strategy for Automatic Metaheuristic Design powered by a hyper-heuristic search to minimize the entropy generation rate of microchannel heat sinks and tailor population-based and metaphor-less MHs for solving the MTM. A mathematical model based on thermodynamic modeling via the Entropy Generation Minimization (EGM) criterion was used to obtain the value of the entropy generation rate of a rectangular microchannel heat sink according to their design. Four different scenarios were considered, varying the design specifications for the heat sinks and comparing our generated MH against seven well-known heuristic-based algorithms from the literature. The one-sided Wilcoxon signed ranked test was used to perform these comparisons. Statistical evidence was found to claim that our tailored MHs manage to outperform them, in most cases, at least in the tested scenarios. Additionally, we followed a methodology to infer which operators should be considered in a curated heuristic space to design the proper MH easily. We found that using this curated search space benefits the overall process, as the HH algorithm managed to tailor high-performing MHs faster and more consistently than its counterpart. Furthermore, insights were obtained on which HH parameters are more suitable for our search, as some can enhance the tailoring process when tuned correctly. Finally, we tested some of our best designs found to see how they perform when minor fluctuations appear on some variables, just as they occur in real-life implementations. All the experimentation processes also found that the search operators of evolutionary algorithms are well suited to solve this problem, as they compose several of our tailored MHs, and that the combination of High Thermal Conductive Graphite and water achieved the lower entropy generation rate values from the four combinations tested.
Feature transformations for improving the performance of selection hyper-heuristics on job shop scheduling problems
(Instituto Tecnológico y de Estudios Superiores de Monterrey) Garza Santisteban, Fernando; Terashima Marín, Hugo; Özcan, Ender; School of Engineering and Sciences; School of Engineering and Sciences; Campus Monterrey; Amaya Contreras, Ivan
Solving Job Shop (JS) scheduling problems is a hard combinatorial optimization problem. Nevertheless, it is one of the most present problems in real-world scheduling environments. Throughout the recent computer science history, a plethora of methods to solve this problem have been proposed. Despite this fact, the JS problem remains a challenge. The domain it- self is of interest for the industry and also many operations research problems are based on this problem. The solution to JS problems is overall beneficial to the industry by generating more efficient processes. Authors have proposed solutions to this problem using dispatch- ing rules, direct mathematical methods, meta-heuristics, among others. In this research, the application of feature transformations for the generation of improved selection constructive hyper-heuristics (HHs) is shown. There is evidence that applying feature transformations on other domains has produced promising results; Also, no previous work was found where this approach has been used for the JS domain. This thesis is presented to earn the Master’s degree in Computer Science of Tecnolo ́gico de Monterrey. The research’s main goals are: (1) the assessment of the extent to which HHs can perform better on JS problems than single heuristics, and that they are not specific to the instances used to train them; and (2), the degree to which HHs generated with feature transformations are revamped. Experiments were carried out using instances of various sizes published in the literature. The research involved profiling the set of heuristics chosen, ana- lyzing the interactions between the heuristics and feature values throughout the construction of a solution, and studying the performance of HHs without transformations and by using two transformations found in the literature. Results indicate that for the instances used, HHs were able to outperform the results achieved by single heuristics. Regarding feature transforma- tions, it was found that they induce a scaling effect to feature values throughout the solution process, which produces more stable HHs, with a median performance comparable to HHs without feature transformations, but not necessarily better. Results are conclusive in terms of the objectives of this research. Nevertheless, there are several ideas that could be explored to improve the HHs, which are outlined and discussed in the final Chapter of the thesis. The following major contributions are derived from this research: (1) applying a se- lection constructive HH approach, with feature transformations, to the JS domain; (2) the rationale behind the JS subproblem dependance in terms of the solution paths followed by the heuristics, which has a great impact in the training process of the HHs; (3) a method to deter- mine the most suitable parameters to apply feature transformations, which could be extended for other domains of combinatorial optimization problems; and (4) a framework for studying HHs in the Job Shop domain.

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