Diseño de una metaheurística GRASP hibridizada con la metodología PAES y la simulación de Monte Carlo en un ambiente Flexible Flow Shop estocástico multi-objetivo

El propósito de este proyecto es estudiar un problema de programación de la producción multiobjetivo en un ambiente Flexible Flow Shop (FFS) estocástico. Los objetivos a minimizar son el valor esperado de la tardanza, la desviación estándar de la tardanza, el valor esperado del tiempo total de terminación y la desviación estándar del tiempo total de terminación. Los parámetros estocásticos son los tiempos entre fallas de las máquinas y los tiempos de reparación de las máquinas. Como método de solución, se propone una simheurística, la cual hibridiza la metaheurística GRASP con la simulación de Monte Carlo y el algoritmo PAES para obtener la frontera de Pareto. Inicialmente, se realiza un diseño experimental de la versión determinística del problema para evaluar el desempeño de la simheurística, comparando los resultados de la simheurística con el tiempo total de terminación obtenido en la programación de los trabajos con la regla de despacho FL, y la tardanza con la regla de despacho ENS2. Un segundo diseño de experimentos es diseñado para evaluar los efectos de los diferentes coeficientes de variación y la distribución de probabilidad para ambos parámetros estocásticos en las cuatro funciones objetivo del caso estocástico. Para el caso estocástico, los resultados arrojaron que ambas distribuciones de probabilidad y coeficientes de variación tienen un efecto significativo en las variables, lo que demuestra la importancia de un ajuste preciso de las distribuciones de probabilidad para obtener soluciones adecuadas.To achieve a higher level of efficiency within a manufacturing industry, the production scheduling is essential, because this process is crucial for the maximization of the business value. Currently, a big part of literature in scheduling is focused on solving a deterministic problem to minimize the makespan. Given that, realistically, the industry is exposed to random events that can affect its performance, the aim of this project is to study a multi-objective stochastic Flexible Flow Shop (FFS) environment. The objectives to minimize are expected value of tardiness, standard deviation of tardiness, expected value of total completion time (equal to flowtime due to release times are zero) and standard deviation of total completion time. The stochastics parameters are the times between failures and times to repair the machines (duration of machine breakdowns). As solution method, a simheuristic is proposed, which hybridizes the metaheuristic Greedy Randomized Adaptive Search Procedures (GRASP) with the Monte Carlo simulation and Pareto Archived Evolution Strategy (PAES) algorithm to obtain the Pareto frontier (see illustration 2). A first experimental design is done to test the simheuristic performance for the deterministic version (see illustration 1) of the problem by comparing the results of the simheuristic with the flowtime obtained by scheduling the jobs with FL dispatching rule, and the tardiness with the ENS2 dispatching rule. A second design of experiments is designed to evaluate the effects of different coefficients of variation and probability distribution of both stochastic parameters in the four objective functions of the stochastic case. To do both experimental designs 324 benchmark instances were evaluated in both cases. Results show, that for the deterministic case, the metaheuristic presents an average improvement of 3% in flowtime against FL rule, 2% in tardiness against ENS2 rule. For the stochastic case, results show that both probability distributions and coefficient of variation have a significant effect in the four response variables, which shows the importance of an accurate fitting of probability distributions to obtain adequate solutions.Ingeniero (a) IndustrialPregrad

A general Framework for Utilizing Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A concise overview

Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of metaheuristic optimization algorithms to address the unrelated parallel machine scheduling problem (UPMSP) through the lens of sustainable development goals (SDGs). The primary objective of this study is to explore how metaheuristic optimization algorithms can contribute to achieving sustainable development goals in the context of UPMSP. We examine a range of metaheuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and more, and assess their effectiveness in optimizing the scheduling problem. The algorithms are evaluated based on their ability to improve resource utilization, minimize energy consumption, reduce environmental impact, and promote socially responsible production practices. To conduct a comprehensive analysis, we consider UPMSP instances that incorporate sustainability-related constraints and objectives