4 research outputs found
A Multi-Objective Mixed-Model Assembly Line Sequencing Problem With Stochastic Operation Time
In today’s competitive market, those producers who can quickly adapt themselves todiverse demands of customers are successful. Therefore, in order to satisfy these demands of market, Mixed-model assembly line (MMAL) has an increasing growth in industry. A mixed-model assembly line (MMAL) is a type of production line in which varieties of products with common base characteristics are assembled on. This paper focuses on this type of production line in a stochastic environment with three objective functions: 1) total utility work cost, 2) total idle cost, and 3) total production rate variation cost that are simultaneously considered. In real life, especially in manual assembly lines, because of some inevitable human mistakes, breakdown of machines, lack of motivation in workers and the things alike, events are notdeterministic, sowe consideroperation time as a stochastic variable independently distributed with normal distributions; for dealing with it, chance constraint optimization is used to model the problem. At first, because of NP-hard nature of the problem, multi-objective harmony search (MOHS) algorithm is proposed to solve it. Then, for evaluating the performance of the proposed algorithm, it is compared with NSGA-II that is a powerful and famous algorithm in this area. At last, numerical examples for comparing these two algorithms with some comparing metrics are presented. The results have shown that MOHS algorithm has a good performance in our proposed model
A simheuristic for bi-objective stochastic permutation flow shop scheduling problem
This paper addresses the stochastic permutation flow shop problem (SPFSP) in which the stochastic parameters are the processing times. This allows the modeling of setups and machine breakdowns. Likewise, it is proposed a multi-objective greedy randomized adaptive search procedure (GRASP) coupled with Monte-Carlo Simulation to obtain expected makespan and expected tardiness. To manage the bi-objective function, a sequential combined method is considered in the construction phase of the meta-heuristic. Moreover, the local Search combines 2-optimal interchanges with a Pareto Archived Evolution Strategy (PAES) to obtain the Pareto front. Also, some Taillard benchmark instances of deterministic permutation flow shop problem were adapted in order to include the variation in processing times. Accordingly, two coefficients of variation (CVs) were tested: one depending on expected processing times values defined as twice the expected processing time of a job, and a fixed value of 0.25. Thus, the computational results on benchmark instances show that the variable CV provided lower values of the expected makespan and tardiness, while the con-stant CV presented higher expected measures. The computational results present insights for further analysis on the behavior of stochastic scheduling problems for a better approach in real-life scenarios at industrial and service systems
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