Scheduling Modeling
Ali Husseinzadeh Kashan; Saeed Afkhami; Parisa Maroofkhani
Abstract
Purpose: In this research due to the importance of the U-shaped assembly line balancing and, on the other hand, the importance of human factors and setup times, we want to develop a bi-objective mathematical model minimize the cycle time and the total cost.Methodology: Since the research problem is shown ...
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Purpose: In this research due to the importance of the U-shaped assembly line balancing and, on the other hand, the importance of human factors and setup times, we want to develop a bi-objective mathematical model minimize the cycle time and the total cost.Methodology: Since the research problem is shown to be NP-hard, NSGA-II, which is a population-based algorithm, and also SPEA-II are used to solve the problem.Findings: A mathematical model for the problem on hand is developed. We solve the problem using NSGA-II and SPEA-II. We use four criteria for analyzing the results of the mathematical model and evaluating the performance of the multi-objective evolutionary algorithms. The experimental results demonstrate that NSGA-II is superior to SPEA-II.Originality/Value: A bi-objective mathematical model for the U-shaped assembly line balancing problem considering setup-times and workers' skill is developed, and the problem is solved using two algorithms.
Scheduling Modeling
Roja Ruhbakhsh; Esmaeil Mehdizadeh; Mohammad Amin Adibi
Abstract
Purpose: Lot streaming, which has much attention in recent years, is an effective technique to increase production efficiency in a production system by splitting a job into several smaller parts in a multi-stage production system. But important assumptions that exist in the real-world scheduling environment ...
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Purpose: Lot streaming, which has much attention in recent years, is an effective technique to increase production efficiency in a production system by splitting a job into several smaller parts in a multi-stage production system. But important assumptions that exist in the real-world scheduling environment are always ignored. Hence, in this paper, these assumptions are discussed and the results are reviewed. In this paper, the aim is solving a multi objective mathematical model for solving hybrid flow shop scheduling problem with lot-streaming, setup time and transportation time.Methodology: At first, a multi objective mathematical programming model is presented for solving the problem. Then, by wighting method, the multi objective model convert to single objective model and GAMS software is used to solve the small size problems to show the performance of the mathematical mothel. Inspired by previous studies, two multi objective metaheuristic algorithms based on the genetic algorithm is used to solve the large-scale problems. To illustrate the performance of the proposed metaheuristic algorithms, the obtained results of the algorithms compared with GAMS outputs in single mode.Findings: To validate the proposed model, a sample is solved using GAMS software and compared with the genetic algorithm. The obtained results show the performance of the mathematical model. Then, two proposed algorithms are used to solve the large-scale problems. For this purpose, 30 instance problems are randomly generated and six indicators are used to compare the algorithms. After performing the experiments and comparing the algorithms with each other, the results show NRGA algorithm performs bether than NSGA-II.Originality/Value: In this paper, for solving a multi objective hybrid flow shop scheduling problem with lot-streamingm mathematical model with the aim of minimizing the makespan and total tardiness, the sequence-dependent setup time and the transportation time constraints between consecutive stages are considered. Since the problem is NP-hard, NSGA-II and NRGA algorithms were used to solve the proposed problem.