A parallel genetic algorithm for multi-objective flexible flowshop scheduling in pasta manufacturing

Ke, Shen; Pessemier, Toon De; Martens, Luc; Joseph, Wout

doi:10.1016/j.cie.2021.107659

Cited by 24 publications

(5 citation statements)

References 49 publications

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“…In the process of comprehensive evaluation, weighting is generally carried out according to the importance of the index [14] . Through certain calculation steps, the comprehensive evaluation scores of different schemes can be obtained, and the scheme with high scores will be finally adopted, that is, the optimal solution will be obtained [15] .…”

Section: Brief Introduction Of Comprehensive Evaluation Methodsmentioning

confidence: 99%

Multi-objective optimization problem based on non-dominated sorting genetic algorithm

Zhang

2023

Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022)

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Multi-objective optimization problem is a very common problem in mathematical modeling and daily research [1]. It belongs to the planning problem, and its ultimate goal is to find the optimal solution under certain constraints [2]. In order to solve the multi-objective programming problem, we usually use the multi-objective programming function in MATLAB, or take the traditional intelligent algorithm to solve, but the traditional algorithm is easy to fall into local optimal solution, and the error is large, which cannot meet the actual needs [3]. Therefore, this paper proposes an improved genetic algorithm, that is, non-dominated sorting genetic algorithm (NSGA-II) to solve multi-objective optimization problems, in order to better meet the research and practical needs [4]. In order to verify its superiority, this paper uses two examples, by comparing the results of the traditional algorithm and the improved genetic algorithm, using the comprehensive evaluation method to analyze, and finally draws the conclusion that the improved genetic algorithm is superior to the traditional algorithm.

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Section: Brief Introduction Of Comprehensive Evaluation Methodsmentioning

confidence: 99%

Multi-objective optimization problem based on non-dominated sorting genetic algorithm

Zhang

2023

Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022)

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“…In the article by Shen et al (2021), the two-phase genetic algorithm (CTGA) is applied to the case of a generalized flexible flow shop problem (GFFS) while introducing mixed-integer multi-objective mixed-integer programming (MIP) for the GFFS, then the observation that the CTGA is efficient for the multiobjective optimization due to the flexibility provided by the Genetic algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Job Shop Scheduling Problem by Genetic Algorithms: Case Study

SAHAR,

HERROU,

SEKKAT

2023

Management and Production Engineering Review

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The Job Shop scheduling problem is widely used in industry and has been the subject of study by several researchers with the aim of optimizing work sequences. This case study provides an overview of genetic algorithms, which have great potential for solving this type of combinatorial problem. The method will be applied manually during this study to understand the procedure and process of executing programs based on genetic algorithms. This problem requires strong decision analysis throughout the process due to the numerous choices and allocations of jobs to machines at specific times, in a specific order, and over a given duration. This operation is carried out at the operational level, and research must find an intelligent method to identify the best and most optimal combination. This article presents genetic algorithms in detail to explain their usage and to understand the compilation method of an intelligent program based on genetic algorithms. By the end of the article, the genetic algorithm method will have proven its performance in the search for the optimal solution to achieve the most optimal job sequence scenario.

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“…In the article by Ke Shen et al [1], the two-phase genetic algorithm (CTGA) is applied to the case of a generalized flexible flowshop problem (GFFS) while introducing mixed-integer multi-objective mixed-integer programming (MIP) for the GFFS, then the observation that the CTGA is efficient for the multi-objective optimization due to the flexibility provided by the Genetic algorithm. the work of Sarahí Báez et al [2] have the same objective as our article, it aims to study a machine scheduling problem and the criterion used to assess the quality of the planning is the makespan, except that the method used to solve this problem is based on hybrid algorithms which combines GRASP and variable neighborhood search metaheuristics unlike our work which will mainly focus on genetic algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Job Shop Scheduling Problem Using Genetic Algorithms

Habbadi¹,

Herrou²,

SEKKAT³

2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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The Job Shop scheduling problem is very widespread considering its utility in the industry. Several researchers have worked on this subject with the aim of optimizing work sequences. This case study provides an overview on genetic algorithms which present a real potential for solving this type of combinatorial problem of job shop scheduling problem. During this study, the application of this method will be done manually in order to understand the procedure and the process of executing programs based on genetic algorithms. It is a problem where the decision analysis must figure strongly along the process because of the numerous choices and allocations of jobs to machines at the right time, in a very specific order and over a given duration. This operation is done at the operational level and research must find an intelligent method to find the best and most optimal combination.

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A parallel genetic algorithm for multi-objective flexible flowshop scheduling in pasta manufacturing

Cited by 24 publications

References 49 publications

Multi-objective optimization problem based on non-dominated sorting genetic algorithm

Multi-objective optimization problem based on non-dominated sorting genetic algorithm

Optimization of Job Shop Scheduling Problem by Genetic Algorithms: Case Study

Job Shop Scheduling Problem Using Genetic Algorithms

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