Journal ArticleDOI
Co-evolutionary genetic algorithm for fuzzy flexible job shop scheduling
Deming Lei
- Vol. 12, Iss: 8, pp 2237-2245
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TLDR
An effective co-evolutionary genetic algorithm (CGA) is developed for the minimization of fuzzy makespan and Computational results show that CGA outperforms those algorithms compared.Abstract:
Fuzzy flexible job shop scheduling problem (FfJSP) is the combination of fuzzy scheduling and flexible scheduling in job shop environment, which is seldom investigated for its high complexity. We developed an effective co-evolutionary genetic algorithm (CGA) for the minimization of fuzzy makespan. In CGA, the chromosome of a novel representation consists of ordered operation list and machine assignment string, a new crossover operator and a modified tournament selection are proposed, and the population of job sequencing and the population of machine assignment independently evolve and cooperate for converging to the best solutions of the problem. CGA is finally applied and compared with other algorithms. Computational results show that CGA outperforms those algorithms compared.read more
Citations
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Journal ArticleDOI
A research survey: review of flexible job shop scheduling techniques
Imran Ali Chaudhry,Abid Ali Khan +1 more
TL;DR: The paper aims at presenting the development of flexible JSS and a consolidated survey of various techniques that have been employed since 1990 for problem resolution.
Journal ArticleDOI
A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems
TL;DR: The mathematical model of FJSP is presented, the constraints in applications are summarized, and the encoding and decoding strategies for connecting the problem and algorithms are reviewed to give insight into future research directions.
Journal ArticleDOI
A research survey: review of AI solution strategies of job shop scheduling problem
Banu Çaliş,Serol Bulkan +1 more
TL;DR: This paper focus on artificial intelligence approaches to NP-hard job shop scheduling (JSS) problem and successful approaches of artificial intelligence techniques such as neural network, genetic algorithm, multi agent systems, simulating annealing, bee colony optimization, ant colony optimization and particle swarm algorithm are presented.
Journal ArticleDOI
A Self-Learning Genetic Algorithm based on Reinforcement Learning for Flexible Job-shop Scheduling Problem
TL;DR: A self-learning genetic algorithm (SLGA) is proposed in this paper, in which genetic algorithm [GA] is adopted as the basic optimization method and its key parameters are intelligently adjusted based on reinforcement learning (RL).
Journal ArticleDOI
An effective teaching-learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time
TL;DR: In this paper, an effective teaching–learning-based optimization algorithm (TLBO) is proposed to solve the flexible job-shop problem with fuzzy processing time (FJSPF).
References
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Journal ArticleDOI
A genetic algorithm for the Flexible Job-shop Scheduling Problem
TL;DR: A genetic algorithm for the Flexible Job-shop Scheduling Problem (FJSP) integrates different strategies for generating the initial population, selecting the individuals for reproduction and reproducing new individuals to prove that genetic algorithms are effective for solving FJSP.
Journal ArticleDOI
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Weijun Xia,Zhiming Wu +1 more
TL;DR: The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the multi-objective FJSP, especially for problems on a large scale.
Journal ArticleDOI
Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic
TL;DR: This paper proposes a Pareto approach based on the hybridization of fuzzy logic (FL) and evolutionary algorithms (EAs) to solve the flexible job-shop scheduling problem (FJSP).
Journal ArticleDOI
A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems
Jie Gao,Linyan Sun,Mitsuo Gen +2 more
TL;DR: This paper developed a hybrid genetic algorithm (GA) that uses two vectors to represent solutions and developed an efficient method to find assignable time intervals for the deleted operations based on the concept of earliest and latest event time.
Journal ArticleDOI
Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems
Joc Cing Tay,Nhu Binh Ho +1 more
TL;DR: Experimental results show that composite dispatching rules generated by the genetic programming framework outperforms the single dispatches rules and composite dispatch rules selected from literature over five large validation sets with respect to minimum makespan, mean tardiness, and mean flow time objectives.