A tutorial survey of job-shop scheduling problems using genetic algorithms: Part II. Hybrid genetic search strategies

Cheng, Runwei; Gen, Mitsuo; Tsujimura, Yasuhiro

doi:10.1016/s0360-8352(99)00022-4

Cited by 142 publications

(67 citation statements)

References 3 publications

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“…For each problem, 10 experiments have been carried out. Pt ik follows discrete uniform distribution [1,10] and L i ranges from [1,50]. Also, setup time is ignored.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…This process is called CROSSOVER. For extensive review on genetic crossover operators, please refer to [10]. These new chromosomes then may be self-tuned called MUTATION with a probability MUTATION RATE (MR).…”

Section: Genetic Algorithmsmentioning

confidence: 99%

“…The penalty costs (ec i , tc i ) of the 1 st 3x3 are given as (3,7), (4, 1) and (9,10). Over ten 3x3 problems, three different scenarios: (i) No LS; (ii) Max.…”

Section: A Experiments Onementioning

confidence: 99%

“…This is one possible reason LSGAvi can obtain solutions with lower OPC. [1,10]. Moreover, due to administration policies, only lots with L i > 10 are allowed to be split and lots can only be split into S i 10 sub-lots where i = 1…n'.…”

Section: B Experiments Twomentioning

confidence: 99%

“…Moreover, due to administration policies, only lots with L i > 10 are allowed to be split and lots can only be split into S i 10 sub-lots where i = 1…n'. Also, ec i and tc i vary from [1,10] for all lot i. Noted that the existing splitting policy of the company follows LSGAei.…”

Section: B Experiments Twomentioning

confidence: 99%

See 4 more Smart Citations

Lot Streaming Technique in Job-Shop Environment

Chan¹,

Wong²,

Chan³

Proceedings of the 2005 IEEE International Symposium On, Mediterrean Conference on Control and Automation Intelligent Control,

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Applying Lot Streaming (LS) technique, a new approach is applied to determine LS conditions in Job-shop Scheduling Problem (JSP) using Genetic Algorithms (GA). LS approach allows a job (lot) to be split into a number of smaller sub-jobs (sub-lots) so that successive operations of the same job can be overlapped. In this connection, the total completion time of the whole job may be shortened. The proposed approach is developed to solve two sub-problems. The first sub-problem is called LS problem in which LS conditions are determined and another sub-problem is JSP after LS conditions are determined. A number of benchmarked problems will be investigated to study the optimum LS conditions in Just-In-Time (JIT) environment. Experiment results suggest that the model works fairly well with different objectives and good solutions can be obtained within reasonable time frame.

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“…For each problem, 10 experiments have been carried out. Pt ik follows discrete uniform distribution [1,10] and L i ranges from [1,50]. Also, setup time is ignored.…”

Section: Experiments Resultsmentioning

confidence: 99%

Section: Genetic Algorithmsmentioning

confidence: 99%

“…The penalty costs (ec i , tc i ) of the 1 st 3x3 are given as (3,7), (4, 1) and (9,10). Over ten 3x3 problems, three different scenarios: (i) No LS; (ii) Max.…”

Section: A Experiments Onementioning

confidence: 99%

Section: B Experiments Twomentioning

confidence: 99%

Section: B Experiments Twomentioning

confidence: 99%

See 3 more Smart Citations

Lot Streaming Technique in Job-Shop Environment

Chan¹,

Wong²,

Chan³

Proceedings of the 2005 IEEE International Symposium On, Mediterrean Conference on Control and Automation Intelligent Control,

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Multiprocessor Scheduling

2002

Scheduling in Real‐Time Systems

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Scheduling theory is concerned with the optimal allocation of scarce resources (for instance, machines, processors, robots, operators, etc.) to activities over time, with the objective of optimizing one or several performance measures. The study of scheduling started about fifty years ago, being initiated by seminal papers by Johnson (1954) and Bellman (1956). Since then machine scheduling theory have received considerable development. As a result, a great diversity of scheduling models and optimization techniques have been developed that found wide applications in industry, transport and communications. Today, scheduling theory is an integral, generally recognized and rapidly evolving branch of operations research, fruitfully contributing to computer science, artificial intelligence, and industrial engineering and management. The interested reader can find many nice pearls of scheduling theory in textbooks, monographs and handbooks by Tanaev et al. (1994a,b),

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Automated scheduling for clone‐based refactoring using a competent GA

et al. 2010

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Refactoring is a widely accepted technique to improve the software quality by restructuring its design without changing its behavior. In general, a sequence of refactorings needs to be applied until the quality of the code is improved satisfactorily. In this case, the final design after refactoring can vary with the application order of refactorings, thereby producing different quality improvements. Therefore, it is necessary to determine a proper refactoring schedule to obtain as many benefits as possible. However, there is little research on the problem of generating appropriate schedules to maximize quality improvement. In this paper, we propose an approach to automatically determine an appropriate schedule to maximize quality improvement through refactoring. We first detect code clones that are suitable for refactoring and generate the most beneficial refactoring schedule to remove them. It is straightforward to select the best from the exhaustively enumerated schedules. However, such a technique becomes NP‐hard, as the number of available refactorings increases. We apply a genetic algorithm (GA) to generate the best refactoring schedule within a reasonable time to cope with this problem. We compare the GA‐based approach with manual scheduling, greedy heuristic‐based, and exhaustive approaches for four open systems. The results show that the proposed GA‐based approach generates more beneficial schedules than the others. Copyright © 2010 John Wiley & Sons, Ltd.

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A tutorial survey of job-shop scheduling problems using genetic algorithms: Part II. Hybrid genetic search strategies

Cited by 142 publications

References 3 publications

Lot Streaming Technique in Job-Shop Environment

Lot Streaming Technique in Job-Shop Environment

Multiprocessor Scheduling

Automated scheduling for clone‐based refactoring using a competent GA

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