A batch splitting method for a job shop scheduling problem in an MRP environment

Jeong, Hanil; Park, Jin-Woo; Leachman, Robert C.

doi:10.1080/002075499190194

Cited by 39 publications

(17 citation statements)

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“…AN IMPROVED GENETIC ALGORITHM In this scheduling problem, a job's batch size is probably greater than one, as in the real manufacturing environment. By splitting the original batch into many smaller batches, and regarding every smaller batch as a single part, we can process these smaller batches on different resources at the same time, and the idle times of resources with jobs which have smaller processing times by splitting the original batches can be filled up [8]. Thus, an improved schedule can be obtained.…”

Section: Operations On Fuzzy Processing Timementioning

confidence: 95%

“…Li Fu-Ming et al(2006) adopted a parallel chromosome coding method, one for the machine allocation, the other for the scheduling of operations [7]. There also exists many researches about the batch-splitting scheduling problems [8,9,10]. Gur Mosheiov and Daniel Oron studied batch scheduling problem with a lower bound that all batch sizes shared and an upper bound that all batch sizes could not exceed [10].…”

Section: Introductionmentioning

confidence: 99%

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A batch splitting job shop scheduling problem with bounded batch sizes under multiple-resource constraints using genetic algorithm

Wang

Zhao

et al. 2008

2008 IEEE Conference on Cybernetics and Intelligent Systems

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Considering alternative resources for operations, requirement of multiple resources to process an operation and a job's batch size greater than one in the real manufacturing environment, a study is made on the batch splitting scheduling problem with bounded batch sizes under multiple-resource constraints, based on the objective to minimize the maximum completion time. A genetic algorithm which is suitable for this problem is proposed, with a new chromosome representation, which takes into account the batch splitting of the original batches of jobs. And a new crossover method and a new mutation method are proposed based on the new chromosome representation. The results of the simulation indicate that the method is feasible and efficient.

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Section: Operations On Fuzzy Processing Timementioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

A batch splitting job shop scheduling problem with bounded batch sizes under multiple-resource constraints using genetic algorithm

Wang

Zhao

et al. 2008

2008 IEEE Conference on Cybernetics and Intelligent Systems

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“…In contrast, LS seems not very promising in JSP and open shop problem (OSP). Even so, some studies about LS to JSP [6,7] and OSP [8] can be found. According to Trietsch and Baker [9], LS can be classified into 4 types: (I) equal size (ES) sub-jobs without intermittent idling means that jobs are split into sub-jobs with even size and are processed on the same machine continuously, (II) ES sub-jobs with intermittent idling that allows idle time between sub-jobs on the same machine, (III) varied size (VS) sub-jobs without intermittent idling means that jobs are split into sub-jobs of uneven size and there is no idle time between sub-jobs on the same machine, and (IV) VS sub-jobs with intermittent idling.…”

Section: Literature Reviewmentioning

confidence: 99%

Lot streaming for product assembly in job shop environment

Chan

Wong

Chan

2008

Robotics and Computer-Integrated Manufacturing

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“…Over years, issues related to JSP are prevalent. One common assumption of classical JSP is that jobs (lots) cannot be split into smaller jobs (sub-jobs or sub-lots) due to single lot size [1][2]. If it is relaxed, non-single unit jobs can be split.…”

Section: Introductionmentioning

confidence: 99%

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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A batch splitting method for a job shop scheduling problem in an MRP environment

Cited by 39 publications

References 0 publications

A batch splitting job shop scheduling problem with bounded batch sizes under multiple-resource constraints using genetic algorithm

A batch splitting job shop scheduling problem with bounded batch sizes under multiple-resource constraints using genetic algorithm

Lot streaming for product assembly in job shop environment

Lot Streaming Technique in Job-Shop Environment

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