A genetic algorithm for job-shop scheduling in a semiconductor manufacturing system

Cavalieri, Salvatore; Crisafulli, Francisco J.; Mirabella, O.

doi:10.1109/iecon.1999.816541

Cited by 12 publications

(7 citation statements)

References 4 publications

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“…Thus, the genetic algorithms are more general and powerful techniques in neighborhood searches than others. Cavalieri et al [38] presented an interesting application of genetic algorithms to solving a job shop scheduling problem in a semiconductor manufacturing system at ST Microelectronics plant. The algorithm guarantees the compatibility of the solution with online scheduling with a clearly better performance than that of the FIFO approach currently used in the plant.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Job shop scheduling techniques in semiconductor manufacturing

Gupta‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Sivakumar

2004

Int J Adv Manuf Technol

120

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This paper presents a brief review on job shop scheduling techniques in semiconductor manufacturing. The manufacturing environment in a semiconductor industry is considered a highly complex job shop, involving multiple types of work centers, large and changing varieties of products, sequencedependent setup times, reentrant process flow, etc., in a dynamic scheduling environment. Due to the stubborn nature of the deterministic job shop scheduling problem itself, many of the solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem-specific strategies. In this paper, we seek to provide a brief overview of the problem, the techniques used and the researchers involved in solving this problem.

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Section: Genetic Algorithmmentioning

confidence: 99%

Job shop scheduling techniques in semiconductor manufacturing

Gupta‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Sivakumar

2004

Int J Adv Manuf Technol

120

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“…the branch and bound method [4], and Lagrangian relaxation-based approaches [5], etc. In addition, meta-heuristics methods may be used to solve manufacturing scheduling problems by making incremental changes (termed as permutation), such as genetic algorithm [6] and simulated annealing [7], etc. All of these methods are difficult to apply to actual Proceeding of the 2006 IEEE International Conference on Automation Science and Engineering Shanghai, China, October 7-10, 2006 manufacturing scheduling problems because of the high computational cost.…”

Section: B Literature Reviewmentioning

confidence: 99%

A fast rescheduling method in semiconductor manufacturing allowing for tardiness and scheduling stability

Cheng¹,

Sugi²,

Ota³

et al. 2006

2006 IEEE International Conference on Automation Science and Engineering

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In semiconductor manufacturing, rescheduling problems are extremely difficult to solve in real time due to the high frequency of disturbances that occur approximately every minute. This paper proposes a new approach to online manufacturing rescheduling. Unlike the traditional methods to have a scheduling process again, we (1) revise the existing schedule to keep high schedule stability based on message passing rescheduling with operation sorting; and (2) during the manufacturing process, improve the performance of the revised schedule with the introduction of a quick local search on semi-critical paths. In actual problems with about 200,000 processes, this method can effectively accommodate disturbances in less than 1 second, and a better schedule can be obtained in less than 1 minute. This method has been demonstrated to be more effective than conventional dispatching-rule methods, some of which have been actually applied in many facilities, because it offers higher schedule stability and fewer violations of due dates.

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“…The scheduling goal of on-time delivery for individual parts is modeled as penalties on part delivery tardiness , where is the completion time of part . The scheduling problem then boils down to (4) subject to constraints (1)- (3). Note that this formulation falls into the class of separable optimization problem.…”

Section: Job-shop Scheduling Using Lrmentioning

confidence: 99%

“…Good scheduling leads to increased efficiency, utilization, and, ultimately, profitability. There have been many efforts made toward developing good scheduling systems for semiconductor manufacturing [1]- [4] and other industries [5]- [8], and the effective resolution of scheduling problems has resulted in significant savings. For example, a scheduling system reported in [5] is estimated to save over a million dollars a year for a major steel company.…”

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confidence: 99%