A real-time production scheduler for a stochastic manufacturing environment

Davis, Wayne J.; Jones, Albert

doi:10.1080/09511928808944350

Cited by 60 publications

(32 citation statements)

References 16 publications

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“…An integer center C of the segment [p L i , p U i ] was generated using a uniform distribution in the range [1,100]. The lower bound p L i of the possible processing time p i was determined using the equality …”

Section: For the Benchmark Instances 1|pmentioning

confidence: 99%

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Single Machine Scheduling Problem with Interval Processing Times and Total Completion Time Objective

Sotskov

2018

Algorithms

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Abstract:We consider a single machine scheduling problem with uncertain durations of the given jobs. The objective function is minimizing the sum of the job completion times. We apply the stability approach to the considered uncertain scheduling problem using a relative perimeter of the optimality box as a stability measure of the optimal job permutation. We investigated properties of the optimality box and developed algorithms for constructing job permutations that have the largest relative perimeters of the optimality box. Computational results for constructing such permutations showed that they provided the average error less than 0.74% for the solved uncertain problems.

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Section: For the Benchmark Instances 1|pmentioning

confidence: 99%

“…In a stochastic approach, job processing times are assumed to be random variables with the known probability distributions [1,2]. If one has no sufficient information to characterize the probability distribution of all random processing times, other approaches are needed [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Single Machine Scheduling Problem with Interval Processing Times and Total Completion Time Objective

Sotskov

2018

Algorithms

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“…Davis and Jones [DAV88] used this approach to deconpose scheduling into a two-level decision-making problem (see Figure 6) . They use both pricedirected and goal-directed methods to ensure coordination of objective functions and constraints.…”

Section: 1mentioning

confidence: 99%

“…It will then use that plan, together with start and finish times from its supervisor, to determine an exact schedule. The framework outlined in [DAV88] will be used to distribute both of these decisions across the AME^shop floor hierarchy.…”

Section: The Amrf Approachmentioning

confidence: 99%

The cell as part of a manufacturing system

Jones¹

1990

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“…An on-line simulation methodology is proposed by Davis and Jones [6] to analyze several scheduling rules in a stochastic job-shop. Nof compare three types of automatic recovery procedures, such as rerouting, splitting orders and rescheduling, when disruption events occur.…”

Section: Introductionmentioning

confidence: 99%

Match-up scheduling under a machine breakdown

Aktürk

Gorgulu

1999

European Journal of Operational Research

115

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When a machine breakdown forces a modi®ed¯ow shop (MFS) out of the prescribed state, the proposed strategy reschedules part of the initial schedule to match up with the preschedule at some point. The objective is to create a new schedule that is consistent with the other production planning decisions like material¯ow, tooling and purchasing by utilizing the time critical decision making concept. We propose a new rescheduling strategy and a match-up point determination procedure through a feedback mechanism to increase both the schedule quality and stability. The proposed approach is compared with alternative reactive scheduling methods under di erent experimental settings. Ó

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A real-time production scheduler for a stochastic manufacturing environment

Cited by 60 publications

References 16 publications

Single Machine Scheduling Problem with Interval Processing Times and Total Completion Time Objective

Single Machine Scheduling Problem with Interval Processing Times and Total Completion Time Objective

The cell as part of a manufacturing system

Match-up scheduling under a machine breakdown

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