Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach

Choi, Shiu Hong; Wang, K.

doi:10.1016/j.cie.2012.04.001

Cited by 68 publications

(22 citation statements)

References 35 publications

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“…Other work has been done to solve large optimization problems with the decomposition approach, some examples are, a steel plant schedule Mixed Integer Linear Programming problem was solved with decomposition [22], or a flow shop schedule combining a genetic algorithm with decomposition [23].…”

Section: Introductionmentioning

confidence: 99%

A decomposition approach for SMT-based schedule synthesis for time-triggered networks

Pozo

Steiner

Rodríguez-Navas

et al. 2015

2015 IEEE 20th Conference on Emerging Technologies &Amp; Factory Automation (ETFA)

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Real-time networks have tight communication latency and minimal jitter requirements. One way to ensure these requirements is the implementation of a static schedule, which defines the transmission points in time of time-triggered frames. Synthesizing such static schedules is known to be an NP-complete problem where the complexity is driven by the large number of constraints imposed by the network. Satisfiabily Modulo Theories (SMT) have been proven powerful tools to synthesize schedules of medium-to-large industrial networks. However, the schedules of new extremely large networks, such as integrated multi-machine factory networks, are defined by an extremely large number of constraints exceeding the capabilities of being synthesized by the tool alone.This paper presents a decomposition approach that will allow us to improve to synthesize schedules with up to two orders of magnitude in terms of the number of constraints that can be handled. We also present an implementation of a dependency tree on top of the decomposition approach to address applicationimposed constraints between frames.

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Section: Introductionmentioning

confidence: 99%

A decomposition approach for SMT-based schedule synthesis for time-triggered networks

Pozo

Steiner

Rodríguez-Navas

et al. 2015

2015 IEEE 20th Conference on Emerging Technologies &Amp; Factory Automation (ETFA)

View full text Add to dashboard Cite

show abstract

“…Nondeterministic manufacturing environment have been optimized with multiple methods and from different perspective. Choi et al [10] presented a combined algorithm named novel decomposition-based approach, to minimizing the interval makespan of a flexible flow shop with stochastic processing times. Huang et al [11] proposed an improved genetic algorithm with CUDA and developed a longest common substring coding method to optimize fuzzy flow job shop scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Application of Interval Theory and Genetic Algorithm for Uncertain Integrated Process Planning and Scheduling

Wang

Gao

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Process planning and scheduling are two important parts in intelligent manufacturing system and have great impacts on production efficiency. Integrate them can highly increase the production feasibility and optimality. Researchers have done a lot work on integration of process planning and scheduling (IPPS). But former researchers rarely focused on uncertain environment. In reality many factors can cause the uncertainty of production process time. This paper pioneers in choosing a better solution in uncertain manufacturing environment based on interval theory. The uncertain process time is modeled as interval number. And then, the completion time is also an interval number. Genetic Algorithm (GA) is used to solve this model. The feasibility and effectiveness of the solution have been taken into consideration. The experimental results obtained by increasing the scale of the problem illustrate the proposed method is stable and effective.

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“…Choi and Wang (2012) presented a decompositionbased approach for the flexible flow shop problem. To deal with uncertain job processing times, this method combines two reactive approaches with a reactive -proactive approach.…”

Section: Introductionmentioning

confidence: 99%

A new approach to reducing the effects of stochastic disruptions in flexible flow shop problems with stability and nervousness

Rahmani

Heydari

Makui

et al. 2013

International Journal of Management Science and Engineering Man

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We consider a flexible flow shop system with stochastic disruptions. In industrial environments, scheduling systems often operate under dynamic and random circumstances. In these conditions, it is inevitable to encounter some disruptions and breakdowns which are inherently stochastic events. These disruptions quickly cause the initial schedule to become infeasible and non-optimal and in need of appropriate revisions and rescheduling methods. In this paper, a different approach is considered to achieving a stable schedule despite the arrival of new unpredicted jobs into the process. This approach proposes an appropriate reactive method to deal with this unexpected event so that the best sequence can be determined in this dynamic system based on classical objectives and other performance measures. Computational results indicate that the method presented performs better than two other heuristic approaches.

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Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach

Cited by 68 publications

References 35 publications

A decomposition approach for SMT-based schedule synthesis for time-triggered networks

A decomposition approach for SMT-based schedule synthesis for time-triggered networks

Application of Interval Theory and Genetic Algorithm for Uncertain Integrated Process Planning and Scheduling

A new approach to reducing the effects of stochastic disruptions in flexible flow shop problems with stability and nervousness

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