A robust adaptive scheduler for an intelligent workstation controller

Cho, H.; Wysk, Richard A.

doi:10.1080/00207549308956756

Cited by 71 publications

(34 citation statements)

References 20 publications

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“…One issue addressed in this study is to test the effectiveness of this vu/e switching approach as a high-level decision not only in dynamic but also in stochastic environments. Some existing studies that consider similar approaches are reviewed in the previous section Wysk 1988, 1989;Ishii and Talavage 1991;Cho and Wysk 1993).…”

Section: Multi-pass Rule Selection Algorithmmentioning

confidence: 99%

“…After observing the advantages of switching rules in each time period Ishii and Talavage (1994) tested the idea of using a different rule on each machine in each period. Cho and Wysk (1993) developed a neural network model to generate alternative dispatching rules based on the current system status, which are then evaluated by multi-pass simulation. Aytug, Koehler, and Snowdon (1994) also considered a rule-selection algorithm strengthened with a genetic algorithm-based machine learning scheme in a parallel machine setting with flow-time objective.…”

Section: Literature Reviewmentioning

confidence: 99%

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Experimental investigation of iterative simulation-based scheduling in a dynamic and stochastic job shop

Kutanoğlu

Sabuncuoğlu

2001

Journal of Manufacturing Systems

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A vital component of modern manufacturing systems is the scheduling and control system, which determines companies' overall performance in their respective supply chains. This paper studies iterative simulation-based scheduling mechanisms for manufacturing systems that operate in dynamic and stochastic environments. Also assessed are the issues involved when these mechanisms are used to make higher-level scheduling decisions, such as dispatching rule selection, instead of generation of a full schedule. A typical simulation-based system is outlined and tested under various experimental conditions. Examined are the effects of stochastic events such as machine breakdowns and processing time variations on the system performance, and the effectiveness of the simulation-based approach from the control point of view is evaluated. Finally, different levels of two important factors (look-ahead window and scheduling period) are compared for the iterative approach. Computational results show that, although simulation-based scheduling proves effective when these parameters are properly set, the overall performance diminishes due to the dynamic and stochastic nature of the system, which degrades the multi-pass improvement capability of the simulation runs. Experimental results also support the initial expectation in that frequent updates to the higher-level schedule may not be necessary when these decisions are naturally "adaptive" to the unexpected system changes.

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Section: Multi-pass Rule Selection Algorithmmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Experimental investigation of iterative simulation-based scheduling in a dynamic and stochastic job shop

Kutanoğlu

Sabuncuoğlu

2001

Journal of Manufacturing Systems

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“…Table 4 lists the predictors used in the experiments. The selection of these predictors is generally on the basis of earlier research (see for example, [35,54,55]). It is acknowledged though that a simple attribute-selection model is not capable to generate and guarantee even near-optimal subset of predictors.…”

Section: Methodsmentioning

confidence: 99%

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

Shahzad

Mebarki

2016

Computers

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Abstract:A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation of dispatching rules is desired to make them more effective in changing shop conditions. Meta-heuristics are able to perform quite well and carry more knowledge of the problem domain, however at the cost of prohibitive computational effort in real-time. The primary purpose of this research lies in an offline extraction of this domain knowledge using decision trees to generate simple if-then rules that subsequently act as dispatching rules for scheduling in an online manner. We used similarity index to identify parametric and structural similarity in problem instances in order to implicitly support the learning algorithm for effective rule generation and quality index for relative ranking of the dispatching decisions. Maximum lateness is used as the scheduling objective in a job shop scheduling environment.

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“…It can only provide information about what is not advisable. To supplement simulation deficiencies some researchers (Chryssolouris et al, 1990;Cho and Wysk, 1993;Chu, 1993;Chen and Sagi, 1994b;Wilhelm et al, 1994) proposed a neural network approach which sought to provide the inverse of simulation function: given a set of desired cell performance measures, the neural network outputs a suitable cell design. Neural networks are gaining popularity, because of their ability to predict accurately.…”

Section: Introductionmentioning

confidence: 99%

A framework for intelligent design of manufacturing cells

Sagi

Chen

1995

J Intell Manuf

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One of the major thrusts of 'agile/lean/responsive' manufacturing strategies of the twentyfirst century is to introduce advanced information technology into manufacturing. This paper presents a framework for robust manufacturing system design with the integration of simulation, neural networks and knowledge-based expert system tools. An operation/ cost-driven cell design methodology was applied to concurrently consider cell physical design and the complexity of cell control functions. Simulation was exercised to estimate performance measures based on input parameters and given cell configurations. A rulebased expert system was employed to store the acquired expert knowledge regarding the relation between cell control complexities, cost of cell controls, performance measures and cell configuration. Neural networks were applied to predict the cell design configuration and corresponding complexities of cell control functions. Training of neural networks was performed with both forward and backward methods by using the same pair of data sets. Hence, trained neural networks will be able to predict either input or output parameters. This innovative new design methodology was illustrated via a successful implementation exercise resulting in actually acquiring an automated cell at industrial settings. The experience learned from this exercise indicates that the proposed design methodology works well as an effective decision support system for cell designers and the management in determining appropriate cell configuration and cell control functions at the design stage.

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A robust adaptive scheduler for an intelligent workstation controller

Cited by 71 publications

References 20 publications

Experimental investigation of iterative simulation-based scheduling in a dynamic and stochastic job shop

Experimental investigation of iterative simulation-based scheduling in a dynamic and stochastic job shop

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

A framework for intelligent design of manufacturing cells

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