A Dynamic Selection of Dispatching Rules Based on the Kano Model Satisfaction Scheduling Tool

Ferreirinha, Luís; Baptista, Sara; Pereira, Ângela; Santos, Alex José Souza dos; Bastos, João; Madureira, Ana; Varela, Leonilde

doi:10.1007/978-3-319-91334-6_46

Cited by 2 publications

(6 citation statements)

References 15 publications

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“…The purpose of scheduling is to optimize a certain measurement of economic and operational performance. So, a typical production scheduling problem can be seen as, n parts must be processed by using m machines, and each part must be processed in a given order on the respective machine to find the schedule that optimizes certain performance metric [3,8,9,10].…”

Section: Production Scheduling Problemsmentioning

confidence: 99%

“…The main goal in solving this problem is no longer to find optimum schedules, because they quickly will become obsolete, but instead be able to efficiently adapt current solutions to the dynamic environment, since near optimal solutions that are easily adaptable, will be preferable to optimum ones. So, when non-planned perturbations occur, it is necessary to find a new schedule with the quality close to the schedule that could have been executed if all of the uncertainty had been revealed a priori [8,15].…”

Section: Dynamic Schedulingmentioning

confidence: 99%

“…ways of sequencing n jobs. In the literature, the performance of dispatching rules is usually evaluated experimentally, although in some cases a dispatching rule can be shown to be optimal like the Shortest Processing Time (SPT) that minimizes the Mean Flow Time and the Earliest Due Date (EDD) that minimizes the Maximum Tardiness, among others [2,8,6,14,15,19]. A dynamic scheduling can be viewed as a collection of linked static problems, which implies that methods developed for static scheduling problems become applicable to dynamic ones.…”

Section: Dispatching Rulesmentioning

confidence: 99%

“…In view of the above, this paper proposes a decision support tool to dynamic environments that attempts to fill the gap between scheduling theory and practice stated by the author in [7]. Continuing the model proposed in [8] this paper presents a prototype that does not require any interaction with the user besides the definition of the criteria of performance. The definition of the criteria of performance are classified through the degree of satisfaction of the Kano's Model, which classifies the performance measures and balance the interests of the stakeholders.…”

Section: Introductionmentioning

confidence: 99%

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Decision Support Tool for Dynamic Scheduling

Ferreirinha

Santos

Madureira

et al. 2019

Advances in Intelligent Systems and Computing

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Production scheduling in the presence of real-time events is of great importance for the successful implementation of real-world scheduling systems. Most manufacturing systems operate in dynamic environments vulnerable to various stochastic real-time events which continuously forces reconsideration and revision of pre-established schedules. In an uncertain environment, efficient ways to adapt current solutions to unexpected events, are preferable to solutions that soon become obsolete. This reality motivated us to develop a tool that attempts to start filling the gap between scheduling theory and practice. The developed prototype is connected to the MRP software and uses meta heuristics to generate a predictive schedule. Then, whenever disruptions happen, like arrival of new tasks or cancelation of others, the tool starts rescheduling through a dynamicevent module that combines dispatching rules that best fit the performance measures pre-classified by Kano's model. The proposed tool was tested in an indepth computational study with dynamic task releases and stochastic execution time. The results demonstrate the effectiveness of the model.

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Section: Production Scheduling Problemsmentioning

confidence: 99%

Section: Dynamic Schedulingmentioning

confidence: 99%

Section: Dispatching Rulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Decision Support Tool for Dynamic Scheduling

Ferreirinha

Santos

Madureira

et al. 2019

Advances in Intelligent Systems and Computing

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show abstract

“…The model proposed in [5] was revised and extended with an in-depth computational study to validate the concept and performance of the prototype through statistical evidence. The developed prototype tool is designed not only in a static environment but also in a dynamic one.…”

Section: Introductionmentioning

confidence: 99%

An Industry 4.0 oriented tool for supporting dynamic selection of dispatching rules based on Kano model satisfaction scheduling

Ferreirinha¹,

Baptista²,

Pereira³

et al. 2019

FME Transactions

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Production scheduling is an optimizing problem that can contribute strongly to the competitive capacity of companies producing goods and services. A way to promote the survival and the sustainability of the organizations in this upcoming era of Industry 4.0 (I4.0) is the efficient use of the resources. A complete failure to stage tasks properly can easily lead to a waste of time and resources, which could result in a low level of productivity and high monetary losses. In view of the above, it is essential to analyse and continuously develop new models of production scheduling. This paper intends to present an I4.0 oriented decision support tool to the dynamic scheduling. After a fist solution has been generated, the developed prototype has the ability to create new solutions as tasks leave the system and new ones arrive, in order to minimize a certain measure of performance. Using a single machine environment, the proposed prototype was validated in an in-depth computational study through several instances of dynamic problems with stochastic characteristics. Moreover, a more robust analysis was done, which demonstrated that there is statistical evidence that the proposed prototype performance is better than single method of scheduling and proved the effectiveness of the prototype.

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A Dynamic Selection of Dispatching Rules Based on the Kano Model Satisfaction Scheduling Tool

Cited by 2 publications

References 15 publications

Decision Support Tool for Dynamic Scheduling

Decision Support Tool for Dynamic Scheduling

An Industry 4.0 oriented tool for supporting dynamic selection of dispatching rules based on Kano model satisfaction scheduling

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