Time value of delays in unreliable production systems with mixed uncertainties of fuzziness and randomness

Chen, Shih‐Pin

doi:10.1016/j.ejor.2016.06.021

Cited by 7 publications

(5 citation statements)

References 54 publications

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“…The figures depict the rough shape from these values. In the following examples, we assume that the arrival rate Λ, service rate µ, reward R and holding cost C are trapezoidal fuzzy numbers represented by [1,2,3,4], [5,6,7,8], [10,15,20,25], [2,4,6,8], respectively. As is shown in the figures, we can make the following observations.…”

Section: 1mentioning

confidence: 99%

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Analysis of strategic customer behavior in fuzzy queueing systems

Chen¹,

Liu²,

Zhang³

2020

Journal of Industrial &Amp; Management Optimization

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This paper analyzes the optimal and equilibrium strategies in fuzzy Markovian queues where the system parameters are all fuzzy numbers. In this work, tools from both fuzzy logic and queuing theory have been used to investigate the membership functions of the optimal and equilibrium strategies in both observable and unobservable cases. By Zadeh's extension principle and α-cut approach, we formulate a pair of parametric nonlinear programs to describe the family of crisp strategy. Then the membership functions of the strategies in single and multi-server models are derived. Furthermore, the grated mean integration method is applied to find estimate of the equilibrium strategy in the fuzzy sense. Finally, numerical examples are solved successfully to illustrate the validity of the proposed approach and a sensitivity analysis is performed, which show the relationship of these strategies and social benefits. Our finding reveals that the value of equilibrium and optimal strategies have no deterministic relationship, which are different from the results in the corresponding crisp queues. Since the performance measures of such queues are expressed by fuzzy numbers rather than by crisp values, the system managers could get more precise information.

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Section: 1mentioning

confidence: 99%

“…The model is motivated by a situation that occurs in an automated car wash operation with a machine [2]. Concretely, a strategic customer is typically the driver who wants to wash the car in an automated car wash system.…”

mentioning

confidence: 99%

Analysis of strategic customer behavior in fuzzy queueing systems

Chen¹,

Liu²,

Zhang³

2020

Journal of Industrial &Amp; Management Optimization

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“…Commonly, when dealing with unreliable data, fuzzy methods have the capability of describing the degree of uncertainty for each variable. For example, Chen [156] employs a cost-based fuzzy decision model to deal with unreliable systems. Xie et al [157] incorporate the designed fuzzy weighting function into their fuzzy control model under communication links.…”

Section: ) Unreliable Data and Imbalanced Datamentioning

confidence: 99%

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

2019

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Feature selection has been an important research area in data mining, which chooses a subset of relevant features for use in the model building. This paper aims to provide an overview of feature selection methods for big data mining. First, it discusses the current challenges and difficulties faced when mining valuable information from big data. A comprehensive review of existing feature selection methods in big data is then presented. Herein, we approach the review from two aspects: methods specific to a particular kind of big data with certain characteristics and applications of methods in classification analysis, which are significantly different to the existing review work. This paper also highlights the current issues of feature selection in big data and suggests the future research directions. INDEX TERMS Feature selection, big data, data mining, applications.

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“…This study of the multi-state queueing system with server breakdowns is motivated by its wide applications [4,5]. For example, in the automated car wash facility situation, as the facility operates, it may gradually deteriorate due to wear and it will fall into a subnormal state with a higher breakdown probability or lower service rate.…”

Section: Introductionmentioning

confidence: 99%

Dynamic vs. static maintenance rate policies for multi-state queueing systems

2021

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This paper considers a single-server queueing system with server breakdowns. When the server fails, it is sent to repair immediately and its maintenance rate is allowed to be adjustable. The goal is to find the optimal maintenance rates that minimize the long-run average cost of the system. We address the static and dynamic maintenance control problems respectively. For the static control model, we derive the stationary system performances and the explicit solution of the optimal maintenance rate. Then regarding the dynamic maintenance problem, we formulate it as a Markov decision process (MDP) and the optimal dynamic policy is proved to be a threshold policy. Based on the structure of optimal dynamic policy, we construct a performance evaluation for computing efficiently the optimal threshold and average cost. Finally, a comparative study of the dynamic and static policies is presented by numerical experiment which shows the impact of system parameters on the optimal maintenance policies.

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Time value of delays in unreliable production systems with mixed uncertainties of fuzziness and randomness

Cited by 7 publications

References 54 publications

Analysis of strategic customer behavior in fuzzy queueing systems

Analysis of strategic customer behavior in fuzzy queueing systems

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

Dynamic vs. static maintenance rate policies for multi-state queueing systems

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