Impact of operation flexibility and dispatching rules on the performance of a flexible manufacturing system

Chan, Felix T. S.

doi:10.1007/s00170-003-1594-1

Cited by 27 publications

(5 citation statements)

References 12 publications

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“…According to Browne et al (1984), there are eight different classes, which have been analyzed in various contributions. Two of the most important types are the possibility of performing an operation on more than one machine and the ability of a manufacturing system to use multiple alternative routes to produce a given part (Chan, 2001(Chan, , 2003(Chan, , 2004.…”

Section: Introductionmentioning

confidence: 99%

A constraint programming model for the scheduling of flexible manufacturing systems with machine and tool limitations

Zeballos

Quiroga

Henning

2010

Engineering Applications of Artificial Intelligence

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Section: Introductionmentioning

confidence: 99%

A constraint programming model for the scheduling of flexible manufacturing systems with machine and tool limitations

Zeballos

Quiroga

Henning

2010

Engineering Applications of Artificial Intelligence

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“…Furthermore, parts arriving at any work station are made to wait in a queue until the required machine becomes available. Once this required machine is idle, parts must be selected from the waiting queue based on scheduling rules [32][33][34]. As shown in Table 3, each of the considered FMS factors considered is studied with 2 levels.…”

Section: Methodsmentioning

confidence: 99%

Multi-Objective Design Optimization of Flexible Manufacturing Systems Using Design of Simulation Experiments: A Comparative Study

et al. 2022

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One of the basic components of Industry 4.0 is the design of a flexible manufacturing system (FMS), which involves the choice of parameters to optimize its performance. Discrete event simulation (DES) models allow the user to understand the operation of dynamic and stochastic system performance and to support FMS diagnostics and design. In combination with DES models, optimization methods are often used to search for the optimal designs, which, above all, involve more than one objective function to be optimized simultaneously. These methods are called the multi-objective simulation–optimization (MOSO) method. Numerous MOSO methods have been developed in the literature, which spawned many proposed MOSO methods classifications. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Moreover, previous classifications have been focused on general MOSO methods and rarely related to the specific area of manufacturing design. For this reason, a new conceptual classification of MOSO used in FMS design is proposed. After that, four MOSO methods are selected, according to this classification, and compared through a detailed case study related to the FMS design problem. All of these methods studied are based on Design of Experiments (DoE). Two of them are metamodel-based approaches that integrate Goal Programming (GP) and Desirability Function (DF), respectively. The other two methods are not metamodel-based approaches, which integrate Gray Relational Analysis (GRA) and the VIKOR method, respectively. The comparative results show that the GP and VIKOR methods can result in better optimization than DF and GRA methods. Thus, the use of the simulation metamodel cannot prove its superiority in all situations.

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“…Each machine has an input and output buffer of finite capacity. Chan (2004) considered flexible system consisting of twelve machines having input buffers of finite capacity. There was common buffer of infinite capacity to take care of blocking of the system.…”

Section: Developments Of Fms Modelsmentioning

confidence: 99%

A Simulation Study of FMS Under Routing and Part Mix Flexibility

Ali

Ahmad

2014

Glob J Flex Syst Manag

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This paper studies the performance of a flexible manufacturing system under different manufacturing strategies. Manufacturing strategies under consideration are manufacturing flexibility, lot size and scheduling of jobs in the system. The impact of all these strategies is evaluated on the make-span performance of the flexible manufacturing system. The methodologies involved include simulation, Taguchi experimental design and analyzing the results with help of ANOVA. Number of different simulation models is developed with the help of JAVA language. The first model relates to the manufacturing system, which is conventional in nature without any flexibility embedded in the system. The second model takes into partial manufacturing flexibility in the form of routing flexibility. Finally, the third models incorporate much higher level of routing flexibility. All these models are further evaluated at different level of part mix flexibility. Also the scheduling of parts is considered with the objective of minimizing the total make-span time of parts. The results show that considering different manufacturing strategies decisions in a simultaneous manner can significantly improve the performance of the flexible manufacturing systems.

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Impact of operation flexibility and dispatching rules on the performance of a flexible manufacturing system

Cited by 27 publications

References 12 publications

A constraint programming model for the scheduling of flexible manufacturing systems with machine and tool limitations

A constraint programming model for the scheduling of flexible manufacturing systems with machine and tool limitations

Multi-Objective Design Optimization of Flexible Manufacturing Systems Using Design of Simulation Experiments: A Comparative Study

A Simulation Study of FMS Under Routing and Part Mix Flexibility

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