Fuzzy work-in-process inventory control of unreliable manufacturing systems

Tsourveloudis, Nikos C.; Dretoulakis, E.; Ioannidis, Stratos

doi:10.1016/s0020-0255(00)00030-x

Cited by 53 publications

(46 citation statements)

References 9 publications

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“…However, actually, stochastic of WIP control make it more difficult to simulate and control actual production. Fortunately, a fuzzy control methodology can easily resolve this problem, and keep the WIP inventory at a reasonable level [13]. So, in this paper, a fuzzy controller is set to simulate real control.…”

Section: A Centralized Fuzzy Control Methodologymentioning

confidence: 99%

Simulation-Based Environmental Cost Analysis for Work-in-Process

Zhao¹

2012

Int. j. simul. model.

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Under the modern manufacturing environment-oriented, both productivity and green environmental consideration are made in diverse phases of production operations. In this paper, in order to dynamically analyse and control changes of Work-In-Process (WIP) inventory level, a centralized fuzzy control method is proposed. Additionally, a new environmental management accounting method, Material Flow Cost Accounting (MFCA), is adopted to find and calculate environmental waste hiding in the production processes. Moreover, a simulation model integrated with these methods above is constructed. Based on the simulation data, sensitivity analysis between control factors of WIP inventory level and negative product costs ratio is made. To achieve a reasonable productivity as well as green environmental performance for this case, a corresponding optimized solving measure is put forward by means of OptQuest in the Arena simulation system. Through studying, the proposed control approach of WIP can reduce the green environmental cost, and improve production capacity.

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Section: A Centralized Fuzzy Control Methodologymentioning

confidence: 99%

Simulation-Based Environmental Cost Analysis for Work-in-Process

Zhao¹

2012

Int. j. simul. model.

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“…As a result, complex production systems are not amenable to accurate and exact modelling. In this framework, in order to overcome the system complexity analysis and its control design, a decomposition of the system into elementary subsystems is frequently considered [42,43,46]. Thus, a production system composed of N machines and a set of buffers can be viewed as a collection of a set of N elementary production modules PM(i), i = 1, …, N. Each one is defined by a machine and its sets of upstream and downstream buffers.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, the structure and the consequent outputs in response to external disturbances and environmental conditions are determined by empirical evidence, i.e., observed input/output behaviour of the system. In the literature, the intelligent distributed control for production systems has been introduced either through some paradigms such as holonic systems [6,22,23,26] and agent-based systems [36,37] or by using artificial intelligent techniques such as neural networks and fuzzy logic [1,33,46]. In the former case, the distributed control means that the control algorithm is distributed over a number of entities (software components) that combine their calculation power and their local knowledge to optimise the global performances.…”

Section: Introductionmentioning

confidence: 99%

“…In [1,12], complex control problems of scheduling and planning have been addressed in a distributed fashion by using fuzzy control theory. In [46], Tsourveloudis et al develop a distributed fuzzy system for work-in-process inventory control of unreliable machines.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, the supervisor provides an action able to tune the distributed controllers to ultimately achieve desired specifications [31]. For example, in order to improve the WIP performance, the authors [20] introduce a fuzzy supervisor to tune the lower level of the distributed fuzzy controllers given in [46]. In [47], an evolutionary algorithm strategy is used for the optimisation of the approaches provided in [46] and [20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent distributed and supervised flow control methodology for production systems

Tamani

Boukezzoula

Habchi

2009

Engineering Applications of Artificial Intelligence

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This paper deals with the development of an intelligent distributed and supervised control approach for highvolume production systems in which the flow of parts can be approximated by a continuous (fluid) model. The proposed approach is based on the decomposition of the production system into elementary modules in order to reduce the control design computational complexity. In this context, a two levels control structure is proposed. At the local level, a surplus-based principle is adopted to regulate the production flow for each module according to the distributed structure. The proposed control methodology decides how to adjust the production rate in order to avoid system overloading and eliminate machine starvation or blocking. In this context, the local control law is synthesized by using the Takagi-Sugeno fuzzy systems. At the high level, a supervisory controller is designed to improve the overall system performances. A supervisor provides an additive component for each local controller when the overall system performances deviate from their acceptable domains (degraded mode). This is done by combining both local and global information into a unified formalism by using aggregation operators and according to fuzzy interval representation of the desired objectives. Finally, the feasibility of the proposed methodology is validated with simulation examples.

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Fuzzy Techniques in Scheduling of Manufacturing Systems

Ioannidis

Tsourveloudis

Fuzzy Applications in Industrial Engineering

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Fuzzy work-in-process inventory control of unreliable manufacturing systems

Cited by 53 publications

References 9 publications

Simulation-Based Environmental Cost Analysis for Work-in-Process

Simulation-Based Environmental Cost Analysis for Work-in-Process

Intelligent distributed and supervised flow control methodology for production systems

Fuzzy Techniques in Scheduling of Manufacturing Systems

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