An Efficient Operations Management Strategy Using Fuzzy Logic Decision Making Based Shop Floor Control Technique

2018 ◽  
Author(s):  
R. Muruganandham ◽  
J. Dinesh ◽  
A. Mohammed Rahell Basha ◽  
Venkatesh Kannan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Operations Management ◽  
Management Strategy ◽  
Control Technique ◽  
Shop Floor ◽  
Shop Floor Control ◽  
Floor Control

scholarly journals An Efficeint Operations Management Strategy using Fuzzy Logic Decision Making Based Shop Floor Control Technique

2019 ◽  
Vol 8 (2S4) ◽  
pp. 629-631
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Operations Management ◽  
Recent Decade ◽  
Control Technique ◽  
Shop Floor ◽  
Shop Floor Control ◽  
High Productivity ◽  
Floor Control ◽  
The Way

In any organization, shop floor control seems to be a vital and pivotal activity and effective shop floor control paves the way for cent percet production. Effective utilization of resources, proper production planning and successful maintainence would always pave the way for high productivity which in turn fulfils the commitment to meet out the delivery schedule for custoemers. Hence it is understood that continous and consistent shop floor management demands a serious focus that contributes for sound operations management. More developments have taken place in the recent decade in framing new methodologies and adopting new advanced techniques to control the shop floor acivities of an organization. The purposes of our paper is to bring out a new model for shop floor control using Fuzzy Logic Decision Taking technique to help the operations professionals to enable them for successful professional decision making in shop floor control. Multi Criteria Decision Making (MCDM) Techniques have witnessed tremendous application in operations management. Our paper has successfully attempted to apply Fuzzy Logic Decision Making Technique which is one among the various MCDM tools . The technique considers many factors like Production, Wastages, Breakdown or Idle Time etc and these factors are treated as criteria and upon using the tool, the Management Information System(MIS) resports generated after mathermatical manipulation using Fuzzy Logic seems to be much usedful for corporate decision making

scholarly journals Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Control Technique ◽  
Distributed Intelligence ◽  
High Level ◽  
The Impact ◽  
Fuzzy Subsets

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

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