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Dynamic Fuzzy Expert System for Multi Objective Criteria for Selection of Manufacturing Method using Sugeno Model

Affiliations

  • Production Department, KIT’s College of Engineering,Kolhapur –416234,Maharashtra, India
  • Sanjay Ghodawat Group of Institutions, Atigre – 416118,Kolhapur, Maharashtra, India
  • Department of Computer Studies, Chhatrapati Shahu Institute of Business, Education and Research, Kolhapur – 416004, Maharashtra, India

Abstract


Objectives: Operational effectiveness is an ultimate objective of any manufacturing organization. On the shop floor the main focus is on elimination of waste and delays. The main target is designing a product for which manufacturing is a low cost process. Once organizations select appropriate tools and methods, the decision on performance measures can be taken through which progress can be monitored. The present work offers fuzzy expert system for manufacturing method selection in a dynamic environment where the organization’s objectives are subject to constant amendments. Methods/Statistical Analysis: In order to incorporate ambiguity into the manufacturing environment, the authorshave designed and developed a fuzzy expert system using Sugeno model based on the dynamic ranges of triangular membership functions for input objectives which is extremely efficient as compared to the Mamdani method in classifying vague data. In this paper, the authors proposearchitecture for fuzzy expert system based on multi objective criteria for selection of manufacturing method employing Sugeno model with constant output. Fuzzy expert system is created outside MATLAB and MATLAB is used only for creating user interface for querying methods based on objectives and for the evaluation of rules. Findings: As more than one manufacturing method may serve a single objective, each manufacturing method is encoded using a binary digit and the output is decimal representation of this binary encoding. Also, it is observed that, the number of fuzzy rules increase exponentially as a function of number of objectives. In order to cater to this problem, instead of generating so many rules the required modifications are performed at the code level to incorporate multi objective criteria. Finally a simulink model is developed for selective methods and objectives. The results obtained using Sugeno Fuzzy Expert System is compared with that obtained using Mamdani method and crisp expert system. Results: It is found that the crisp expert system and Sugeno type FIS yield similar results for classification while Mamdani type FIS offers more flexibility in method selection due to the nature of output members functions which are overlapping fuzzy sets. This yields the manager a greater freedom in method selection based on infrastructure and other resource availability.

Keywords

Expert System, Fuzzy Logic, Manufacturing, MATLAB, Membership Functions, Simulink.

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References


  • Halevi G. Handbook of Production Management Methods, Butterworth Heinemann Publications; 2003.p. 2–16.
  • Gardan Y, Minich C. Feature-based models for CAD/CAM and their limits. Computers in Industry. 1992; 23: 3–13.Crossref
  • Co-Davies BJ. Application of expert systems in process planning. Annals of CIRP. 1986; 35(2): 451–452.
  • Coyne RD, Rosenman MA, Radford AD. Knowledge-based design systems. Addison-Wesley; 1989; 12(3): 1–2.
  • Munro S. Lean Manufacturing Starts With Lean Design. Automotive Manufacturing and Production.1999;111 (8): 1–27.
  • Goldratt E. The fundamental measurements. Theory of Constraints Journal. 1988;1(3): 1–21.
  • Rao A, Scheraga D. Moving from manufacturing resource planning to just-in-time manufacturing. Production and Inventory Management Journal. 1988; 29(1): 44–50.
  • Turny PB. What is the scope of activity based costing? Journal of Cost Management. 1990; 3(4): 40–42.
  • Datz D. The effect of product design on product quality and product cost. Quality Progress. 1987; 20(6): 63–67.
  • Becker, Gerhart B. The impact of human resource management on organizational performance: Progress and Prospects. Academy of Management Journal. 1996;39: 770–801. Crossref
  • Lambert DM, Gardner JT. Developing and implementing supplier chain partnerships. The International Journal of Logistics Management.1996;7(2): 1–17. Crossref
  • Hales HC, Sovoie JB. Building a foundation for successful business process re-engineering. Industrial Engineering.1994; 17–19.
  • Hayes RH, Pisano GP. Beyond world class: The manufacturing strategy. Harvard Business Review. 1994;72(1):77–86.
  • Lu CJ, Tsai KH, Wang. A virtual testbed for the life cycle design of automated manufacturing facilities.
  • International Journal of Advanced Manufacturing Technologies.1998;14(8): 608–615. Crossref

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