A decision support system for IMS selection based on fuzzy VIKOR method

Intelligent manufacturing system (IMS) presents system with autonomous ability to adapt to unexpected changes. In order to succeed in this ever so demanding, fast pace business environment, managers need integrated and IMS capable of supporting them throughout the decision-making lifecycle. Therefore, it is worthwhile to invest on making appropriate decisions on IMS selection. The selecting process mainly involves evaluation of different alternatives based on different criteria. This process is essentially considered as a multiple criteria decision-making (MCDM) problem which is affected by different tangible and intangible criteria including price, quality, performance, lead-time, efficiency, etc. This paper presents a model based on VIKOR method to select an appropriate IMS. Based on the concept of fuzzy logic and the VIKOR method, the proposed fuzzy VIKOR method has been developed to provide a rational and systematic process. The proposed method can be used to find a best solution to resolve a fuzzy MCDM problem and to specify the preferred solution for any decision problem in real organisational set up.

[1]  P. Yu.  A Class of Solutions for Group Decision Problems , 1973 .

[2]  J. Talavage,et al.  A knowledge-based design assistant for intelligent manufacturing systems , 1990 .

[3]  A. J. Wells,et al.  Comparison of emerging manufacturing concepts , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[4]  Danny Berry,et al.  Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain , 1999 .

[5]  John N. Pearson,et al.  Strategically managed buyer–supplier relationships and performance outcomes , 1999 .

[6]  M. Christopher.  The Agile Supply Chain : Competing in Volatile Markets , 2000 .

[7]  David F. Pyke,et al.  Manufacturing and Supply Chain Management in China: A Survey of State-, Collective-, and Privately-Owned Enterprises , 2000 .

[8]  Gwo-Hshiung Tzeng,et al.  Multicriteria Planning of Post‐Earthquake Sustainable Reconstruction , 2002 .

[9]  Mirko Ficko,et al.  Relational database as a cogitative part of an intelligent manufacturing system , 2004 .

[10]  Gwo-Hshiung Tzeng,et al.  Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS , 2004, Eur. J. Oper. Res..

[11]  Dursun Delen,et al.  An integrated and intelligent DSS for manufacturing systems , 2006, Expert Syst. Appl..

[12]  J. Mikkola,et al.  Coordination and sharing logistics information in leagile supply chains , 2007 .

[13]  R. Krishnamurthy,et al.  Leagile manufacturing: a proposed corporate infrastructure , 2007 .

[14]  Yi-Hsuan Chen,et al.  A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard , 2009, Expert Syst. Appl..

[15]  H. F. Manesh,et al.  AC 2010-24: A VIRTUAL FACTORY APPROACH FOR DESIGN AND IMPLEMENTATION OF AGILE MANUFACTURING SYSTEMS , 2010 .

[16]  M. Saravanan,et al.  Evaluation of meta-heuristic approaches for scheduling optimisation of flexible manufacturing systems , 2011, Int. J. Comput. Appl. Technol..

[17]  Lei Wang,et al.  A neuroendocrine-inspired bionic manufacturing system , 2011 .

[18]  Zahari Taha,et al.  Analytical hierarchy process for the selection of advanced manufacturing technology in an aircraft industry , 2011, Int. J. Appl. Decis. Sci..

[19]  Steve Davis,et al.  Applying confirmatory factor analysis multi-trait multi-method approach in supply chain management research , 2011, Int. J. Appl. Decis. Sci..

[20]  S. Datta,et al.  Interrelationship of drivers for agile manufacturing: an Indian experience , 2012 .

[21]  Hande Erdogan Aktan,et al.  Agricultural performance evaluation by integrating fuzzy AHP and VIKOR methods , 2013, Int. J. Appl. Decis. Sci..

[22]  Mohammad Mehdi Tavakoli,et al.  Ranking organisational units with a human capital management approach using fuzzy TOPSIS: a case study , 2013 .

[23]  Shankar Chakraborty,et al.  Evaluation and selection of flexible manufacturing systems using MACBETH method , 2013 .

[24]  S. Datta,et al.  Supplier evaluation in agile supply chain in fuzzy paradigm , 2013 .

[25]  Michael Mutingi,et al.  Integrating lean and agile manufacturing paradigms: a dynamic simulation analysis , 2013 .

[26]  A. Azadeh,et al.  A neuro-fuzzy regression approach for estimation and optimisation of gasoline consumption , 2014 .

[27]  S. Vinodh,et al.  Application of hybrid MCDM techniques for prioritising the gaps in an agile manufacturing implementation project , 2014 .

[28]  Christoph H. Glock,et al.  Multi-objective supplier selection and order allocation under quantity discounts with fuzzy goals and fuzzy constraints , 2014, Int. J. Appl. Decis. Sci..