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Multi-Criteria Decision Making for Relation between Modern Industrial Systems

Narayan Agrawal


The aim of this research is to find relation between various modern industrial systems using multi-criteria decision making i.e., fuzzy algorithm. The proposed model is focused on integrating and managing the flow of material and information to make industrial systems more responsive to customer demand.  Keeping this view, it is try to create a generalized software program with the aim of minimizing inventory. With the use of Fuzzy process and with the code developed, performance evaluation and relation between modern industrial systems i.e. Kanban, Conwip and Hybrid has been carried out.

Keywords: Kanban, Conwip, just-in-time, hybrid system, fuzzy

Cite this Article

Dr. Narayan Agrawal. Multi-Criteria Decision Making for Relation between Modern Industrial Systems. Recent Trends in Programming Languages. 2018; 5(3): 1–7p.

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Bonvik AM, Couch CE, Gershwin SB. A Comparison of Production-Line Control Mechanisms. Int J Prod Res. 1997; 35(3): 789–804p.

Spearman ML, Zazanis MA. Push and Pull Production Systems: Issues and Comparisons. Oper Res. 1992; 40(3): 521–532p.

Gstettner S, Kuhn H. Analysis of Production Control Systems Kanban and CONWIP. Int J Prod Res. 1996; 34(11): 3253–3274p.

Duri C, Frein Y, Di Mascolo M. Comparison among Three Pull Control Policies: Kanban, Base Stock, and Generalized Kanban. Ann Oper Res. 2000; 93(1): 41–69p.

Geraghty John, Heavey Cathal. A Comparison of Hybrid Push/Pull and CONWIP/Pull Production Inventory Control Policies. Int J Production Economics. 2004; 91(1): 75–90p.

Tang L-L, Yih Y, Liu CY. A Study on Decision Rules of a Scheduling Model in an FMS. Comput Ind. 1993; 22(1): 1–13p.

Gupta MC, Gupta YP, Evans GW. Operations Planning and Scheduling Problem in Advanced Manufacturing Systems. Int J Prod Res. 1993; 31(4): 869–900p.

Petroni A, Rizzi A. A Fuzzy Logic Based Methodology to Rank Shop Floor Dispatching Rules. Int J Prod Econ. 2002; 76(1): 99–108p.

Jing-Wen-Li. Improving the Performance of Job Shop Manufacturing with Demand-Pull Production Control by Reducing Setup/Processing Time Variability. Int J of Prod Econ. 2003; 84(3): 255–270p.

Shih Tsou-Ching. Multi-Objective Inventory Planning Using MOPSO and TOPSIS. Expert Syst Appl. 2008; 35(1–2): 136–142p.

Sheu Jiuh-Biing. A Hybrid Neuro-Fuzzy Analytical Approach to Mode Choice of Global Logistics Management. Eur J Oper Res. 2008; 189(3): 971–986p.

Chyuan Lin-Ming, Cheng Wang-Chen, Shi Chen-Ming, et al. Using AHP and TOPSIS Approaches In Customer-Driven Product Design Process. Comput Ind. 2008; 59(1): 17–31p.

Kahraman Cengiz, Çevik Sezi, Yasin Nüfer, et al. Fuzzy Multi-Criteria Evaluation of Industrial Robotic Systems. Comput Ind Eng. 2007; 52(4): 414–433p.

Yang Taho, Hung Chih Ching. Multiple-Attribute Decision Making Methods for Plant Layout Design Problem. Robot Comput Integr Manuf. 2007; 23(1): 126–137p.

Shyur Hsu-Huan, Shih Shih-Jyh. A Hybrid MCDM Model for Strategic Vendor Selection. Math Comput Model. 2006; 44(7–8): 749–761p.

Yang Taho, Chen Chen-Mu, Hung Chih-Ching. Multiple Attribute Decision-Making Methods for the Dynamic Operator Allocation Problem. Math Comput Simul. 2007; 73(5): 285–299p.

Ayag Zeki. An Analytic-Hierarchy-Process Based Simulation Model for Implementation and Analysis of Computer-Aided Systems. Int J Prod Res. 2002; 40(13): 3053–3073p.

Yang Taho, Kuo Chunwei. A Hierarchical AHP/DEA Methodology for the Facilities Layout Design Problem. Eur J Oper Res. 2003; 147(1): 128–136p.

Sharma S, Agrawal N. Multi Criteria Decision Making for the Selection of a Production Control Policy” Int. Journal of Industrial and Systems Engineering (IJISE), Vol. 6, Issue 3, 2010, 321-339p.

Sharma S, Agrawal N. Selection of a Pull Production Control Policy under Different Demand Situations for a Manufacturing System by AHP-Algorithm. Comput Oper Res, Elsevier Science Ltd., Oxford, UK Press; 2009; 36(5): 1622–1632p.


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