Prioritizing decision criteria of flexible manufacturing systems using fuzzy TOPSIS

2017 ◽  
Vol 28 (7) ◽  
pp. 913-927 ◽  
Author(s):  
Reshma Yasmin Siddiquie ◽  
Zahid A. Khan ◽  
Arshad Noor Siddiquee

Purpose The purpose of this paper is to systematically demonstrate the use of an effective multiple criteria decision-making technique, i.e. fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) in ranking the decision criteria of flexible manufacturing systems (FMS). Design/methodology/approach A questionnaire is specially designed and served to the industry experts to collect their opinion on several FMS decision criteria. Subsequently, fuzzy TOPSIS is used to prioritize the decision criteria. Findings Fuzzy TOPSIS multiple criteria decision-making technique is explained and applied to determine relative importance of the several decision criteria of FMS. This will help management of organizations in taking decision for implementing FMS in their organizations. From this study, it is found that customer satisfaction is the top most criterion among several other criteria for the successful implementation of FMS. Research limitations/implications In situation like the one considered in this research, there are dependencies and interactions among the criteria and, therefore, other techniques such as fuzzy analytic network process would have been a better choice. Nevertheless, fuzzy TOPSIS also provides good result as it incorporates vagueness associated with the decision maker’s opinion pertaining to the several FMS decision criteria. Originality/value This paper presents a fuzzy TOSIS model to help managers understand the relative importance of the several FMS decision criteria so that they can use this information for successful implementation of this advanced manufacturing technology in their organizations.

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6333 ◽  
Author(s):  
Fengjia Yao ◽  
Bugra Alkan ◽  
Bilal Ahmad ◽  
Robert Harrison

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly.


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