Authors: Ali Salmasnia; Asghar Moeini; Hadi Mokhtari; Cyrus Mohebbi
Addresses: Department of Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O. Box: 14115-111, Tehran, Iran ' School of Computer Science, Engineering and Mathematics, Flinders University, GPO Box 2100, Adelaide 5001, South Australia, Australia ' Department of Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O. Box: 14115-111, Tehran, Iran ' Department of Information, Operation and Management Science, Kaufman Management Center, New York University, New York, New York 10012, USA; Morgan Stanley, 2000 Westchester Avenue, Purchase, NY 10577, USA; Columbia University, 116th Street and Broadway, New York, NY 10027, USA
Abstract: Setting of process variables to meet required specification of quality characteristics is one of the important problems in quality control processes. In general, most industrial and production systems are dealing with several different responses and the problem is to simultaneously optimise these responses. To obtain the most satisfactory solution, a decision-makers (DM) preference on the trade-offs among the quality characteristics should be incorporated into the optimisation procedure. This study suggests a robust posterior preference articulation approach based on a non-dominated sorting genetic algorithm (NSGA-II) to optimise multiple responses. In order to minimise the variation in deviation of responses from targets, maximum and sum of deviations are taken into consideration. To investigate the performance of the suggested approach, a computational analysis on a real world chemical engineering example is performed. Results show the superiority of the proposed approach compared to the existing techniques.
Keywords: non-dominated sorting genetic algorithms; NSGA-II; multiresponse optimisation; posterior preference; decision making; VIKOR; process variables; quality control; multiple responses; chemical engineering.
International Journal of Applied Decision Sciences, 2013 Vol.6 No.2, pp.186 - 207
Available online: 11 Apr 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article