Multi objective performance analysis: A novel multi-criteria decision making approach for a supply chain. (April 2016)

• Record Type: Journal Article
• Authors: Dey, Balaram; Bairagi, Bipradas; Sarkar, Bijan; Sanyal, Subir Kumar
• Is Part Of: Computers & industrial engineering. Volume 94(2016)
• Page Start: 105
• Page End: 124
• Publication Date: 2016-04
• Subjects: Multi objective performance analysis (MOPA) -- Modified weight concept -- Analysis of variances -- Sensitivity analysis -- MCDM -- Supply chain; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.01.019
• Languages: English
• ISSNs: 0360-8352

Abstract

Graphical abstract: Highlights: A novel modified weight concept included in algorithm MOPA is unique in nature. New concept reduces inherent inaccuracy of weights significantly. MOPA can handle subjective and objective attributes; benefit and cost criteria. The algorithm MOPA fits itself in the class of applied MCDM techniques. ANOVA and SA reveal MOPA as precisely accurate and effective decision making tool. Abstract: This investigation introduces multi objective performance analysis (MOPA), a novel multi-criteria decision making (MCDM) approach to solve decision problems in a supply chain. In this paper, an innovative modified weight concept is employed to modify the weights of the criteria in order to reduce the affect of the inherent inaccuracy involved with direct use of weights. Modified weight and normalized performance rating are integrated to compute modified weighted performance (MWP). Aggregate modified weighted performances (AMWP) of the alternatives are determined to evaluate benefit cost ratio (BCR) which is considered as the final selection index of the alternative. The proposed algorithm MOPA is illustrated with six real life decision problems in various stages of a supply chain to adjudge its enviable significance from the point of simplicity, feasibility and applicability. In order to ensure the compatibility, the result obtained by the proposed algorithm MOPA is compared with the proven and established MCDM methodologies TOPSIS, SAW, MOORA, ELECTRE II, and VIKOR. The comparative analysis shows that the achieved result perfectly matches with most of the cited decision problems of previous research works published in various journals. Analysis of variance (ANOVA) reveal that the modified weight concept reduces the relative dispersion of weights significantly, leads to precise decision. Sensitivity analysis (SA) and other investigations also find MOPA as a simple, robust, effective and precise decision making tool.