Bipradas Bairagi

A De Novo multi-approaches multi-criteria decision making technique with an application in performance evaluation of material handling device

We propose a De Novo MCDM model (TPOP) for precise ranking and selection.We introduce advanced version of entropy weighting method.This model (TPOP) eliminates inappropriate weights distribution.This model (TPOP) overcomes the rank reversal of the conventional approaches.The new model (TPOP) assists and guides decision makers. Application of multiple conventional approaches to a particular multi-criteria decision making (MCDM) problem often suffers rank reversal giving rise to confusion and ambiguity in appropriate decision making. To eradicate the confusion, this paper proposes a De Novo multi-approaches multi-criteria decision making method namely Technique of Precise Order Preference (TPOP). The TPOP first examines the inconsistency in the ranking order of the alternatives of a MCDM problem by using multiple conventional approaches. If inconsistency/rank reversal in ranking order of the alternatives exists then TPOP, using advanced version of entropy weighting method introduced in this research work, measures weights of the final selection values of conventional approaches. Subsequently, TPOP based on these weights and final selection values computes precise selection indices (PSI) that determines accurate ranking order for the alternatives. The proposed technique is illustrated by two real life examples on material handling device (MHD) ranking and selection problems. The first example is initially solved using five conventional integrated fuzzy multi-criteria decision making techniques (FMCDMs) whereas the second example is taken from previous researchers works. The results obtained using TPOP justify the validity, applicability and requirements of the proposed technique. The study shows that the proposed multi-approaches, multi-criteria decision making technique can be a useful and effective model in MCDM.

Multi objective performance analysis

Display Omitted 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. 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.

Warehouse location selection by fuzzy multi-criteria decision making methodologies based on subjective and objective criteria

Abstract This paper proposes three new extended fuzzy multi-criteria decision making methodologies capable of handling subjective and objective factors for the evaluation and selection of warehouse location. A warehouse location selection problem involves both subjective as well as objective criteria. The concept of fuzzy set theory is integrated with the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), Simple Additive Weight (SAW) and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) methods to assess subjective criteria in terms of subjective factor measures. A classical normalization technique is employed to assess the objective criteria in terms of objective factor measures. Subjective factor measures and objective factor measures are integrated by the Brown and Gibson model to calculate the warehouse location selection index. The proposed methods are illustrated with two examples of warehouse location selection. A comparative study of the results and a sensitivity analysis are carried out. The study finds that the proposed methodologies are useful and effective fuzzy multi-criteria decision making tools for the evaluation and selection of warehouse location in a supply chain.

Selection of robot for automated foundry operations using fuzzy multi-criteria decision making approaches

This paper employs three Fuzzy Multi-Criteria Decision Making (FMCDM) methodologies in the evaluation and selection of robots for automated foundry operations. In the methodologies, a Fuzzy Analytical Hierarchy Process (FAHP) is integrated individually with a Fuzzy Technique for Order Preference by Similarity to the Ideal Solution (FTOPSIS), a Fuzzy VIsekriterijumska optimizacija i KOmpromisno Resenje (FVIKOR) and a Complex PRoportional ASsessment method with the application of Grey systems theory (COPRAS-G). In each case, a FAHP is used to estimate the fuzzy weights of the selection criteria under consideration. FTOPSIS, FVIKOR and COPRAS-G are applied to evaluate as well as to select the robots. A real life problem of robots selection in foundry operation is cited to demonstrate and validate the applicability and potentiality of the employed methodologies. A comparative analysis of the results obtained by the methodologies is carried out. The study finds that the employed methodologies are useful, effective and sound surrogates for selecting the best robot in an FMCDM environment.

Group heterogeneity in multi member decision making model with an application to warehouse location selection in a supply chain

Display Omitted A novel MCGDM methodology based on group heterogeneity concept.Group heterogeneity established from pair wise preference comparison matrix.Biasness of information restricted by consistency check mechanism of AHP.The algorithm fits itself in the class of established MCDM techniques.Investigation finds the model as a robust and effective decision making tool. Group decision making (GDM) is more effective in extracting the real case scenarios of the decision problems to add competitive advantages in a supply chain. Group members from wider spectrum of the environment naturally command variation in knowledge level to their respective domain. The degree of heterogeneity of the decision makers in a group plays a crucial role in realistic assessment of both alternatives and selection criteria. This paper proposes a new Multi criteria GDM approach in adroit exploitation of the group heterogeneity during evaluation process and restrict the biasness of information while decision making. The importance of the heterogeneous degree of expertise is established through pair wise preference comparison matrix. To overcome the biasness, the consistency check mechanism of analytical hierarchy process (AHP) is employed. A real case example on warehouse location selection in a supply chain is illustrated to demonstrate the validity and effectiveness of the proposed approach. In order to ensure the applicability, compatibility and validity of the proposed approach, comparative study is carried out with the proven and established MCDM methodologies SAW, MOORA, TOPSIS, VIKOR, ELECTRE II, COPRAS and PROMETHEE. ANOVA, Sensitivity analysis (SA) and other investigations find the proposed approach as a rational, robust, effective and precise decision making aid to the supply chain managers.

Selection of robotic systems in fuzzy multi criteria decision-making environment

This paper proposes a technique for order preference by similarity to ideal solution (TOPSIS)-based fuzzy multi criteria decision-making (FMCDM) approach which is capable of dealing with tangible as well as intangible factors while selecting the best robotic system. The concept of fuzzy set theory is used to assess intangible factors. A decision matrix and a weight matrix are developed by subjective perception of decision makers. Fuzzy TOPSIS utilises subjective performance ratings to compute subjective factor measure (SFM) in terms of closeness coefficient. Objective performance ratings are utilised to determine objective factor measure (OFM). SFM and OFM along with coefficient of decision-making attitude evaluate robotic system selection index (RSSI) and aggregated robotic system selection index (ARSSI) which are the key selection parameter of robotic systems. The proposed methodology is illustrated with an example for its applicability. The result is compared with the solutions obtained by MOORA and SAW to show the effectiveness of the proposed methodology. Finally, sensitivity analysis of the problem is carried out for searching the best alternative while coefficient of decision-making attitude varies. The overall result shows that the proposed MCDM model is a very useful and effective tool for selection of robotic system.

A hybrid fuzzy technique for the selection of warehouse location in a supply chain under a utopian environment

This paper proposes a novel hybrid fuzzy multi criteria decision-making technique for warehouse location selection in a supply chain under a utopian environment. In the proposed methodology, the concept of fuzzy set theory is used to measure subjective performance ratings of warehouse locations and weights of criteria. The simple additive weighting method and factor rating systems are combined to calculate the final fuzzy values (FFVs) of each warehouse location. FFVs are implemented to evaluate preference relationships between the alternatives. Pairwise comparison of the preference relationships generate a fuzzy preference relation matrix (FPRM). This investigation introduces four key selection parameters computed from the FPRM, viz. row sum, column sum, row sum − column sum difference and row sum–column sum ratio to select the best warehouse location. The proposed algorithm is illustrated with the solution of a real life problem of warehouse location selection. A comparative analysis is accomplished to show the acceptability and effectiveness of the proposed method. The result of this study clearly establishes the proposed methodology as one of the most erudite decision-making tools in a supply chain.