A multi-objective flexible manufacturing system design optimization using a hybrid response surface methodology

The present study proposes a hybrid framework combining multiple methods to determine the optimal values of design variables in a flexible manufacturing system (FMS). The framework uses a multi-objective response surface methodology (RSM) to achieve optimum performance. The performance of an FMS is characterized using various weighted measures using the best-worst method (BWM). Subsequently, an RSM approximates the functional relationship between the FMS performance and design variables. The central composite design (CCD) is used for this aim, and a polynomial regression model is fitted among the factors. Eventually, a bi-objective model, including the fitted and cost functions, is formulated and solved. As a result, the optimal percentage for deploying the FMS equipment and machines to achieve optimal performance with the lowest deployment cost is determined. The proposed framework can serve as a guideline for manufacturing organizations to lead strategic decisions regarding the design problems of FMSs. It significantly increases productivity for the manufacturing system, reduces redundant labor and material handling costs, and facilitates productio

Evaluation of performance factors of FMS by combined decision making methods as AHP, CMBA and ELECTRE methodology

Present research is aimed to analyze the performance factors of flexible manufacturing systems by combined decision-making methodologies like analytical hierarchy process (AHP), combinatorial mathematics-based approach (CMBA) and improved ELECTRE. Six variables affecting the three factors of performance of flexible manufacturing systems viz. productivity, flexibility and quality are considered for the evaluation of performance factors. The weights of the attributes are calculated with AHP and the index score is calculated with CMBA methodology. CMBA methodology is the fusion of AHP and GTMA. ELECTRE approach has been used for the outranking of factors. The results show that productivity had the maximum impact on the performance of manufacturing systems. A high Spearman’s rank correlation also exists among the methods used

Where have all the equations gone?: A unified view on semi-quantitative problem structuring and modelling

For several decades structural modelling has assisted decision makers with the cognitive burden of exploring and interpreting complex situations. Three well-known techniques - labelled collectively here as semi-quantitative problem structuring and modelling (SPSM) - include ISM (Interpretive Structural Modelling); MICMAC (Matrice d'Impacts Croises-Multiplication Appliquee a un Classement); and DEMATEL (DEcision MAking Trial and Evaluation Laboratory). SPSM approaches pioneered the joint application of graph-theoretical principles and human-computer interaction. Yet today a template-style research approach prevails, focusing on the application context rather than seeking to advance or critically assess the individual techniques in their own right. This paper develops a unifying methodological view of SPSM, currently missing in the literature, by comparing and contrasting - for each technique - analytical and procedural aspects typically taken for granted. The paper's findings highlight: (1) Previously unnoticed overlaps between techniques that up to now have been deemed mutually exclusive, and incongruences between those that are often applied jointly; 2) Potential issues that arise when key analytical principles of SPSM are either applied uncritically or dispensed with altogether; 3) The need to leverage human-computer interaction, a prominent aspect in early SPSM research that is now surprisingly neglected. These findings are illustrated by a review of SPSM applications in the context of supply chain risk management.Industrial Ecolog