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The purpose of this paper is to propose the use of graph theoretic structural modeling for assessing the possible reduction in complexity of the work flow procedures in an organization due to lean initiatives. A tool to assess the impact of lean initiative on complexity of the system at an early stage of decision making is proposed.

First, the permanent function-based graph theoretic structural model has been applied to understand the complex structure of a manufacturing system under consideration. The model helps by systematically breaking it into different sub-graphs that identify all the cycles of interactions among the subsystems in the organization in a systematic manner. The physical interpretation of the existing quantitative methods linked to graph theoretic methodology, namely two types of coefficients of dissimilarity, has been used to evolve the new measures of organizational complexity. The new methods have been deployed for studying the impact of different lean initiatives on complexity reduction in a case industrial organization.

The usefulness and the application of new proposed measures of complexity have been demonstrated with the help of three cases of lean initiatives in an industrial organization. The new measures of complexity have been proposed as a credible tool for studying the lean initiatives and their implications.

The paper may lead many researchers to use the proposed tool to model different cases of lean manufacturing and pave a new direction for future research in lean manufacturing.

The paper demonstrates the application of new tools through cases and the tool may be used by practitioners of lean philosophy or total quality management to model and investigate their decisions.

The proposed measures of complexity are absolutely new addition to the tool box of graph theoretic structural modeling and have a potential to be adopted by practical decision makers to steer their organizations though such decisions before the costly interruptions in manufacturing systems are tried on ground.

The functioning of multi-gene genetic programming (MGGP) algorithm suffers from the problem of difficulty in model selection. During the preliminary analysis, it is observed that there are many models in the population whose performance is better than that of the model selected with a little compromise on training error. Therefore, an ensemble evolutionary (Ensemble-MGGP) approach is proposed and applied to the data obtained from the vibratory finishing process. The paper aims to discuss these issues.

Unlike the standard GP, each model participating in Ensemble-MGGP approach is made by combining the set of genes. Predicted residual sum of squares criterion (PRESS) criterion is integrated to improve its evolutionary search. The parametric analysis and sensitivity analysis (SA) conducted on the proposed model validates its robustness by unveiling dominant input parameters and hidden non-linear relationships.

The results indicate that the proposed Ensemble-MGGP model outperforms the standardized MGGP model. SA and parametric analysis reveals relationships and insights into vibratory finishing process.

Literature emphasises on characterization of vibratory finishing process using the experimental-based-studies. In addition, the issue of difficulty in model selection in genetic programming is addressed. This work proposes a new ensemble evolutionary approach to counter these issues.