Currently, robotic manufacturing cells entail complex decisions concerning sequencing issues due to uncertainty which arises in different parameters such as time to failure, time to repair and cycle times that can be effectively supported by computer simulation models. The paper is focused on part sequencing of a two-machine robotic cell in a flow shop which produces different parts. The process is supported by a single gripper robot to load/unload products and also in displacement within the system. This study considers machine failures and repair such that S 2 cycle time and total production cost should be minimized. In this study, simulation facilitated input part sequence and also data envelopment analysis method is applied to trace the optimum sequence for satisfying the objective functions. Results through some numerical examples showed some simulation advantages specially to model many uncertainties and what if analysis.

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Data envelopment analysis (DEA) is one of the most popular techniques for measuring relative efficiencies of various similar units. However, lack of opportunity to compare the decision making units (DMUs) on the same scale in DEA model can make it less practical to classify DMUs. In this paper, we present common weights for DMUs by applying a scientific methodology utilizing goal programming as one of multi criteria decision making (MCDM) techniques, thereby we deal with improving discrimination power for selecting the efficient DMUs. The paper investigates the validity of the ranking technique, an index called the relative closeness (RC) to the ideal DMU (IDMU). Finally, via a previously reported numerical example, the proposed data envelopment analysis-goal programming (DEAGP) model is compared with that obtained by the DEA-AHP.

Inter-firm performance differences are influenced by several contextual variables, and managerial ability is one important factor that enables some firms to gain leadership positions in the market and helps them to sustain the advantage over successive time periods. However, managerial ability is the cognitive capability which is not directly observable/measurable. In this article, an indirect estimate of managerial ability under a three-stage approach for 20 Indian general insurance companies based on 120 firm-year observations spread over the period 2012–2013 to 2017–2018 is provided. The three-stage estimation method for the measurement of firm-specific managerial ability includes data envelopment analysis (DEA)-goal programming, pooled regression, residual of the pooled regression, Ordinary Least Squares, and General Additive Model regression. Unlike other studies, in this study, DEA-goal programming method is considered to improve discriminatory power for proper classification of the Indian general insurance companies. The results indicate that the influence is statistically significant.