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.

This paper aimed to demonstrate a metaheuristic as a solution procedure to schedule a two-machine, identical parts robotic cell under breakdown. The proposed previous model enabled one to determine optimal allocation of operations to the machines and corresponding processing times of each machine. For the proposed mathematical model to minimize cycle time and operational cost, multi-objective particle swarm optimization (MOPSO) algorithm was provided. Through some numerical examples, the optimal solutions were compared with the previous results. MOPSO algorithm could find the solutions for problems embeds up to 50 operations in a rationale time.

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To find the best sequence of the parts to the production system has been respected by researchers as a research gap especially in robotic cells that are confronted with breakdowns. Through the current study a simulation-based optimization approach is presented to trace the optimum sequence of a two-machine robotic cell that produces different products. A material handling device to support the transport and load/unload in this manufacturing system is a single gripper robot. Here, simulation enables the authors to determine sequencing of parts to the cell; also, a comparison is done based on DEA and DEAGP methods to trace the optimal cyclic sequences satisfying the objective functions, and through Andersen/Petersen's super-efficiency approach, the best cyclic sequence is selected. Applying DEAGP usually improves the discrimination power to select the efficient cyclic sequence of the parts, though the results in this problem are not satisfactory, and DEA results are more reasonable.

The purpose of this paper is to model two problems comprising schedule-allocate (in case of producing identical parts) and sequencing of parts (in case of producing different parts). The first model is used for minimizing the cycle time and operational cost, and the second one for minimizing both the mean and standard deviation of the total production cost as well the cycle time, in an unreliable three-machine robotic cell which confronted with many uncertainty factors. In the current article, mathematical modelling and simulation-based optimization method have been presented to schedule-allocate similar parts and trace the optimal sequence of different parts. Several solution procedures, including epsilon-constraint method and multiobjective particle swarm optimization algorithm, for identical parts case and response surface methodology for different parts case are applied. The results derived from solving numerical examples revealed some advantages in terms of time to attain the optimal solution.