One of the primary concerns with data envelopment analysis arises when the number of inputs and outputs increases. In such a case, required computations will become time consuming and there is a concern on developing some hybrid methods to increase the capability of this method. In this paper, a combination of data envelopment analysis and multi-layer neural network is proposed, in a case study, for predicting the performance of Alborz insurance subsidiaries in large cities in Automobile insurance firms. This research can be used in the future databases for performance prediction. On the other side, these results can aid Alborz insurance in its future investments as well as the performance index usage.

This paper introduces the effect of task deterioration in simple assembly line balancing problem. In many realistic assembly lines, a deterioration task is considered when a task is started earlier than the assigned time since the station time is constant and the earliness of the task does not reduce the cycle time. This phenomenon is known as deteriorating tasks. Therefore, we seek an optimal assignment and schedule of tasks in workstations, in order to minimize the number of stations for a given cycle time, which is known as SALBP-1. For this purpose, a mathematical model is proposed. Since the pure SALBP-1 is proved to be NP-hard and considering task deterioration complicates problem further, we propose a genetic algorithm for solving such problem. Several well-known test problems are solved to study the performance of the proposed approach.