operator allocation

The purpose of this article is to present an approach based on simulation optimization for improving the performance of cellular manufacturing systems through optimizing operator allocation and job dispatching rules in each cell. In this study, we have considered stochastic parameters, machines’ breakdown and multiple products in order to consider the problem as close as possible to real-world situation. The presented approach is composed of Taguchi design of experiments, discrete event simulation, artificial neural networks, and data envelopment analysis. First, controllable and response variables are determined based on the objective of the study and expert judgment. Then, the design of experiments is used in order to develop experimental scenarios base on controllable variables. Furthermore, simulation is used to evaluate experimental scenarios and their related response variables. Then, in order to expand the experimental results to the whole feasible solution space, artificial neural networks is used. Finally, the optimum scenario is determined using data envelopment analysis. After determining the optimum scenario, it is compared to the present condition of the case and the improvements are determined. In order to evaluate the performance of the presented approach, a lathing factory which uses a cellular manufacturing system is considered as the case study. Due to the fact that the high volume of manufacturing systems around the world forms the cellular manufacturing system, optimization of these lines has been of great importance and so far have been studied by many researchers in this regard. Most researchers have considered the problems in simple terms and ignored many of the assumptions. They have been optimized cellular manufacturing line problems by using mathematical modellings and meta-heuristic algorithms, but it should be noted that assumptions such as the uncertainty of problem parameters, machines’ breakdown and variable demand are among the existing and dominant conditions in cellular manufacturing problems, which, by taking them, can bring the problem as close as possible to real-world conditions, and, on the other hand, research results become more practical. Because of the complicated nature of such problems, mathematical modelling will not be efficient and useful. In this situation, simulation is one of the best approaches at hand. By using simulation modelling, it is possible to consider all parameters of the problem, stochastic, which make the model much closer to reality. The purpose of this study is to present an approach for optimizing operator allocation and job dispatching rules on machines in a cellular manufacturing ambience, in order to minimize delay costs per piece and maximizing the average efficiency of machines. Since the model of this study is seeking multiple objectives, the simulation model of the problem includes several responses. In the end, the operator's optimum number for allocation to each cell and the optimal job dispatching rules in each cell will be determined with the aim of achieving the objectives of the problem. Azadeh et al. used fuzzy data envelopment analysis (FDEA) and computer simulation to optimize operator allocation in a cellular manufacturing system. They indicated the effectiveness and superiority of the method through a practical case study (Azadeh et al., 2010)(Azadeh, 2010 #9;Azadeh, 2010 #9). Besides, an approach for multi-response optimization problem by using artificial neural network (ANN) and data envelopment analysis (DEA) is studied by Bashiri et al., (2013). Studies have been done so far show that optimal operator allocation along with the optimal job dispatching rules in the cellular manufacturing system has not been performed in the stochastic conditions, and from this point of view, the present study is unique. This section describes the proposed methodology which is illustrated in Figure 1 In the present study, the cellular manufacturing system was first evaluated and the data needed to simulate the system were collected. After the initial simulation of the manufacturing system in ARENA simulation software, controllable variables were determined according to the features of the manufacturing system. Then, using Taguchi’s experimental design method in Minitab software, experimental scenarios were designed by various combinations of controllable variables. Then, the simulation model was modified and simulated according to any experimental scenario, and the problem response variables, that were the same problem objective functions, were extracted. After extracting the results of the experimental scenarios, considering that without evaluating other not tested scenarios, it is impossible to identify the optimum scenario, by using artificial neural networks, the experimental results were expanded to the entire possible modes. For this purpose, data on experimental scenarios with their results were placed as training data in the neural network. After setting the parameter, the optimal neural network was identified. Table (1) shows that the network number 7 with 6.8% error is chosen as the optimal structure of the neural network.