A computer-assisted performance analysis and optimization (CPAO) of manufacturing systems based on ARENA simulation software (original) (raw)
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Simulation project requires highly qualified multidisciplinary staff rarely available in a Small and medium-sized enterprise (SME). The aim of this paper is to develop a computerassisted performance analysis and optimization (CPAO) to help a SME manager which is considered in this paper as an inexperienced user in applying a simulation project without using explicitly the ARENA ® software. After the design of the suitable simulation model with ARENA ® software by an expert simulation modeler, the inexperienced user of CPAO can operate the process of simulation and optimization easily and simply. Major manipulations include the following. The setting of possible configurations. (2) The statistical analysis and graphical analysis of simulation results. (3) The improvement and the optimization of some criteria. The developed CPAO application is carried out in two steps. Firstly, the Unified Modeling Language (UML) is employed for the CPAO design phase. Secondly, Visual Basic Administration (VBA) language is exploited to develop various User Forms dialogues with the inexperienced user, ARENA software, Ms Excel and Ms Access.
Simulation as a Tool for Process Optimization in a Manufacturing Company
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Computer simulation is a very important method for studying the efficiency of manufacturing systems. This paper presents the results of simulation research about how buffer space allocated in a flow line and operation times influence the throughput of a manufacturing system. The production line in the study consists of four stages and is based on a real machining manufacturing system of a small production enterprise. Using Tecnomatix Plant Simulation software, a simulation model of the system was created and set of experiments was planned. Simulation experiments were prepared for different capacities of intermediate buffers located between manufacturing resources (CNC machines) and operation times as input parameters, and the throughput per hour and average life span of products as the output parameter. On the basis of the experiments, the impact of the allocation of intermediate buffer capacities on production efficiency is analysed.
[PDF]Optimisation of a Production Line using Simulation and
This paper presents the use of simulation to assist the decision-making process involved in implementing lean manufacturing principles at a carton box die factory. The paper describes the application of discrete event simulation to improve and optimise the performance of the carton box die assembly line. Simulation experiments measure each system's resource requirements and performance, quantifying benefits to be derived from applying the principles of lean manufacturing. In this study, Simio is used to model and simulate different experimental scenarios in order to quantify the impact of selected input parameters on objective functions such as lead time. Results show that changes in the layout can reduce workers' movements and increase productivity.
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Productivity plays a significant role for most companies in order to measure the efficiency. In reality there is an essential need to evaluate the effect of different factors which are increasing productivity and achieving the high level of quality, high production rate, machine utilization. On the other hand, manufacturing companies are striving to sustain their competitiveness by improving productivity and quality of manufacturing. This can be acquired by finding ways to deal with various problems which have affected the productivity of manufacturing systems. This paper aims at applying design of experiment (DOE) and computer simulation to develop a model for predicting production line productivity by determining the optimum main factors level. One paint factory was selected as the case study. The production line was simulated by Arena 13.9 software. Following that, DOE was conducted to determine which factors have the most significant effect on the productivity. Final result showed that two factors B (Number of labor) and C (Failure time of lifter) have the most significant effect on the manufacturing system productivity. Based on the final model, to achieve the maximum productivity, the factors should be placed on the levels of A =-1, B = 1, C = 1, and D = 1. This combination translates to the service rate of mixer = UNIF (20, 40), number of labor = 20, failure time of lifter = 60min and number of permil = 5, respectively.