This paper presents a methodology for the selection of an optimal investment variant using Monte Carlo simulation and OptQuest optimization. The decision-making process also includes risk analysis. Investment variants involve renewal and development of production equipment. Two approaches to investment decision making are introduced. The first approach is based on the analysis of the distribution function of Net Present Value (NPV), and the rule of mean value and coefficient of variation is used as the decision criterion for determining the profitability of investment variants. The second approach, based on the cumulative probability distribution of NPV, provides a comparative assessment of the investment variants using stochastic dominance rules. Both approaches lead to the choice of the same investment variant. In order to increase the profitability of the selected investment variant and reduce its risk, OptQuest optimization is subsequently implemented. The introduced approaches can be a useful support tool in investment decision-making.
The paper presents quantitative approach for management decisions of the manufacturing system for production of fireplaces, related to evaluation of key parametersproductivity and throughput, which most authors and methodologies consider to be substantial. Methodology was based on creating the simulation model of the fireplace production line in software Witness; optimizing the production capacity by selecting constraints, based on results from simulation model; evaluating the simulation experiments with the goal to increase productivity; setting production to maximize sales profits using Simplex method. Simulation model was built according to a technological process of fireplaces in a semi-automated production. Improvement in a production process within theory of constraints philosophy is complemented by mathematical modelling -Simplex method, that estimate profit maximization in case the company management decides to produce more product variants.
The current mechanical engineering is inconceivable without the implementation of CAx systems in design and manufacturing process of individual components. The automotive industry is a clear evidence of how CAx systems affect the innovation cycle of its product -a car. The innovation cycle in automotive was reduced from 8-12 years to the current 4-6 years. Even in this short interval automakers make some small design modifications called a facelift. Development in the automotive industry, therefore, is closely related to news and functionality CAx systems. CAD systems at the turn of the millennium are characterized as parametric graphic systems with a history tree of product creation. Parametric design implemented into CAD systems makes the model variable and open to rapid change management. The history tree in turn enables rapid editing and modification of forming or editing functions.
Simulations are widely used in manufacturing system design, production planning and decision making. The aim of this paper is to present the possibility of using Monte Carlo simulations in the production plan optimizing and in the project risk management. Optimization is accomplished through two different approaches which principles and results are mutually compared. According to the first approach, production optimization is performed via a deterministic model using the Generalized Reduced Gradient algorithm. The second approach is based on the stochastic model. The optimized production plan is submitted to risk analysis. Two approaches are demonstrated in order to reduce the rate of risk. The first way is modifying the production plan to increase the forecast reliability; the second approach is limiting the uncertainty of key variables. The detailed methodology enables implementing presented approaches in solving various optimization tasks.
Demand forecasting is very often used in production planning, especially, when a manufacturer needs in a longer production cycle to respond flexibly to market demands. Production based on longer-term forecasts means bearing the risk of forecast unreliability in the form of finished product inventory deficit or excess. The use of computer simulation allows us to improve the planning process and optimise the plan for the intended goal. This paper presents the use of quantitative forecasting and computer simulations to create the production plan. Two approaches to production plan creation are demonstrated in a model case study. Products are characterized by varying demand and are produced on a single production line in continuous operation. The first approach uses ARIMA(2,0,2) (Auto-Regressive Integrated Moving Average) prognostic method selected as the most reliable method based on MAPE (Mean Absolute Percent Error). The second method applies Monte Carlo simulations and optimisation. The aim of the plan optimisation is minimisation the total costs connected with line rebuilding and storage of products. The comparison of the two approaches shows that planning using computer simulations and optimisation leads to lower total costs.
The main goal of the paper is to propose solution toward capacity improvement of assembly process on a selected production line. The major content is focused on Basic Most method and its application in improvement process. There is a theoretical expectation that after applying Basic Most method to the assembly process, the capacity of production line would increase about twenty percentages. Motion and Time studies are often applied for improvements in efficiency of assembly processes. First, Process Screen method is used for gaining the information about process behaviour and its individual, partial and total duration. The final solution consists of new layout design of working station at the selected production line.
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