Search

The aim of the paper is to present a case study and to point out the possibilities of using computer simulation for the purpose of increasing the efficiency and efficiency of custom production of a company. The use of simulation modeling as a scientific method in research and in practice brings benefits such as financial, time, material and energy savings, as well as streamlining activities in real practice. The development of advanced simulation systems has opened up new possibilities and significantly supported the trend of streamlining production activities, thus reducing costs and improving business performance. Simulation, however, is not a tool for obtaining an optimal solution, but rather a tool that allows you to test different decision outputs on a simulation model. Such a simulation model makes it possible to carry out various experiments to evaluate, analyse and determine solution parameters that can then be used in a real system. Risk factors can be investigated and determined beforehand by 'replacing' the running simulation model while monitoring system performance and behaviour, then, after applying the required changes, the future behaviour of the system is examined for any potential problems and obstacles. Can be removed in advance. The goal is to analyse the material flow in the production process and then create a simulation to determine the length of production and identify bottlenecks in the production process. In order to get a better idea of the production process, a simulation model was developed in the selected software tool as a custom production project under the conditions of a particular company. The incentive to start production is given to the customer, where every order placed is immediately sent to the customer. The highest frequency order in the enterprise's production program is used to create the material flow. [ABSTRACT FROM AUTHOR]

Silicon content prediction is quite significant for supervising the state of blast furnace and is usually selected as the indicator to represent the thermal state. In practical industry, the fluctuation exists in the operation of blast furnace all the time. What's worse, it is inaccurate to build the predictive model with many outliers. To solve these problems, this paper has developed a model to predict the silicon content using support vector regression (SVR) combined with clustering algorithms, including hard C-means (HCM) clustering and fuzzy C-means (FCM) clustering. Through data processing, the data points are clustered based on the similarity, and then different SVR models are established. In order to make full use of FCM, a new method using multiple SVRs and FCM based on membership degree (MFCM-SVRs) is proposed where the membership degree is applied to eliminate the outliers. Simulation results verify that the multiple SVRs based on HCM (HCM-SVRs) and MFCM-SVRs possess superiority in terms of accuracy and speed, which makes the method serve better for practical production. [ABSTRACT FROM AUTHOR]

In the literature, although a few studies can be found regarding the application of revenue management to special cases of Make-to-Order manufacturing systems with stochastic capacity, there is not any study when capacity or demand (or both) are random variables with general distribution function. Therefore, in this paper, an approach is developed to study a more general case of Make-to-Order manufacturing systems based on the concept of revenue management. Due to the random nature of capacity and demand, the exact size of capacity to satisfy the orders is not known at the time of arriving orders. Consequently, the vital decision is either to accept or reject an order at the time of arrival. If an order is accepted but later rejected due to the lack of capacity, a penalty has to be paid to the customer. On the other hand, an order can be rejected by anticipating the capacity shortage at the time its arrival, while there will be some unused capacity at the processing stage. Then, this also results in the loss of revenue. We assume there are two classes of customers. The price paid by the customers of each class or the penalty paid to them is different from those of the other class customers. Although the objective function which represents the expected total revenue is not necessarily concave, this study demonstrates that it has unimodal property and as a result the existence of an optimal solution is guaranteed. This property has been proved previously for special cases where demand or capacity is continuous random variable. This study confirms this property also holds in more general cases. The proposed approach for a variety of scenarios, discrete and mixed random variables, is investigated by simulation techniques. [ABSTRACT FROM AUTHOR]

Discrete event simulation (DES) has been widely applied to modelling and simulation of computer and engineering systems and is an active field of research that has now evolved from 2D to 3D discrete event simulation. This paper attempts to address several key issues in a successful implementation of DES models based on our own and the previous experiences of others. It describes the common basis, which forms the core for the application of modelling and simulation methodologies that are available to support manufacturing systems analysis, design and performance evaluation. Through a comprehensive literature survey, this paper summarises and compares the most widely used optimisation techniques for simulation of manufacturing systems; an overview of the recent and popular simulation languages and packages available for the modelling and simulation community and the classification of their utility for modelling and simulation of manufacturing systems is also given. Finally, this paper summarises and reports the latest development in the most exciting world wide web (www)-based simulation techniques that represent a future that may completely change the nature and future exploitation of modelling and simulation technology in industry. [ABSTRACT FROM AUTHOR]

This paper presents the salient aspects of developing simulation-based metamodels for scheduling a typical flexible manufacturing system (FMS) operating in a tool-sharing environment. A discrete-event simulation model of the FMS is developed for the purpose of experimentation. Seven scheduling rules from the literature are incorporated in the simulation model for part scheduling decision. The performance measures considered for analysis are mean flow time, mean tardiness, and percentage of tardy parts. Simulation experiments have been carried out for various scenarios arising out of the settings of the mean interarrival time of parts for processing in the system and due-date factor. The simulation results are used to develop regression-based metamodels. These metamodels have been subjected to systematic analysis. The metamodels are found to offer a good prediction of the performance of FMS within the domain of their definition. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of scheduling a set of independent jobs with sequence-dependent setups and distinct due dates on non-uniform multi-machines to minimize the total weighted earliness and tardiness, and explores the use of artificial neural networks as a valid alternative to the traditional scheduling approaches. The objective is to propose a dynamical gradient neural network, which employs a penalty function approach with time varying coefficients for the solution of the problem which is known to be NP-hard. After the appropriate energy function was constructed, the dynamics are defined by steepest gradient descent on the energy function. The proposed neural network system is composed of two maximum neural networks, three piecewise linear and one log-sigmoid network all of which interact with each other. The motivation for using maximum networks is to reduce the network complexity and to obtain a simplified energy function. To overcome the tradeoff problem encountered in using the penalty function approach, a time varying penalty coefficient methodology is proposed to be used during simulation experiments. Simulation results of the proposed approach on a scheduling problem indicate that the proposed coupled network yields an optimal solution which makes it attractive for applications of larger sized problems. [ABSTRACT FROM AUTHOR]

The use of computational process modeling and simulation has become widespread as the real cost of computers has decreased and their capabilities have increased. However, many science and engineering university, faculties have failed to treat this area as a formal undergraduate discipline. The result has been generally a self-taught approach by professionals to both the practice and the management of modeling and simulation. This article describes process modeling and simulation techniques and applications. [ABSTRACT FROM AUTHOR]