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An optimization of the production process is defined as the search for solutions with improved production efficiency. Process optimization should be one of the main components of a business strategy that not only delivers benefits to customers but also helps increase the performance of production processes and benefits the entire business. The traditional approaches to job shop scheduling are based on the exact mathematically formalized model. If the number of model parameters is high and the environment is rather uncertain, the practical applications are quite restricted. That is why the theory proposes an approach based on a large-scale computer simulation. The main goal of this paper is to show that in a concrete company case, the simulation-based approach provides increased productivity. The presented study proposes the practical application of the Tecnomatix software used in the research to optimize the production system. The partial aims of the paper are as follows: (1) to create a simulation model of the production system with the help of the Plant Simulation module, (2) to model the current state of matters in the company, and (3) to propose a solution to the problem. Ultimately, we show that the simulation approach to the production line control provides rather effective solutions when compared to the intuitive one based on trial-and-error experience. The improvement includes a significant (1) shortening of the production cycle and (2) increase in productivity. [ABSTRACT FROM AUTHOR]

Simulation models have become promising methods to investigate and improve real-world systems. This paper describes the development of effective simulation-based models for maintenance systems in a manufacturing environment whereby production lines send their faulty but repairable items to a repair shop. The proposed models aim at investigating the centralized storage of repaired items with three replenishing strategies, lot-for-lot (L4L), economic order quantity and reorder point (Q-R), and minimum-maximum order level (s-S) reordering policies of repairable items with backordering allowance in a risk-free and flexible manner to effectively manage the procurement of new repairable items and control three inventories of repairable items, repaired ready-for-use, new, and faulty awaiting repair. The models take into account the uncertain environment of maintenance systems and the probabilistic nature of the failures, repair times, and lead times of repairable items. The demand for repairable items in these three inventories is both stochastic and dynamic. A simulation case problem is presented to demonstrate the suitability and applicability of the proposed simulation models and to study the influence of different parameters on performance measures using parametric analysis. The main results are the vital decision-making insights provided by the developed models about the three reordering policies and the three inventories of repairable items. These managerial insights are essential for achieving minimum inventory costs and maximum service levels. [ABSTRACT FROM AUTHOR]

The delay is a very important element in modeling hardware behavior, and is realized in many hardware description languages such as ADLIB-SABLE, Verilog, and VHDL. The state of the art on hardware delay identifies four classes. In the first class, mean values are used as a precise delay element in the simulation; we found it in VHDL (VHSIC (very high speed integrated circuit) Hardware Description Language), where a single value is utilized to characterize the transport delay. In the second class, the delay is represented by an interval min max, meaning that the delay value is precisely unknown and every value in the interval can represent a possible value for the actual delay. In the third class, a delay is expressed in the form of a stochastic distribution. Fuzzy models of delay constitute the last class. In reality, it is very difficult, if not impossible, to obtain a precise value of the delay; there are many reasons for that: temperature, voltage, variation in the manufacturing process, and other environment parameters. The Min Max DEVS formalism allows an efficient design of the min max delay by proposing a definition of the lifetime function based on time interval. Moreover, its simulation semantics allows the simulation of Min Max DEVS models with only one replication, allowing us to conclude whether the min max delay is too large or exact simulations cannot be obtained. In this paper, we propose to highlight the Min Max DEVS formalism through examples from digital circuits, after having recalled its basic definitions and its simulation semantics. Then, we compare the simulation results obtained with those provided by the well-known tool in the field of digital circuits, Verilog, using the same examples. [ABSTRACT FROM AUTHOR]