Deep reasoning procedures are model-based, inferring single or multiple causes and/or timing relations from the knowledge of behavior of component models and their causal structure. The overall goal of this paper is to develop an automated deep reasoning methodology that exploits deep knowledge of structure and behavior of a system. We have proceeded by building a software environment that uses such knowledge to reason from advanced symbolic simulation techniques introduced by Chi and Zeigler. Such reasoning system has been implemented and tested on several examples in the domain of performance evaluation, and event-based control.

Efficient event evaluation and propagation techniques are proposed to enhance the advancement of simulation clocks of conservative and optimistic logic simulation protocols on parallel processing environments. The first idea of the techniques proposed in this paper is to allow more than one event evaluation per simulation cycle and to pack more than one propagation event in a single message. The second idea is to use advancement windows resulted in good performance in parallelism and execution times.

In many real-world simulation studies the several measures of performance are of interest simultaneously. There exist very limited number of studies that explain and suggest the methods or procedures of inferencing the system performances at the same time. This study presents a procedure for determining the number of simulation observations required to achieve the prespecified confidence level for several measures of system performance. Mean values are selected as the measures, for instance, expected ordering cost, expected holding cost, and expected shortage cost for a given period of time in the study of inventory problems. Basically, the batch means approach is applied and extended to develop an algorithm to carry out the procedure handling more than single parameter. The efficacy of the presented method is assessed through the experiments. The empirical results based on some stochastic systems such as queues and inventory problems show that the suggested method produces as excellent result in terms of the precision of estimated means and the number of observations required.

This paper examines the use of fuzzy cognitive maps to help understand manufacturing strategy processes. This technique is illustrated through a case study of a manufacturing strategy formulation. Advantages of fuzzy cognitive maps are highlighted by comparing the level of understanding before and after the technique was used. The merit from the use of fuzzy cognitive maps for manufacturing strategy formulation is also discussed. (Manufacturing Strategy Formulation, Fuzzy Cognitive Map, Strategy Process Model, Feedback)

Performance measurement and analysis for computer systems has been studied for a long time. However, little attention has been focused on how to distribute a given budget to each part of a computer system for enhancing system performance maximally. In traditional approaches, performance enhancement is achieved by identifying a dominant system bottleneck and enhancing the bottleneck's performance. These approaches, however, often bring problems since the removal of a bottleneck may result in other bottlenecks. This paper proposes an approximated method for such budget distribution problem. For budget distribution, a critical set is defined. The set contains the servers of which performance changes affect the overall system performance significantly. Then, the given budget is distributed properly to every server in the critical set. To verify the proposed method, two benchmark experiments are carried out I SLAM environments. The experimental results show that the proposed method provides better results than the traditional method does in many cases.

For many practical and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. In this paper, with discussion of simulation optimization techniques, a case study in machining process for application of simulation optimization is presented. Most of optimization techniques can be classified as single-or multiple-response techniques. The optimization of single-response category, these strategies are gradient based search methods, stochastic approximate method, response surface method, and heuristic search methods. In the multiple-response category, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphical method, direct search method, constrained optimization, unconstrained optimization, and goal programming methods. The choice of the procedure to employ in simulation optimization depends on the analyst and the problem to be solved.

Traditionally, simulation-based learning games which are known as flight-simulators have been constructed as a black-box game. Within a black-box game, game-players can view and modify only a part of model parameters. Game-players cannot change the structure of a simulation model. In a black-box game, game-players cannot understand and learn the system structure which is responsible for the system behavior. In this paper, the multi-tasking at the level of operating systems is exploited to enhance the transparency of simulation-based learning game. The white-box game or transparent-box game allows game-players ot view and modify the model structure. The multi-tasking solution for white-box learning game is implemented with Smalltalk language on MS-/windows operating system.