Increasing Accuracy of Simulation Modeling via a Dynamic Modeling Approach

Simulating processes is a valuable tool which provides in-depth knowledge about overall performance of a system and caters valuable insight on improving processes. Current simulation models are developed and run based on the existing business and operations conditions at the time during which the simulation model is developed. Therefore a simulation run over one year will be based on operational and business conditions defined at the beginning of the run. The results of the simulation therefore are unrealistic, as the actual process will be going through dynamic changes during that given year. In essence the simulation model does not have the intelligence to modify itself based on the events occurring within the model. The paper presents a dynamic simulation modeling methodology which will reduce the variation between the simulation model results and actual system performance. The methodology will be based on developing a list of critical events in the simulation model that requires a decision. An expert system is created that allows a decision to be made for the critical event and then changes the simulation parameters. A dynamic simulation model is presented that updates itself based on the dynamics of the actual system to reflect correctly the impact of organization restructuring to overall organizational performance

Intelligent Simulation Modeling of a Flexible Manufacturing System with Automated Guided Vehicles

Although simulation is a very flexible and cost effective problem solving technique, it has been traditionally limited to building models which are merely descriptive of the system under study. Relatively new approaches combine improvement heuristics and artificial intelligence with simulation to provide prescriptive power in simulation modeling. This study demonstrates the synergy obtained by bringing together the "learning automata theory" and simulation analysis. Intelligent objects are embedded in the simulation model of a Flexible Manufacturing System (FMS), in which Automated Guided Vehicles (AGVs) serve as the material handling system between four unique workcenters. The objective of the study is to find satisfactory AGV routing patterns along available paths to minimize the mean time spent by different kinds of parts in the system. System parameters such as different part routing and processing time requirements, arrivals distribution, number of palettes, available paths between workcenters, number and speed of AGVs can be defined by the user. The network of learning automata acts as the decision maker driving the simulation, and the FMS model acts as the training environment for the automata network; providing realistic, yet cost-effective and risk-free feedback. Object oriented design and implementation of the simulation model with a process oriented world view, graphical animation and visually interactive simulation (using GUI objects such as windows, menus, dialog boxes; mouse sensitive dynamic automaton trace charts and dynamic graphical statistical monitoring) are other issues dealt with in the study

A control strategy for promoting shop-floor stability

This research aimed to study real-time shop floor control problem in a manufacturing environment with dual resource (machine and labour), under impact of machine breakdowns. In this study, a multiperspective (order and resource perspectives) control strategy is proposed to improve effectiveness of dispatching procedure for promoting shop floor stability. In this control strategy, both order and resource related factors have been taken into account according to information on direct upstream and succeeding workcentres. A simulated manufacturing environment has been developed as a platform for testing and analysing performances of the proposed control strategy. A series of experiments have been carried out in a variety of system settings and conditions in the simulated manufacturing environment. The experiments have shown that the proposed control strategy outperformed the ODD (Earliest Operation Due Date) rule in hostile environments, which have been described by high level of shop load and/or high intensity of machine breakdowns. In hostile environments, the proposed control strategy has given best performance when overtime was not used, and given promising results in reduction of overtime cost when overtime was used to compensate for capacity loss. Further direction of research is also suggested

Scheduling and control in the batch process industry using hybrid knowledge based simulation

This thesis relates to the area of short term scheduling and control in batch process plants. A batch process plant consists of individual plant items linked by a pipe network through which product is routed. The structure of the network and the valve arrangements which control the routing severely constrains the availability of plant items for configuration in routes when a plant is operating. Current approaches to short term scheduling contain simplifying assumptions which ignore these constraints and this leads to unrealistic and infeasible schedules. The work undertaken investigates the use of techniques from the areas of Artificial Intelligence (AI) and Discrete Event Simulation (DES) in order to overcome these simplifying assumptions and develop good schedules which can be implemented in a plant. The main divisions of work cover a number of areas. The development of a representation scheme for batch plant networks, and procedures for reasoning about the constraints imposed by their structure to infer the actual availability of plant items for routing purposes at any time. The development of a dynamic rule-based route configuration procedure which takes into account the constraints on plant item availability. The development of an activity scheduling framework for batch plants based on this. The development of a dynamic simulation model to take account of finite capacity constraints in a batch plant. The integration of these elements in a hybrid structure to make best use of the techniques available from the areas of AI and DES. The representation scheme and procedures developed for reasoning about the constraints in a plant network enable the simplifying assumptions of other approaches to be overcome so that the system can produce good feasible schedules. The hybrid structure is a practical one to take for implementation and enables the best use of techniques from AI and DES