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

The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose

Semiconductor manufacturing simulation design and analysis with limited data

This paper discusses simulation design and analysis for Silicon Carbide (SiC) manufacturing operations management at New York Power Electronics Manufacturing Consortium (PEMC) facility. Prior work has addressed the development of manufacturing system simulation as the decision support to solve the strategic equipment portfolio selection problem for the SiC fab design [1]. As we move into the phase of collecting data from the equipment purchased for the PEMC facility, we discuss how to redesign our manufacturing simulations and analyze their outputs to overcome the challenges that naturally arise in the presence of limited fab data. We conclude with insights on how an approach aimed to reflect learning from data can enable our discrete-event stochastic simulation to accurately estimate the performance measures for SiC manufacturing at the PEMC facility

A Demonstration of Promodel

Advances to the state-of-the-art in simulation software have made stochastic modeling more accessible to business school students and faculty. Students are learning to build computer simulation models at an earlier point in their academic program, often in courses that included only manual simulation exercises or spreadsheet problems in the past. Even more promising from a teaching perspective is the capability that advanced simulation software gives faculty to quickly create example models that reinforce the concepts to be covered in class. These models can range from simply illustrative to highly interactive

Dealing with the gray zones in the management of gastric cancer: The consensus statement of the Istanbul Group

The geographical location and differences in tumor biology significantly change the management of gastric cancer. The prevalence of gastric cancer ranks fifth and sixth among men and women, respectively, in Turkey. The international guidelines from the Eastern and Western countries fail to manage a considerable amount of inconclusive issues in the management of gastric cancer. The uncertainties lead to significant heterogeneities in clinical practice, lack of homogeneous data collection, and subsequently, diverse outcomes