Gönenç Yücel

This course is designed to provide students with the tools and knowledge necessary to conduct a simulation supported analysis of socio-technical problems using agent-based models (ABMs). Students will gain understanding and awareness of the fundamental differences of agent-based modeling from other simulation modeling approaches, and nature of problems/objectives that ABMs fit the best. Besides, students will develop competency in building ABMs, analyzing and interpreting results from these models, and communicating a complete simulation supported analysis cycle to peers/clients. Example models used during the semester will be drawn from social, economic, environmental, industrial, energy and logistic/transportation problems. For the term project, students will go through a model supported analysis process as they develop an ABM in order to analyze a problem from their own areas of interest.

This course is designed to provide students with the tools and knowledge necessary to conduct a simulation supported analysis of socio-technical problems using agent-based models (ABMs). Students will gain understanding and awareness of the fundamental differences of agent-based modeling from other simulation modeling approaches, and nature of problems/objectives that ABMs fit the best. Besides, students will develop competency in building ABMs, analyzing and interpreting results from these models, and communicating a complete simulation supported analysis cycle to peers/clients. Example models used during the semester will be drawn from social, economic, environmental, industrial, energy and logistic/transportation problems. For the term project, students will go through a model supported analysis process as they develop an ABM in order to analyze a problem from their own areas of interest.

Fundamentals of supply chain management and enterprise resources planning (ERP); aggregate production planning: static, dynamic, nonlinear and lot sizing models; operations scheduling: flow shops and job shops; materials management and materials requirement planning (MRP); capacity resources planning (CRP); distribution system management; implementation of manufacturing management strategies.

Nature and classification of production systems. Product design. Forecasting methods; simple linear regression, moving average and exponential smoothing methods. Capacity requirements planning. Design of discrete production systems: product–based layout and assembly line balancing; process-based layout and design of work stations; group technology and cell design; material handling and storage systems. Facility location; discrete and continuous space location models.

Basic concepts of discrete-event simulation modeling/analysis. Event-scheduling versus Process-interaction approach. Random number and random variate generation; inverse transformation and other selected techniques. Input data analysis and goodness of fit tests. Specific computer simulation languages. Analysis of simulation output and model validation.