Management Science Modeling

The focus of this topic will be mathematical modeling, simulation and analysis of logistics-related problems and operations that can assist in decision making. Quantitative modeling and optimization using Excel is central to this topic. The course contains 1) Introduction to operational analysis 2) Introduction to modeling and optimization in Excel 3) Linear programming, 4) Transport problems and network models 5) Simulation and statistical analysis 6) Inventory management under uncertainty 7) Queue theory with FlexSim, 8) Prediction models.

KNOWLEDGE: The candidate will obtain knowledge of what operations research is and how it can be used to solve selected logistics problems. The candidate will understand and be able to apply the central terminology in the field of operational analysis. The candidate will learn the 7-step systematic modeling process and how to build good spreadsheet models and simulations in Excel. The candidate will understand the characteristics of linear programming models, typical network models for simple distribution networks. The candidate will be enabled to describe and solve / analyze the important challenges in logistics, namely, prediction of demand with uncertainty, inventory modeling, and queue system.

SKILLS: The candidate will gain good skills in quantitative modeling and analysis and become a power-user of Excel. The candidate will be able to recognize, model and solve a linear programming and networking models in Excel and conduct a sensitivity analysis. The candidate will be able to model and solve a stochastic simulation model in Excel. The candidate will be able to calculate central parameters within queue theory. The candidate will be able to model inventory and safety stock subjected uncertain demand and delivery time. The candidate will be able to predict variation in demand and seasonality using various forecasting models.

GENERAL COMPETENCE: The candidate should be able to assess operational analytical issues and find appropriate solution methods. The candidate should be able to critically evaluate his or her own solutions to operational analytical problems. The candidate should be able to solve problems in cooperation with others as well as to communicate these.

Lectures with exercises, teach by working through spreadsheet modeling examples; Individual or group assignments with submission