Decision Support & Optimization

Decision Support & Optimization Learning Objectives

  1. Gain overview of Operations research models, Methods
  2. Gain a Mathematical toolbox and use computer aided modelling (e.g.Excel Solver) Overview and Models & Methods
  3. Gain awareness of promising recent/emerging fields of operations research related to integrated logistics and physical internet and resilient supply chains.
  4. Tackle Optimization & Scheduling Problems
  5. Know how to select and use design models and quantitative solution techniques for Scheduling (Production, Transportation, data processing etc)
  6. Modelling strategic and operational decisions and results analysis

Decision Support and Optimization in Supply Chain Management Content
The development of flexible manufacturing systems has played a significant role in the field of computer-integrated manufacturing. Such systems work at very high complexity levels and, thus, require a wide use of scientific and management methods. Provides a comprehensive overview of the basic concepts for modeling scheduling problems. The design models and quantitative solution techniques that are proposed for traditional workshops are helpful for tackling more complicated manufacturing systems. Numerical examples and illustrative problems are included to facilitate the understanding of the models presented.
Standard operations research models and optimization methods for decision-making
LP, Network and MIP model formulation
Solving LPs, Network and MIP models with the Excel solver
Introduction to heuristic methods
Decision support system tools and environments
Applications (production planning, sequencing, scheduling, transportation and routing problems)

Modelling and Simulation in Logistics Content
Modelling strategic and operational decisions
Simulation methods for deterministic and stochastic situations
Mathematical toolbox and computer aided modelling
Simulation: models and languages, validation and experiences planning for result analysis
Numerical simulator (case studies based on a commercial simulator)
Monte Carlo methods