– Being able to model single objective problems
– Mastering the solving methods (graphical, spreadsheet, simplex, Big M…)
– Being able to conduct a sensibility analysis.
– Take optimal decisions in the presence of trade-offs between two or more conflicting objectives.
– Be able to solve a transportation problem
– maximize performance whilst minimizing other counstaints at the same time.
– Find a representative set of Pareto optimal solutions, and/or quantify the trade-offs in satisfying the different objectives and/or finding a single solution that satisfies the subjective preferences of a human decision maker
– Mathematical foundations of optimization, modeling and solution techniques for very large and/or complex problems, algorithmic solution methods.
– Duality and transportation problem
– Network optimisation
– Integer programming
– Decision under risk and uncertainty
– Markov chains
