AIMS AND OBJECTIVES OF THE COURSE: The aim of the discipline is to introduce to
basic areas and problems in production, service and operational management, to provide basic
knowledge about main probabilistic notions, contemporary approaches and methods of
science for operations management and research and to build basic skills for their application
in analysis and decision making, connected with manufacturing management, service and
operations.
DESCRIPTION OF THE COURSE: The main topics concern: introduction to functional
areas and problem in POM; data representation and analysis in POM; forecasting techniques –
regression analysis, moving average and exponential smoothing; decision making under risk –
expected value-variance criterion, aspiration level criterion, decision trees; decision making
under uncertainty – Laplace, minmax(maxmin), Savage and Hurvicz criteria; Markovian
decision processes – Markov chains, steady-state probabilities, exhaustive enumeration
approach, policy improvement algorithm; Markovian decision processes in inventory control;
queuing models – specialized Poisson models of type (M/M/C), specification of production
machines and overhaul management; planning and scheduling, service systems; ADD S/TH;
Mixed Integer Programming application in optimal dispatching of production systems;
Inventory models – deterministic static and dynamic EOQ models.
