Duration: 3 hours for 13 weeks [ECTS: 6]

Course outline

1. PRIOR AND POSTERIOR ANALYSIS IN DECISION MAKING: Construction of Payoff tables, Maximin and Maximax payoff criteria, Minimax regret criterion, Payoff tables with and without additional information, Decision trees with additional information.
2. LINEAR AND INTEGER PROGRAMMING: Problem formulation, Interpreting the solution – sensitivity analysis, Linear and Integer programming applications to business problems, Solving problems using computer software.
3. QUEUING MODELS: Elements of a queuing model and its dynamic evolution, Evaluation criteria of a queuing model in steady-state conditions, Cost models, Capacity determination of a queuing system.
4. INVENTORY MODELS: Inventory systems with stochastic demand, Single period inventory models – The newsvendor problem, Continuous review inventory systems (Q,R), Periodic review inventory systems.
5. MARKOVIAN DECISION ANALYSIS: Markov Chains, Transition Table and the calculation of steady- state probabilities, Applications of decision making with Markovian processes, Solving problems with computer software.

Learning Outcomes

By the end of this course, students will be able to:

1. Apply the Maximin, Maximax, and Minimax regret criteria and analyze the results obtained in decision-making problems under conditions of uncertainty,
2. Understand a complex operational decision-making problem in such a way that, under risk conditions, to construct the necessary decision-making tree, apply the criteria of maximum expected payoff and/or minimum expected regret and evaluate the results obtained by carrying out the appropriate sensitivity analysis,
3. Apply Bayes' theorem for the calculation of posterior conditional probabilities of events as a next step to the prior analysis in decision-making problems, construct posterior payoff tables when additional information is obtained, and evaluate the value of additional information by determining its expected value,
4. Develop linear and integer programming models for a number of “almost” real operational decision-making problems, solve them using appropriate software, evaluate the results obtained by carrying out the necessary sensitivity analysis and make substantive proposals at the decision-making stages,
5. Select the appropriate queueing model according to the characteristics of a service system, calculate the required service system assessment sizes, and estimate the direct and indirect operating costs of that system in order to make substantive proposals at the decision-making stages,
6. Formulate the “best” inventory policy in Newsvendor models, and in continuous and periodic review inventory models.