| CODE | SOR3320 | ||||||||
| TITLE | Integer and Dynamic Programming | ||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||
| MQF LEVEL | Not Applicable | ||||||||
| ECTS CREDITS | 4 | ||||||||
| DEPARTMENT | Statistics and Operations Research | ||||||||
| DESCRIPTION | - Separability and separable programming - Integer Programming - Indicator variables and logical constraints - Combinatorial problems and preprocessing - Solution techniques - Two-stage decision model and decomposability - Parametric analysis and conditional optimization - States and modified models - Multistage deterministic decision models - Policies and functional equation - Principle of optimality and solution techniques - Selected problems solved by Dynamic Programming (work force size model, equipment replacement model, inventory model, etc.) Study-unit Aims: Teaching Integer Linear Programming and Dynamic Programming with finite horizon to science students taking Statistics & OR as one of the courses. Queuing Theory involves the application of probability and the theory of stochastic processes. It models systems with discrete behavior that can be formalized as queuing systems made of queues and servers. This includes both single queue systems and queuing networks. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to demonstrate understanding of the theoretical backgrounds of Integer Linear Programming and Dynamic Programming with finite horizon. For selected mathematical models they will be able to list the assumptions and assess applicability of results. 2. Skills: By the end of the study-unit the student will be able to: - Analyze given practical situations from the Integer Linear Programming or Dynamic Programming with finite horizon perspective. - Select appropriate mathematical model by checking its assumptions with the reality and finding the input parameters. - Solve the model by using appropriate software tools. - Interpret the results in given mostly optimization or decision making context. Suggested Texts: - Sniedovich, M. (1992) Dynamic Programming, Marcel Dekker. - Minoux, M. (1986) Mathematical Programming - Theory and Algorithms, Wiley. - Nemhauser, G.L., and Wolsey, L.A. (1998) Integer and Combinatorial Optimization, Wiley. - Williams, H.P. (1999) Model Building in Mathematical Programming - 4th edition, Wiley. - Hastings, N.A.J. (1973) Dynamic Programming with Management Applications, Crane-Russak. - Bertsekas, D.P. (1987) Dynamic Programming: Deterministic and Stochastic Models, Prentice Hall Inc. |
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| ADDITIONAL NOTES | Pre-requisite Study-units: SOR1310 and SOR1320 | ||||||||
| STUDY-UNIT TYPE | Lecture and Practical | ||||||||
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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