Operational Research and optimization
General data
| Course ID: | 360-FS1-2BOPa |
| Erasmus code / ISCED: |
11.102
|
| Course title: | Operational Research and optimization |
| Name in Polish: | Operational Research and optimization |
| Organizational unit: | Faculty of Mathematics |
| Course groups: |
(in Polish) Erasmus+ sem. zimowy |
| ECTS credit allocation (and other scores): |
4.00
|
| Language: | English |
| Type of course: | elective courses |
| Prerequisites (description): | Student have to know basic notions from Linear algebra and Mathematical Analysis |
| Short description: |
In this course students learn about mathematical models of decision and linoptimization problems, linear programming, nonlinear programming, transportation problem and its generalizations, computer methods of finding optimal solutions, discrete programming, mulicriterial programming and programming under noncertain conditions. After classes they achieve the competence of building models and solving optimization problems including computer programming and interpretation of obtained results. |
| Full description: |
Course profile: academic Form of study: stationary Course type: obligatory Academic discipline: Mathematics, field of study in the arts and science: mathematics Year: 2, semester: 3 Prerequisities: Mathematical Analysis, Linear Algebra lecture 30 h exercise class (in computer laboratory) 30 h Verification methods: lectures, exercises, consultations, studying literature, homeworks, discussions in groups. ECTS credits: 4 Balance of student workload: attending lectures 15x2h = 30h attending exercise classes 15x2h = 30h preparation for classes 7x1h = 7h completing notes after exercises and lectures 7x2h = 14h consultations 5x1h = 5h homeworks: solving exercises 15h = 15h the final examination: preparation.and take 9h + 3h = 12h Quantitative description Direct interaction with the teacher: 60 h., 2 ECTS Practical exercises: 53 h., 2 ECTS |
| Bibliography: |
1. M. Carter, C. Price, G. Rabadi Operations Research. A practical introduction, CRC Press, Taylor & Francis, 2021 2. A. Schrijver Theory of linear and integer programming, Wiley 1998 3. R.G.D. Allen Mathematical Economics, Springer 1959 4. H.A. Eisel, C.I. Sandblom Linear programming and its applications, Springer Verlag 2007 |
| Learning outcomes: |
After classes students achieve the competence of building models and solving optimization problems including computer programming and interpretation of obtained results. |
| Assessment methods and assessment criteria: |
Final exam including practical and theoretical parts. Final mark is according to the grading: 91% - 100% A 81% - 90% B 71% - 80% C 61% - 70% D 51% - 60% E 0% - 50% F |
Classes in period "Academic year 2023/2024" (past)
| Time span: | 2023-10-01 - 2024-06-30 |
 
MO WYK
TU CW
W TH FR |
| Type of class: |
Class, 30 hours
Lecture, 30 hours
|
|
| Coordinators: | Tomasz Czyżycki, Aneta Sliżewska | |
| Group instructors: | Tomasz Czyżycki | |
| Students list: | (inaccessible to you) | |
| Credit: |
Course -
Grading
Class - Grading |
|
| Short description: |
In this course students learn about mathematical models of decision and linoptimization problems, linear programming, nonlinear programming, transportation problem and its generalizations, computer methods of finding optimal solutions, discrete programming, mulicriterial programming and programming under noncertain conditions. |
|
| Full description: |
Plan of classes: 1) Model of decision process 2) Linear programming, model and assumptions 3) Graphical method of solving linear programming problems 4) Simplex method 5) Dual linear problem 6) Nonlinear programming, analytical approach 7) Elements of convex analysis 8) Transportation problem, its generalizations and algorithms 9) Integer programming 10) Multicriterial decision–making 11) Optimization under risk and uncertainty 12) Computer methods in optimization |
|
| Bibliography: |
1. M. Carter, C. Price, G. Rabadi Operations Research. A practical introduction, CRC Press, Taylor & Francis, 2021 2. A. Schrijver Theory of linear and integer programming, Wiley 1998 3. R.G.D. Allen Mathematical Economics, Springer 1959 4. H.A. Eisel, C.I. Sandblom Linear programming and its applications, Springer Verlag 2007 5. Lectures and exercises |
|
Classes in period "Academic year 2024/2025" (past)
| Time span: | 2024-10-01 - 2025-06-30 |
MO TU W TH FR |
| Type of class: |
Class, 30 hours
Lecture, 30 hours
|
|
| Coordinators: | Tomasz Czyżycki | |
| Group instructors: | Tomasz Czyżycki | |
| Students list: | (inaccessible to you) | |
| Credit: |
Course -
Grading
Class - Grading |
|
| Short description: |
In this course students learn about mathematical models of decision and linoptimization problems, linear programming, nonlinear programming, transportation problem and its generalizations, computer methods of finding optimal solutions, discrete programming, mulicriterial programming and programming under noncertain conditions. |
|
| Full description: |
Plan of classes: 1) Model of decision process 2) Linear programming, model and assumptions 3) Graphical method of solving linear programming problems 4) Simplex method 5) Dual linear problem 6) Nonlinear programming, analytical approach 7) Elements of convex analysis 8) Transportation problem, its generalizations and algorithms 9) Integer programming 10) Multicriterial decision–making 11) Optimization under risk and uncertainty 12) Computer methods in optimization |
|
| Bibliography: |
1. M. Carter, C. Price, G. Rabadi Operations Research. A practical introduction, CRC Press, Taylor & Francis, 2021 2. A. Schrijver Theory of linear and integer programming, Wiley 1998 3. R.G.D. Allen Mathematical Economics, Springer 1959 4. H.A. Eisel, C.I. Sandblom Linear programming and its applications, Springer Verlag 2007 5. Lectures and exercises |
|
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