Resource Optimization (ETF RII OR 4755 )

General information

Module title

Resource Optimization

Module code





Computing and Informatics





Module type












Module goal - Knowledge and skill to be achieved by students

  The basic objective of this course is to provide introduction to techniques for defining heuristic algorithms in order to determine solutions of practical problems of resource optimization within the acceptable time. Module illustrates efficient techniques for solving complex problems of decision-making that appear in the resource optimization, both in industry and services, and special attention is devoted to algorithmic aspects and implementation. Students will overwhelm the ability of designing and applying effective heuristic algorithms for solving real complex problems of resource optimization.


  1.Heuristic algorithms for complex optimization problems: design of algorithms. Algorithms based on optimization techniques. Algorithms based on Lagrangian relaxation. Local search procedures. <br>
2.Metaheuristic algorithms: 'Simulated Annealing', 'Tabu Search', Genetic algorithms, hybrid algorithms. <br>
3.Algorithms based on the model of minimum cost, to solve set covering problems. <br>
4.Algorithms for solving Traveling Salesman problem (TSP), packaging (Bin Packing), routing (Vehicle Routing) and the Set Covering problem. <br>
-Performance analysis for described algorithms. <br>
-Modeling of discrete systems. <br>
5.Applications: Problems of distributing products from warehouses to users. Problem of transport for disabled persons. Problems of allocation of vehicles (buses, locomotives) and establishing shifts for personnel in public transport companies. Optimal management of energy systems (distribution of gas, water, electricity). Problems of determining the time schedule for transport companies. Problems of shifts in call centers. <br>


Recommended1. Notes and slides from lectures (See Faculty WEB Site) <br>
2. P. TOTH, Discreet D. VIGO (edited by) ' The Vehicle Routing Problem', SIAM Monographs on Mathematics and Applications 2002. <br>
3. S. HAMMER, P. TOTH ' Knapsack Problems: Algorithms and Computer Implementations', J. Wiley 1990. <br>
4.. R.K. AHUJA, T.L. MAGNANTI, J.B. ORLIN ' Network Flows: Theory, Algorithms and Applications', Prentice Hall 1993. <br>
5. G. GUTIN, To PUNNEN (edited by) ' The Traveling Salesman Problem and its Variations', Kluwer 2002. <br>
Additional1. COLIN R. REEVES Modern Heuristic Techniques for Combinatorial <br>
Problems, McGraw-Hill Inc. 1995 <br>
2. DAVID E. GOLDBERG Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley 1989, 20th Printing November 1999 <br>
3. JERRY BANKS, JOHN S. CARSON II, BARRY L. NELSON, DAVID M. NICOL Discrete-Event System Simulation, Prentice Hall 2001 <br>

Didactic methods

  Through lectures, students will learn about the theory, tasks and applicative examples within thematic units. Lectures consist of theoretical part, presentational descriptive examples, genesis and resolution of specific tasks. In this way, students will have basis for appliance of skilled material in engineering applications. Additional examples and exam tasks are discussed and solved during the laboratory exercises. Laboratory practice and home assignments will enable students of continuous work and their knowledge verification.


  During the course students will collect points according to the following system: <br>
- Attending lectures, exercises and tutorial classes: 10 points, student with more then three absences from lectures, exercises and/or tutorials can not achieve these points; <br>
- Home assignments: maximum of 10 points, assuming solving 5 to 10 assignments evenly distributed throughout the semester; <br>
- Partial exams: two written partial exams, maximum of 20 points for each positively evaluated partial exam; <br>
Student who during the semester achieved less than 20 points must re-enroll this course. <br>
Student who during the semester achieved 40 or more points will access to final oral exam, the exam consists of discussing the partial exams tasks, home assignments and answers to simple questions related to course topics. <br>
Final oral exam provides maximum of 40 points. To achieve a positive final grade, students in this exam must achieve a minimum of 20 points. Students who do not achieve this minimum will access to makeup oral exam. <br>
Student who during the semester achieved 20 or more points, and less than 40 points will access to makeup exam. Makeup exam is structured as follows: <br>
- Written part structured in the same way as a partial written exam, during which students solve problems in topics they failed on partial exams (achieved less then 10 points), <br>
- Oral part structured in the same way as a final oral exam. <br>
Only students who, after passing the written part of the makeup exam managed to achieve a total score of 40 or more points, can access to oral makeup exam, where the score consists of points achieved through attending classes, home assignments, passing partial exams and passing the written part of makeup exam. <br>
Oral makeup exam provides maximum of 40 points. To achieve a positive final grade, students in this exam must achieve a minimum of 20 points. Students who do not achieve this minimum must re-enroll this course. <br>

Aditional notes