Module also offered within study programmes:
General information:
Name:
Operational Research in Engineering
Course of study:
2019/2020
Code:
RIME-2-219-WM-s
Faculty of:
Mechanical Engineering and Robotics
Study level:
Second-cycle studies
Specialty:
Wytwarzanie mechatroniczne
Field of study:
Mechatronic Engineering
Semester:
2
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Responsible teacher:
dr hab. inż. Kaczmarczyk Waldemar (wkaczmar@zarz.agh.edu.pl)
Module summary

Introduction to models and methods of Operations Research. The special focus of this course is on practical modelling skills.

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence: is able to
M_K001 be leaders of small project teams and are able to cooperate with professionals of different fields. Engineering project
Skills: he can
M_U001 able to solve basic decision problems. Examination
M_U002 use software tools to model and solve Operations Research problems. Engineering project,
Examination
Knowledge: he knows and understands
M_W001 basic modelling techniques and algorithms of Operations Research. Engineering project,
Examination
Number of hours for each form of classes:
Sum (hours)
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
45 30 0 0 15 0 0 0 0 0 0 0
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
Social competence
M_K001 be leaders of small project teams and are able to cooperate with professionals of different fields. + - - + - - - - - - -
Skills
M_U001 able to solve basic decision problems. + - - + - - - - - - -
M_U002 use software tools to model and solve Operations Research problems. + - - + - - - - - - -
Knowledge
M_W001 basic modelling techniques and algorithms of Operations Research. + - - + - - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 125 h
Module ECTS credits 5 ECTS
Udział w zajęciach dydaktycznych/praktyka 45 h
Preparation for classes 30 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 50 h
Module content
Lectures (30h):

Introduction to models and methods of Operations Research. The special focus of this course is on practical skills. Students will learn fundamental modelling techniques and software tools used to solve decision and optimisation problems.

  1. Mixed Integer Programming
  2. Multi-Criteria programming
  3. Non-Linear programming
  4. Graph theory
  5. Project scheduling
  6. Dynamic programming
  7. Decision theory
  8. Monte Carlo simulation
  9. Discrete simulation
  10. Queueing theory

Project classes (15h):

Exercises and small project:

  1. Mathematical programming
  2. Project scheduling
  3. Decision theory
  4. Monte Carlo simulation
  5. Discrete simulation

Additional information
Teaching methods and techniques:
  • Lectures: During the lecture, the lecturer describes and solves various decision-making problems. For this purpose, he uses a whiteboard, multimedia presentation, spreadsheets, simulations, and other means. To stimulate students' activity, the lecturer asks them questions or initiates discussion.
  • Project classes: By doing laboratory exercises, students learn about various tools available. While performing projects, students learn to formulate decision-making tasks, build correct models, select appropriate methods and on for solving them. If necessary, the lecturer gives guidance. While working in a group on projects, students learn to allocate tasks and coordinate activities in teams. Writing reports on exercises and projects, students learn to formulate observations and conclusions.
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:
  1. All grades are calculated according to the scale compliant with the regulations of the AGH studies. A score of 50% is required to obtain the lowest passing grade (satisfactory, 3.0).
  2. To pass the project classes students have to complete all the laboratory and project assignments, prepare reports and pass each assignment. The final grade is determined as a weighted average of grades for all assignments.
  3. To pass the course, students need a positive total score from all tests. On each test, questions may concern all issues considered in the class, from the beginning of the semester. The overall rating is determined as a simple average of all grades. Oral answers allow students to get additional points for the overall assessment.
  4. If the student does not pass any form of classes at the required date, he/she is entitled to write a retake in a form agreed with the lecturer.
Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: No
    – Participation rules in classes: Students listen to the lecture, and if they do not understand something they should ask questions. If the lecturer asks questions or initiates discussion, students should present their opinion. During the lecture, students should make their own notes, especially when solving tasks on the board. The script for each lecture, in the form of a PDF file, is available before the lecture. While discussing its content, notes may be limited to the student's own observations. After the lecture, and sometimes before, students should read the recommended reading material. It is not allowed to record or film a lecture without the consent of the teacher.
  • Project classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Students perform laboratory exercises under the guidance of the teacher or independently in small groups they carry out different projects. After completing the exercise or project, students give written programs and obtained results, as well as reports containing a description of tasks, methods, observations, and conclusions.
Method of calculating the final grade:

The final grade is calculated as the average of grades with the following weights:

Grade Weight
Exam 80%
Project 20%
Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

Absence from lectures or exercises needs to be made up by studying the issues discussed in the classroom with the help of lecture notes available on the e-learning platform. If someone omits the test, then have to write it on the date agreed with the lecturer.

Prerequisites and additional requirements:

Basics of mathematics including algebra and probability theory are required to participate.

Recommended literature and teaching resources:

Literature available for download in AGH library:

  1. A. Eiselt, Carl-Louis Sandblom, Operations Research, A Model-Based Approach, Springer, 2012.

Course software available in computer laboratory:

  1. Simul8: https://www.simul8.com/
  2. Palisade DecisionTools: http://www.palisade.com/decisiontools_suite/

Course software available for download:

  1. OpenSolver for Excel: http://opensolver.org/
  2. GPLK (with Scite): http://home.agh.edu.pl/~waldek/glpk/
  3. Python 3. and SciPy (WinPython: https://sourceforge.net/projects/winpython/files/)
Scientific publications of module course instructors related to the topic of the module:
  1. Kaczmarczyk, W., Sawik, T., Schaller, A. i Tirpak, T. (2004). Optimal versus heuristic scheduling of surface mount technology lines, International Journal of Production Research, 42(10): 2083-2110.
  2. Kaczmarczyk, W., Sawik, T., Schaller, A. i Tirpak, T. (2006). Production planning and coordination in customer driven supply chains, Wybrane Zagadnienia Logistyki Stosowanej, Tom 3, Komitet Transportu Polskiej Akademii Nauk, s. 81-89.
  3. Kaczmarczyk, W. (2008). Partial coordination may increase overall costs in supply chains, Decision Making in Manufacturing and Services, 2(1-2): 47-62.
  4. Kaczmarczyk, W. (2009b). Practical tips for modelling lot-sizing and scheduling problems, Decision Making in Manufacturing and Services, 3(1-2): 37-48.
  5. Kaczmarczyk, W. (2009c). Modelling multi-period set-up times in the proportional lot-sizing problem, Decision Making in Manufacturing and Services, 3(1-2): 15-35.
  6. Kaczmarczyk, W. (2009d). Inventory cost settings in small bucket lot-sizing and scheduling models, Total Logistic Management, 2: 27-36.
  7. Kaczmarczyk, W. (2011). Proportional lot-sizing and scheduling problem with identical parallel machines, International Journal of Production Research, 49(9): 2605-2623.
Additional information: