General information:
Name:
Advanced Planning in Operations Management
Code:
UBPJO-371
Profile of education:
Academic (A)
Lecture language:
English
Semester:
Spring
Responsible teacher:
dr hab. inż. Kaczmarczyk Waldemar (wkaczmar@zarz.agh.edu.pl)
Module summary

This course provides an accessible introduction to standard quantitative methods for planning and scheduling of production and logistic operations.

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. Execution of a project
M_K002 acquire knowledge by oneself. Execution of a project
Skills: he can
M_U001 determine basic plans and schedules for typical production and logistic operations. Test,
Execution of exercises,
Execution of a project
M_U002 identify the type of a production or logistic process, to choose and implement appropriate optimisation model for arising planning or scheduling problem. Test,
Execution of exercises,
Execution of a project
Knowledge: he knows and understands
M_W001 basic modelling techniques of management science applied in planning and scheduling of production and logistic operations. Test,
Execution of a project
M_W002 standard quantitative methods, optimisation models and information systems applied to enterprise-wide operational planning and supply chain coordination. Test,
Execution of a project
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
60 30 15 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. - + - + - - - - - - -
M_K002 acquire knowledge by oneself. - + - + - - - - - - -
Skills
M_U001 determine basic plans and schedules for typical production and logistic operations. + + - - - - - - - - -
M_U002 identify the type of a production or logistic process, to choose and implement appropriate optimisation model for arising planning or scheduling problem. + + - + - - - - - - -
Knowledge
M_W001 basic modelling techniques of management science applied in planning and scheduling of production and logistic operations. + + - + - - - - - - -
M_W002 standard quantitative methods, optimisation models and information systems applied to enterprise-wide operational planning and supply chain coordination. + + - - - - - - - - -
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 60 h
Preparation for classes 35 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 30 h
Module content
Lectures (30h):

Managers and information technology professionals should understand the basic methods of enterprise-wide operational planning and supply chain coordination. This course provides an accessible introduction to standard quantitative methods for planning and scheduling of production and logistic operations, basic modelling techniques, optimisation models and information systems applied in operational planning. Basic outcome of this course is understanding of various planning problems and models, their objectives and constraints, chances which give us advanced planning systems and their limitations. Mathematical models are in the background. Assignments give students hands-on experience at developing models and solving problems. Course software and e-books are available to download.

  1. Mixed integer programming (MIP): standard models and tools.
  2. Sales and operations planning (SOP).
  3. Operational planning:
    • master production scheduling (MPS),
    • material requirements planning (MRP I),
    • manufacturing resource planning (MRP II).
  4. Optimisation models for replenishment planning:
    • economical order quantity (EOQ),
    • inventory policies for probabilistic demand,
    • dynamic lot-sizing problem.
  5. Optimisation models for production planning:
    • lot-sizing and scheduling problems,
    • resource-constrained project scheduling problems (RCPSP),
    • machine scheduling problems,
    • safety stocks, lead times and due date setting.
  6. Optimisation models for distribution planning:
    • inventory routing problems,
    • production and distribution coordination models.
  7. Advanced planning systems (APS):
    • general characteristics and structure,
    • decomposition and aggregation,
    • integration and coordination,
    • collaboration,
    • implementation,
    • case studies.

Content of the E-learning platform:

  1. Lecture notes.
  2. E-books.
  3. Spreadsheet MIP models:
    • SOP,
    • MRP II,
    • lot-sizing and scheduling problems,
    • resource-constrained project scheduling problems (RCPSP).
  4. Simulation models:
    • queuing systems,
    • reorder point replenishment policy.

Auditorium classes (15h):

Determining plans “by hand” on the whiteboard:
• MRP, CRP,
• resource-constrained project scheduling problem (RCPSP),
• machine scheduling problems,
• inventory control,

Case studies.

Project classes (15h):

  1. Introduction to mixed integer programming (MIP): standard models and tools. Determining plans “by hand” in a spreadsheet and with help of solver for models described in a spreadsheet (OpenSolver) or in algebraic modelling language (AMPL, GLPK):
    • SOP,
    • MRP II,
    • lot-sizing and scheduling problem,
    • resource-constrained project scheduling problem (RCPSP).
  2. “Optimisation” of policy parameters with help of Monte Carlo and discrete simulation for:
    • inventory control,
    • lead time setting,
    • due date setting,
    • capacity planning.

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.
  • Auditorium classes: Students first try to solve the scheduled assignments themselves before each class and then during the class solve the same tasks under the guidance of the lecturer. If necessary, the lecturer suggests next solution steps, corrects mistakes, points out typical mistakes, indicates alternative solutions, provides additional explanations, initiates discussions on the results.
  • 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 auditorium exercises, 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.
  3. 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.
  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.
  • Auditorium classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Before the exercises, students should recall the content of earlier lectures and try to solve the scheduled assignments. During the exercises, when one of the students solves the and on at the whiteboard, others solve them in parallel in their notebooks. Students should report their doubts on whether the solution presented on the whiteboard is correct, ask for additional explanations if they do not understand the solution method.
  • 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
Tests 70%
Projects 30%
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:

This course is for postgraduate students of management, engineering, and computer science. Basics of mathematics including algebra and probability theory are required to participate.

Recommended literature and teaching resources:

Fundamental literature:

  1. Silver E. A., Pyke D. F., Peterson R., Inventory Management and Production Planning and Scheduling, Wiley, 1998
  2. Vollmann T. E., Berry W. L., Whybark D. C., Jacobs F. R., Manufacturing Planning and Control for Supply Chain Management, McGraw-Hill/Irwin, 2004.

Literature available for download in AGH library:

  1. Drexl A., Kimms A., Lot sizing and scheduling – survey and extensions, European Journal of Operational Research, 99(2), 1997, str. 221-235.
  2. Hartmann S., Project Scheduling under Limited Resources, volume 478 of Series, str. Lecture Notes in Economics and Mathematical Systems, Springer Berlin, 1999.
  3. Neumann K., Schwindt Ch., Activity-on-node networks with minimal and maximal time lags and their application to make-to-order production, OR Spektrum, 19, 1997, str. 205-217.
  4. Stadtler H., Kilger Ch., editors, Supply Chain Management and Advanced Planning, Springer, Berlin, 2008.
  5. Voß S., Woodruff D. L., Introduction to Computational Optimization Models for Production Planning in a Supply Chain, Springer, 2006.

Course software available in the 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/
Scientific publications of module course instructors related to the topic of the module:
  1. Kaczmarczyk, W. (2008). Partial coordination may increase overall costs in supply chains, Decision Making in Manufacturing and Services 2(1-2): 47-62.
  2. Kaczmarczyk, W. (2009b). Practical tips for modelling lot-sizing and scheduling problems, Decision Making in Manufacturing and Services 3(1-2): 37-48.
  3. 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.
  4. Kaczmarczyk, W. (2009d). Inventory cost settings in small bucket lot-sizing and scheduling models, Total Logistic Management 2: 27-36.
  5. Kaczmarczyk, W. (2011). Proportional lot-sizing and scheduling problem with identical parallel machines, International Journal of Production Research 49(9): 2605-2623.
  6. 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.
  7. 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.
Additional information:

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