Module also offered within study programmes:
General information:
Code:
UBPJO-255
Name:
Advanced Planning in Operations Management
Profile of education:
Academic (A)
Lecture language:
English
Semester:
Spring
Responsible teacher:
dr hab. inż. Kaczmarczyk Waldemar (wkaczmar@zarz.agh.edu.pl)
Academic teachers:
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
M_K001 Students can be leaders of small project teams and are able to cooperate with professionals of different fields. Execution of a project
M_K002 Students are able to acquire knowledge by oneself. Execution of a project
Skills
M_U001 Students are able to determine basic plans and schedules for typical production and logistic operations. Execution of a project,
Execution of exercises,
Test
M_U003 Students are able to identify the type of a production or logistic process, to choose and implement appropriate optimisation model for arising planning or scheduling problem. Execution of a project,
Execution of exercises,
Test
Knowledge
M_W001 Students know basic modelling techniques of management science applied in planning and scheduling of production and logistic operations. Execution of a project,
Test
M_W004 Students know standard quantitative methods, optimisation models and information systems applied to enterprise wide operational planning and supply chain coordination. Execution of a project,
Test
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
Others
E-learning
Social competence
M_K001 Students can be leaders of small project teams and are able to cooperate with professionals of different fields. - + - + - - - - - - -
M_K002 Students are able to acquire knowledge by oneself. - + - + - - - - - - -
Skills
M_U001 Students are able to determine basic plans and schedules for typical production and logistic operations. + + - - - - - - - - -
M_U003 Students are able to 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 Students know basic modelling techniques of management science applied in planning and scheduling of production and logistic operations. + + - + - - - - - - -
M_W004 Students know standard quantitative methods, optimisation models and information systems applied to enterprise wide operational planning and supply chain coordination. + + - - - - - - - - -
Module content
Lectures:
  1. Managers and information technology professionals should understand 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:
    . 3.1master production scheduling (MPS),
    . 3.2 material requirements planning (MRP I),
    . 3.3 manufacturing resource planning (MRP II).
    4. Optimisation models for replenishment planning:
    . 4.1 economical order quantity (EOQ),
    . 4.2 inventory policies for probabilistic demand,
    . 4.3 dynamic lot-sizing problem.
    5. Optimisation models for production planning:
    . 5.1 lot-sizing and scheduling problems,
    . 5.2 resource-constrained project scheduling problems (RCPSP),
    . 5.3 machine scheduling problems,
    . 5.4 safety stocks, lead times and due date setting.
    6. Optimisation models for distribution planning:
    . 6.1 inventory routing problems,
    . 6.2 production and distribution coordination models.
    7. Advanced planning systems (APS):
    . 7.1 general characteristics and structure,
    . 7.2 decomposition and aggregation,
    . 7.3 integration and coordination,
    . 7.4 collaboration,
    . 7.5 implementation,
    . 7.6 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:

Determining plans “by hand” on the whiteboard:
1. MRP II,
2. resource-constrained project scheduling problem (RCPSP),
3. (classic) scheduling problems,
4. inventory control,
Case studies.

Project classes:

(In computer laboratory, in the first part of the semester)

1. Introduction to mixed integer programming (MIP): standard models and tools.
Determining plans “by hand” in spreadsheet and with help of solver for models described in spreadsheet (OpenSolver) or in algebraic modelling language (AMPL, GLPK):
2. SOP,
3. MRP II,
4. lot-sizing and scheduling problem,
5. resource-constrained project scheduling problem (RCPSP).

“Optimisation” of policy parameters with help of Monte Carlo and discrete simulation for:
6. inventory control,
7. lead time setting,
8. due date setting,
9. capacity planning.

Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 125 h
Module ECTS credits 5 ECTS
Participation in lectures 30 h
Participation in auditorium classes 15 h
Participation in project classes 15 h
Preparation for classes 35 h
Completion of a project 30 h
Additional information
Method of calculating the final grade:

Exercise assignments 10%, project (case study) 30%, written tests 60%.

Prerequisites and additional requirements:

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

Recommended literature and teaching resources:

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 for download:

  1. OpenSolver for Excel: http://opensolver.org/
  2. SolverStudio for Excel: http://solverstudio.org/
  3. 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: