The aim of the course is to provide master students with a sound understanding of advanced biologically inspired artificial intelligence techniques for effective and efficient optimal scheduling of systems and processes. This course gives an introduction to theoretical and practical knowledge and skills used to solve a broad range of scheduling problems that arise in intelligent manufacturing systems within the Industry 4.0 framework as well as to the software packages and programming skills needed to deal with these problems. The course will focus on production planning and scheduling, manufacturing resources planning and scheduling, job shop scheduling, transportation scheduling, and mobile robot scheduling. The course covers advanced evolutionary and swarm intelligence-based approaches for dealing with optimal scheduling of systems and processes.

After successfully completing this course, the students should be able to: - use information and communication technologies in the scheduling of systems and processes; - formulate and mathematically model the scheduling problems; - implement advanced biologically inspired algorithms, approaches, and strategies to optimize scheduling plans, with the objective to minimize/maximize the fitness function according to optimization criteria; - develop their own software solutions in MATLAB environment; - experimentally evaluate the performance of the algorithms, as well as discus and present achieved results; - work in a team.

Introduction to the scheduling of systems and processes. Definitions, analysis, and classification of scheduling problems. Job-shop scheduling problem. Flow shop scheduling problem. Just-in-Time and Lean manufacturing concepts. Flexible process planning. Fundaments of flexible process plans. Flexibility types and representation of flexible process plans. Mathematical modeling and optimization objectives (production time, production cost, etc.). Scheduling of manufacturing entities. Scheduling of manufacturing systems – planning and scheduling of manufacturing resources. Scheduling of internal material transport systems and flows – scheduling of mobile robots. Mathematical models and optimization objectives (makespan, resource utilization, flow time, cost, tardiness, lateness, etc.). Dynamic scheduling of systems and processes. Rescheduling strategies and methods. Integrated flexible process planning and scheduling. Models for integrated process planning and scheduling – formulation, representation, and mathematical background of the problem. Optimization algorithms. NP-hard problems. Combinatorial optimization problems. Single-objective and multi-objective optimization. Review of metaheuristic optimization algorithms. Biologically inspired optimization algorithms for scheduling of systems and processes. Swarm and evolutionary intelligence. Advanced swarm intelligence algorithms (PSO, ALO, GWO, WOA, etc.). Evolutionary intelligence - introduction to genetic algorithms and genetic programming. Hybrid algorithms in the scheduling of systems and processes. Real-world implementation examples. Examples of system designs and implementations.

Design and implementation of scheduling systems: basic and more advanced concepts (real-world implementation examples). Scheduling of systems and processes - modeling and analysis (laboratory work, case study). A graphical description and representation of the scheduling problem - methods for coding/decoding of scheduling plans (laboratory work, case study). Advanced swarm and evolutionary intelligence-based algorithms for scheduling of systems and processes (laboratory work, programming in MATLAB software package). Software solutions for optimal scheduling (laboratory work, applications in MATLAB software package). The project design (planning and scheduling of manufacturing systems and processes; scheduling of manufacturing tasks and resources; order sequencing; scheduling of intelligent mobile robots).

BSc in Mechanical Engineering or relevant engineering discipline.

Laboratory for industrial robotics and artificial intelligence (ROBOTICS&AI), Department of Production Engineering, University of Belgrade - Faculty of Mechanical Engineering. MATLAB software.

Total assigned hours: 90

New material: 20
Elaboration and examples (recapitulation): 10

Auditory exercises: 10
Laboratory exercises: 15
Calculation tasks: 0
Seminar paper: 0
Project: 20
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 0
Review and grading of lab reports: 0
Review and grading of seminar papers: 0
Review and grading of the project: 5
Test: 0
Test: 5
Final exam: 5

Activity during lectures: 0
Test/test: 40
Laboratory practice: 0
Calculation tasks: 0
Seminar paper: 0
Project: 30
Final exam: 30
Requirement for taking the exam (required number of points): 30

Pinedo, M., & Chao, X. (1999). Operations scheduling with applications in manufacturing and services. McGraw Hill. ISBN 0‐07‐289779‐1; Conway, R. W., Maxwell, W. L., & Miller, L. W. (2003). Theory of scheduling. Courier Corporation. ISBN 9780486428178; Leung, J. Y. (Ed.). (2004). Handbook of scheduling: algorithms, models, and performance analysis. CRC press. ISBN 1-58488-397-9; Pinedo, M. (2012). Scheduling: Theory, Algorithms, and Systems. New York: Springer. ISBN 978-1-4614-1986-0; Kerzner, H., & Kerzner, H. R. (2017). Project management: a systems approach to planning, scheduling, and controlling. John Wiley & Sons. ISBN 9781394290031