Additional Info

• ECTS credits: 6
• University: University of L'Aquila
• Semester: 3
• Lecturer 1: Stefano Smriglio
• Objectives:

   

  Train the students in recognizing machine scheduling problems, classify them in terms of computational complexity and solve them by heuristic, approximation or exact algorithms.

  LEARNING OUTCOMES
  On successful completion of this course, the student should:

  • Acquire knowledge of Machine Scheduling problems, their classification in terms of computational complexity and algorithmic techniques developed for their solution. Acquire the fundamentals of optimization methods for project management.
  • Acquire the ability to recognize Machine Scheduling problems in different application contexts, such as computer science, industrial engineering and management, and to identify effective solution paradigms.
  • Acquire autonomy in modeling and algorithmic choices for complex problems related to scheduling and project management.
  • Being able to hold a conversation and to read texts on topics related to the modeling of scheduling problems and the evaluation of algorithms for their solution
  • Acquire skills upgrading flexible knowledge and skills in the field of scheduling problems that arise in various areas, such as computer science, industrial engineering and management
• Topics:

   

   

  Elements of a (deterministic) scheduling problem, examples of practical applications
  Classification of scheduling problems
  Integer Linear Programming formulations
  Single machine scheduling: computational complexity, heuristic and exact algoritms
  Parallel machine scheduling: exact, heuristic and approximation algorithms
  Relationships with basic Combinatorial Optimization problems
  Optimization problems in Project Scheduling
  Job Shop scheduling: formulations, heuristic and exact algorithms

• Prerequisites:

   

  Basic elements of computational complexity, linear programming and network flows

• Books:

   

  Michael Pinedo, Scheduling Theory, Algorithms, and Systems. Prentice Hall

• Teaching material 1: http://ns.di.univaq.it/~oil/didattica/corsi/so/somain_dx.htm