Theory

Contents:

• Shop Scheduling Problems
• Job Shop Scheduling
• Disjunctive Graph Formulation
• Solution Methods for the Job Shop Problem
  • Exact methods
  • Approximation Methods
  • Reinforcement Learning for Solving Scheduling Problems

References

[AC91]

David Applegate and William Cook. A computational study of the job-shop scheduling problem. ORSA Journal on computing, 3(2):149–156, 1991.

[BlazewiczEP+19]

Jacek Błażewicz, Klaus H Ecker, Erwin Pesch, Günter Schmidt, Małgorzata Sterna, and Jan Weglarz. Handbook on scheduling: From theory to practice. Springer, 2019.

[BlazewiczPS00]

Jacek Błażewicz, Erwin Pesch, and Małgorzata Sterna. The disjunctive graph machine representation of the job shop scheduling problem. European Journal of Operational Research, 127(2):317–331, 2000.

[DBK17]

N Duffie, J Bendul, and M Knollmann. An analytical approach to improving due-date and lead-time dynamics in production systems. Journal of Manufacturing Systems, 45:273–285, 2017.

[Gab09]

Thomas Gabel. Multi-agent reinforcement learning approaches for distributed job shop scheduling problems. PhD thesis, Osnabrück, Univ., Diss., 2009, 2009.

[Hen02]

André Henning. Praktische job-shop scheduling-probleme. PhD thesis, Friedrich-Schiller-Universität Jena, 2002.

[JP14]

Florian Jaehn and Erwin Pesch. Ablaufplanung. Springer, 2014.

[Lan19]

Julia Lange. Solution techniques for the blocking job shop scheduling problem with total tardiness minimization. PhD thesis, Doctoral dissertation, 2019.

[Man60]

Alan S Manne. On the job-shop scheduling problem. Operations research, 8(2):219–223, 1960.

[Pin05]

Michael Pinedo. Planning and scheduling in manufacturing and services. Springer, 2005.

[RS64]

Bernard Roy and B Sussmann. Les problemes d’ordonnancement avec contraintes disjonctives. Note ds, 1964.

[SPSF19]

Günther Schuh, Jan-Philipp Prote, Frederick Sauermann, and Bastian Franzkoch. Databased prediction of order-specific transition times. CIRP Annals, 68(1):467–470, 2019.

[Tal09]

El-Ghazali Talbi. Metaheuristics: from design to implementation. John Wiley & Sons, 2009.

[WdPMM22]

Constantin Waubert de Puiseau, Richard Meyes, and Tobias Meisen. On reliability of reinforcement learning based production scheduling systems: a comparative survey. Journal of Intelligent Manufacturing, 33(4):911–927, 2022.

[ZSC+20]

Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, and Xu Chi. Learning to dispatch for job shop scheduling via deep reinforcement learning. Advances in Neural Information Processing Systems, 33:1621–1632, 2020.