Reinforcement learning approaches for the parallel machines job shop scheduling problem

Yailen Martinez Jimenez; Wauters Tony; Ann Nowe; De Causmaecker Patrick; Katja Verbeeck

Reinforcement learning approaches for the parallel machines job shop scheduling problem

Yailen Martinez Jimenez, Wauters Tony, Ann Nowe, De Causmaecker Patrick, Katja Verbeeck

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

Abstract

This paper addresses the application of AI techniques in a practical OR problem, i.e. scheduling. Scheduling is a scientific domain concerning the allocation of tasks to a limited set of resources over time. The goal of scheduling is to maximize (or minimize) different optimization criteria such as the makespan (i.e. the completion time of the last operation in the schedule), the occupation rate of a machine or the total tardiness. In this area, the scientific community usually classifies the problems according to the characteristics of the systems studied. Important characteristics are: the number of machines available (one machine, parallel machines), the shop type (Job Shop, Open Shop or Flow Shop), the job characteristics (such as preemption allowed or not, equal processing times or not) and so on [1]. In this work we present two Reinforcement Learning approaches for the Parallel Machines Job Shop Scheduling Problem (JSP-PM).

Original language	English
Title of host publication	Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010
Number of pages	2
Publication status	Published - May 2010
Event	International conference on interdisciplinary research on technology, education and communication - , Belgium Duration: 23 May 2010 → 29 May 2010

Publication series

Name	Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010

Conference

Conference	International conference on interdisciplinary research on technology, education and communication
Abbreviated title	itec2010
Country/Territory	Belgium
Period	23/05/10 → 29/05/10

Keywords

Reinforcement Learning
Parallel Machines Job Shop Scheduling

Cite this

Martinez Jimenez, Y., Tony, W., Nowe, A., Patrick, D. C., & Verbeeck, K. (2010). Reinforcement learning approaches for the parallel machines job shop scheduling problem. In Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010 (Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010).

Martinez Jimenez, Yailen ; Tony, Wauters ; Nowe, Ann ; Patrick, De Causmaecker ; Verbeeck, Katja. / Reinforcement learning approaches for the parallel machines job shop scheduling problem. Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010. 2010. (Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010).

@inproceedings{8ecc6b3bdef245d09ef4ccc0d1016373,

title = "Reinforcement learning approaches for the parallel machines job shop scheduling problem",

abstract = "This paper addresses the application of AI techniques in a practical OR problem, i.e. scheduling. Scheduling is a scientific domain concerning the allocation of tasks to a limited set of resources over time. The goal of scheduling is to maximize (or minimize) different optimization criteria such as the makespan (i.e. the completion time of the last operation in the schedule), the occupation rate of a machine or the total tardiness. In this area, the scientific community usually classifies the problems according to the characteristics of the systems studied. Important characteristics are: the number of machines available (one machine, parallel machines), the shop type (Job Shop, Open Shop or Flow Shop), the job characteristics (such as preemption allowed or not, equal processing times or not) and so on [1]. In this work we present two Reinforcement Learning approaches for the Parallel Machines Job Shop Scheduling Problem (JSP-PM).",

keywords = "Reinforcement Learning, Parallel Machines Job Shop Scheduling",

author = "{Martinez Jimenez}, Yailen and Wauters Tony and Ann Nowe and Patrick, {De Causmaecker} and Katja Verbeeck",

year = "2010",

month = may,

language = "English",

series = "Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010",

booktitle = "Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010",

note = "International conference on interdisciplinary research on technology, education and communication, itec2010 ; Conference date: 23-05-2010 Through 29-05-2010",

}

Martinez Jimenez, Y, Tony, W, Nowe, A, Patrick, DC & Verbeeck, K 2010, Reinforcement learning approaches for the parallel machines job shop scheduling problem. in Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010. Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010, International conference on interdisciplinary research on technology, education and communication, Belgium, 23/05/10.

Reinforcement learning approaches for the parallel machines job shop scheduling problem. / Martinez Jimenez, Yailen; Tony, Wauters; Nowe, Ann; Patrick, De Causmaecker; Verbeeck, Katja.

Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010. 2010. (Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

TY - GEN

T1 - Reinforcement learning approaches for the parallel machines job shop scheduling problem

AU - Martinez Jimenez, Yailen

AU - Tony, Wauters

AU - Nowe, Ann

AU - Patrick, De Causmaecker

AU - Verbeeck, Katja

PY - 2010/5

Y1 - 2010/5

N2 - This paper addresses the application of AI techniques in a practical OR problem, i.e. scheduling. Scheduling is a scientific domain concerning the allocation of tasks to a limited set of resources over time. The goal of scheduling is to maximize (or minimize) different optimization criteria such as the makespan (i.e. the completion time of the last operation in the schedule), the occupation rate of a machine or the total tardiness. In this area, the scientific community usually classifies the problems according to the characteristics of the systems studied. Important characteristics are: the number of machines available (one machine, parallel machines), the shop type (Job Shop, Open Shop or Flow Shop), the job characteristics (such as preemption allowed or not, equal processing times or not) and so on [1]. In this work we present two Reinforcement Learning approaches for the Parallel Machines Job Shop Scheduling Problem (JSP-PM).

AB - This paper addresses the application of AI techniques in a practical OR problem, i.e. scheduling. Scheduling is a scientific domain concerning the allocation of tasks to a limited set of resources over time. The goal of scheduling is to maximize (or minimize) different optimization criteria such as the makespan (i.e. the completion time of the last operation in the schedule), the occupation rate of a machine or the total tardiness. In this area, the scientific community usually classifies the problems according to the characteristics of the systems studied. Important characteristics are: the number of machines available (one machine, parallel machines), the shop type (Job Shop, Open Shop or Flow Shop), the job characteristics (such as preemption allowed or not, equal processing times or not) and so on [1]. In this work we present two Reinforcement Learning approaches for the Parallel Machines Job Shop Scheduling Problem (JSP-PM).

KW - Reinforcement Learning

KW - Parallel Machines Job Shop Scheduling

M3 - Conference paper

T3 - Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010

BT - Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010

T2 - International conference on interdisciplinary research on technology, education and communication

Y2 - 23 May 2010 through 29 May 2010

ER -

Martinez Jimenez Y, Tony W, Nowe A, Patrick DC, Verbeeck K. Reinforcement learning approaches for the parallel machines job shop scheduling problem. In Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010. 2010. (Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010).

Reinforcement learning approaches for the parallel machines job shop scheduling problem

Abstract

Publication series

Conference

Keywords

Fingerprint

Cite this