NDLI: Genetic reinforcement learning for scheduling heterogeneous machines

Content Provider	IEEE Xplore Digital Library
Author	Kim, G.H. Lee, C.S.G.
Copyright Year	1996
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA (Kim, G.H.; Lee, C.S.G.)
Abstract	Concerns the development of a learning-based heuristic for scheduling heterogeneous machines. List scheduling methods are flexible enough to be used for a large class of problems, including the heterogeneous machine problem. However, designing a priority rule requires insight into the characteristics of the problem. We propose the iterative list scheduling, which refines priority rules while generating a number of schedules. We also show that the iterative list scheduling can be formulated as a reinforcement learning problem, defining states and actions. Due to the large number of possible states, reinforcement learning algorithms which use value functions in constructing an optimal policy may not be suitable for scheduling problems. Encoding the policies of reinforcement learning into genetic algorithms leads to the genetic reinforcement learning (GRL), which directly works with the policies rather than the values of states. A GRL-based scheduler, EVIS (Evolutionary Intracell Scheduler), has been applied to problems such as the heterogeneous machine scheduling, the job-shop scheduling, the flow-shop scheduling, and the open-shop scheduling problems. The proposed model of EVIS, which has the linear order of population-fitness convergence, was verified with computer experiments. Even without fine tuning of EVIS, the quality of solutions found by EVIS was comparable to that of problem-tailored heuristics for most of the problem instances.
Starting Page	2798
Ending Page	2803
File Size	652150
Page Count	6
File Format	PDF
ISBN	0780329880
ISSN	10504729
DOI	10.1109/ROBOT.1996.506586
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-04-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Job shop scheduling Processor scheduling Genetic algorithms Iterative algorithms Space exploration Genetic engineering Scheduling algorithm Encoding Biological cells
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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