NDLI: A learning-based optimization approach to multi-project scheduling

Content Provider	Springer Nature Link
Author	Wauters, Tony Verbeeck, Katja Causmaecker, Patrick Berghe, Greet
Copyright Year	2014
Abstract	The present paper introduces a learning-based optimization approach to the resource-constrained multi-project scheduling problem. Multiple projects, each with their own set of activities, need to be scheduled. The objectives dealt with here include minimization of the average project delay and total makespan. The availability of local and global resources, precedence relations between activities, and non-equal project start times have to be considered. The proposed approach relies on a simple sequence learning game played by a group of project managers. The project managers each learn their activity list locally using reinforcement learning, more specifically learning automata. Meanwhile, they learn how to choose a suitable place in the overall sequence of all activity lists. All the projects need to arrive at a unique place in this sequence. A mediator, who usually has to solve a complex optimization problem, now manages a simple dispersion game. Through the mediator, a sequence of feasible activity lists is eventually scheduled by using a serial schedule generation scheme, which is adopted from single project scheduling. It is shown that the sequence learning approach has a large positive effect on minimizing the average project delay. In fact, the combination of local reinforcement learning, the sequence learning game and a forward-backward implementation of the serial scheduler significantly improves the best known results for all the MPSPLIB datasets. In addition, several new best results were obtained for both considered objectives: minimizing the average project delay and minimizing the total makespan.
Starting Page	61
Ending Page	74
Page Count	14
File Format	PDF
ISSN	10946136
Journal	Journal of Scheduling
Volume Number	18
Issue Number	1
e-ISSN	10991425
Language	English
Publisher	Springer US
Publisher Date	2014-11-04
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multi-project scheduling Learning automata Dispersion games Operations Research/Decision Theory Calculus of Variations and Optimal Control; Optimization Optimization Artificial Intelligence (incl. Robotics) Production/Logistics/Supply Chain
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Management Science and Operations Research Engineering Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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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
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