Robust scheduling for resource constraint scheduling problem by two-stage GA and MMEDA

Jing Tian; Tomohiro Murata

doi:10.1109/IIAI-AAI.2016.99

Robust scheduling for resource constraint scheduling problem by two-stage GA and MMEDA

Jing Tian, Tomohiro Murata

Graduate School of Information, Production and Systems

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

3 Citations (Scopus)

Abstract

Inspired by the cooperative co-evolutionary paradigm, this paper presents a two-stage algorithm hybrid generic algorithm (GA) and multi-objective Markov network based EDA (MMEDA), to solve the robust scheduling problem for resource constrained scheduling problem (RCSP) with uncertainty. In the first stage, GA is used to find feasible solution for sequencing sub-problem, and in the second stage, MMEDA is adopted to model the interrelation for resource allocation and calculate the Pareto set for multi-objective optimization problems. One problem-specific local search with considering both makespan and robustness is designed to increase the solution quality. Experiment results based on a benchmark and comparisons demonstrate that our approach is highly effective and tolerant of uncertainty.

Original language	English
Title of host publication	Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1042-1047
Number of pages	6
ISBN (Electronic)	9781467389853
DOIs	https://doi.org/10.1109/IIAI-AAI.2016.99
Publication status	Published - 2016 Aug 31
Event	5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan Duration: 2016 Jul 10 → 2016 Jul 14

Other

Other	5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
Country/Territory	Japan
City	Kumamoto
Period	16/7/10 → 16/7/14

Keywords

Estimation Distribution of Algorithm
Markov Network
Multi-objective
Resource Constrained Scheduling Problem
Robust Scheduling

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition

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10.1109/IIAI-AAI.2016.99

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Robust scheduling for resource constraint scheduling problem by two-stage GA and MMEDA. / Tian, Jing; Murata, Tomohiro.
Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1042-1047 7557767.

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

Tian, J & Murata, T 2016, Robust scheduling for resource constraint scheduling problem by two-stage GA and MMEDA. in Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016., 7557767, Institute of Electrical and Electronics Engineers Inc., pp. 1042-1047, 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Kumamoto, Japan, 16/7/10. https://doi.org/10.1109/IIAI-AAI.2016.99

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abstract = "Inspired by the cooperative co-evolutionary paradigm, this paper presents a two-stage algorithm hybrid generic algorithm (GA) and multi-objective Markov network based EDA (MMEDA), to solve the robust scheduling problem for resource constrained scheduling problem (RCSP) with uncertainty. In the first stage, GA is used to find feasible solution for sequencing sub-problem, and in the second stage, MMEDA is adopted to model the interrelation for resource allocation and calculate the Pareto set for multi-objective optimization problems. One problem-specific local search with considering both makespan and robustness is designed to increase the solution quality. Experiment results based on a benchmark and comparisons demonstrate that our approach is highly effective and tolerant of uncertainty.",

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AU - Tian, Jing

AU - Murata, Tomohiro

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N2 - Inspired by the cooperative co-evolutionary paradigm, this paper presents a two-stage algorithm hybrid generic algorithm (GA) and multi-objective Markov network based EDA (MMEDA), to solve the robust scheduling problem for resource constrained scheduling problem (RCSP) with uncertainty. In the first stage, GA is used to find feasible solution for sequencing sub-problem, and in the second stage, MMEDA is adopted to model the interrelation for resource allocation and calculate the Pareto set for multi-objective optimization problems. One problem-specific local search with considering both makespan and robustness is designed to increase the solution quality. Experiment results based on a benchmark and comparisons demonstrate that our approach is highly effective and tolerant of uncertainty.

AB - Inspired by the cooperative co-evolutionary paradigm, this paper presents a two-stage algorithm hybrid generic algorithm (GA) and multi-objective Markov network based EDA (MMEDA), to solve the robust scheduling problem for resource constrained scheduling problem (RCSP) with uncertainty. In the first stage, GA is used to find feasible solution for sequencing sub-problem, and in the second stage, MMEDA is adopted to model the interrelation for resource allocation and calculate the Pareto set for multi-objective optimization problems. One problem-specific local search with considering both makespan and robustness is designed to increase the solution quality. Experiment results based on a benchmark and comparisons demonstrate that our approach is highly effective and tolerant of uncertainty.

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KW - Multi-objective

KW - Resource Constrained Scheduling Problem

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ER -

Robust scheduling for resource constraint scheduling problem by two-stage GA and MMEDA

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Keywords

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