Coupling Genetic Local Search and Recovering Beam Search algorithms for minimizing the total completion time in the single machine scheduling problem subject to release dates

In this paper we consider the well-known single machine scheduling problem with release dates and minimization of the total job completion time. For solving this problem, denoted by 1 | r j | ∑ C j , we provide a new metaheuristic which is an extension of the so-called filtered beam search proposed...

Full description

Saved in:
Bibliographic Details
Published in:Computers & operations research Vol. 39; no. 6; pp. 1257 - 1264
Main Authors: Rakrouki, Mohamed Ali, Ladhari, Talel, T’kindt, Vincent
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.06.2012
Elsevier
Pergamon Press Inc
Subjects:
Algorithms
Comparative analysis
Completion time
Computer Science
Genetic Local Search
Genetic Recovering Beam Search
Genetics
Heuristic
Heuristics
Job shops
Joining
Operations Research
Optimization
Optimization algorithms
Production scheduling
Recovering
Release dates
Scheduling
Searching
Single machine
Studies
Genetic Local Search
Genetic Recovering Beam Search
Release dates
Heuristics
Single machine
ISSN:0305-0548, 1873-765X, 0305-0548
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper we consider the well-known single machine scheduling problem with release dates and minimization of the total job completion time. For solving this problem, denoted by 1 | r j | ∑ C j , we provide a new metaheuristic which is an extension of the so-called filtered beam search proposed by Ow and Morton [30]. This metaheuristic, referred to as a Genetic Recovering Beam Search (GRBS), takes advantages of a Genetic Local Search (GLS) algorithm and a Recovering Beam Search (RBS) in order to efficiently explore the solution space. In this paper we present the GRBS framework and its application to the 1 | r j | ∑ C j problem. Computational results show that it consistently yields optimal or near-optimal solutions and that it provides interesting results by comparison to GLS and RBS algorithms. Moreover, these results highlight that the proposed algorithm outperforms the state-of-the-art heuristics.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2010.12.007