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Algorithms
Special Issues
Novel Evolutionary Computation Approaches to Scheduling and Timetabling
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Novel Evolutionary Computation Approaches to Scheduling and Timetabling

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (1 May 2013) | Viewed by 29889

Special Issue Editors

Prof. Dr. Wolfgang Banzhaf

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Guest Editor
Department of Computer Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada
Dr. Nelishia Pillay

E-Mail Website
Guest Editor
Room F3, 1st Floor Science Block, School of Computer Science, Pietermaritzburg Campus, University of KwaZulu-Natal, South Africa

Special Issue Information

Dear Colleagues,

Scheduling and Timetabling problems are complex combinatorial optimization problems of enormous practical and economic importance. This issue focuses on the application of novel evolutionary computation methods to solve scheduling and timetabling problems. Evolutionary Computation, a machine learning technique, is based on Darwin's theory of evolution and as such iteratively refines an initial population of potential solutions using the processes of evaluation, selection and regeneration in an attempt to find solutions to (complex) optimization problems. The use of Evolutionary Computation techniques to solve scheduling and timetabling problems is a growing field and EC heuristics have been successfully applied to various subdomains including educational timetabling, transportation scheduling, personnel scheduling, job shop scheduling and sports scheduling amongst others.

Given the high processing times typical of evolutionary computing techniques in solving these problems, parallel EC methods and implementations are a recent trend of great importance. Authors are invited to submit manuscripts reporting original research on the application of EC to scheduling and timetabling problems and its efficient implementation.

Professor Wolfgang Banzhaf
Dr. Nelishia Pillay
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • evolutionary computation
  • genetic algorithms
  • genetic programming
  • parallel implementation of EC methods
  • university course timetabling
  • examination timetabling
  • school timetabling
  • transportation scheduling, e.g. railway scheduling, aircraft landing
  • medical scheduling, including nurse, doctor and patient scheduling
  • job shop scheduling
  • personnel scheduling
  • sports scheduling

Published Papers (3 papers)

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Research

495 KiB  
Article
A Generic Two-Phase Stochastic Variable Neighborhood Approach for Effectively Solving the Nurse Rostering Problem
by Ioannis P. Solos, Ioannis X. Tassopoulos and Grigorios N. Beligiannis
Algorithms 2013, 6(2), 278-308; https://doi.org/10.3390/a6020278 - 21 May 2013
Cited by 19 | Viewed by 9343
Abstract
In this contribution, a generic two-phase stochastic variable neighborhood approach is applied to nurse rostering problems. The proposed algorithm is used for creating feasible and efficient nurse rosters for many different nurse rostering cases. In order to demonstrate the efficiency and generic applicability [...] Read more.
In this contribution, a generic two-phase stochastic variable neighborhood approach is applied to nurse rostering problems. The proposed algorithm is used for creating feasible and efficient nurse rosters for many different nurse rostering cases. In order to demonstrate the efficiency and generic applicability of the proposed approach, experiments with real-world input data coming from many different nurse rostering cases have been conducted. The nurse rostering instances used have significant differences in nature, structure, philosophy and the type of hard and soft constraints. Computational results show that the proposed algorithm performs better than six different existing approaches applied to the same nurse rostering input instances using the same evaluation criteria. In addition, in all cases, it manages to reach the best-known fitness achieved in the literature, and in one case, it manages to beat the best-known fitness achieved till now. Full article
(This article belongs to the Special Issue Novel Evolutionary Computation Approaches to Scheduling and Timetabling)
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1247 KiB  
Article
Fast Rescheduling of Multiple Workflows to Constrained Heterogeneous Resources Using Multi-Criteria Memetic Computing
by Wilfried Jakob, Sylvia Strack, Alexander Quinte, Günther Bengel, Karl-Uwe Stucky and Wolfgang Süß
Algorithms 2013, 6(2), 245-277; https://doi.org/10.3390/a6020245 - 22 Apr 2013
Cited by 10 | Viewed by 8225
Abstract
This paper is motivated by, but not limited to, the task of scheduling jobs organized in workflows to a computational grid. Due to the dynamic nature of grid computing, more or less permanent replanning is required so that only very limited time is [...] Read more.
This paper is motivated by, but not limited to, the task of scheduling jobs organized in workflows to a computational grid. Due to the dynamic nature of grid computing, more or less permanent replanning is required so that only very limited time is available to come up with a revised plan. To meet the requirements of both users and resource owners, a multi-objective optimization comprising execution time and costs is needed. This paper summarizes our work over the last six years in this field, and reports new results obtained by the combination of heuristics and evolutionary search in an adaptive Memetic Algorithm. We will show how different heuristics contribute to solving varying replanning scenarios and investigate the question of the maximum manageable work load for a grid of growing size starting with a load of 200 jobs and 20 resources up to 7000 jobs and 700 resources. Furthermore, the effect of four different local searchers incorporated into the evolutionary search is studied. We will also report briefly on approaches that failed within the short time frame given for planning. Full article
(This article belongs to the Special Issue Novel Evolutionary Computation Approaches to Scheduling and Timetabling)
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387 KiB  
Article
Solving University Course Timetabling Problems Using Constriction Particle Swarm Optimization with Local Search
by Ruey-Maw Chen and Hsiao-Fang Shih
Algorithms 2013, 6(2), 227-244; https://doi.org/10.3390/a6020227 - 19 Apr 2013
Cited by 47 | Viewed by 11803
Abstract
Course timetabling is a combinatorial optimization problem and has been confirmed to be an NP-complete problem. Course timetabling problems are different for different universities. The studied university course timetabling problem involves hard constraints such as classroom, class curriculum, and other variables. Concurrently, some [...] Read more.
Course timetabling is a combinatorial optimization problem and has been confirmed to be an NP-complete problem. Course timetabling problems are different for different universities. The studied university course timetabling problem involves hard constraints such as classroom, class curriculum, and other variables. Concurrently, some soft constraints need also to be considered, including teacher’s preferred time, favorite class time etc. These preferences correspond to satisfaction values obtained via questionnaires. Particle swarm optimization (PSO) is a promising scheme for solving NP-complete problems due to its fast convergence, fewer parameter settings and ability to fit dynamic environmental characteristics. Therefore, PSO was applied towards solving course timetabling problems in this work. To reduce the computational complexity, a timeslot was designated in a particle’s encoding as the scheduling unit. Two types of PSO, the inertia weight version and constriction version, were evaluated. Moreover, an interchange heuristic was utilized to explore the neighboring solution space to improve solution quality. Additionally, schedule conflicts are handled after a solution has been generated. Experimental results demonstrate that the proposed scheme of constriction PSO with interchange heuristic is able to generate satisfactory course timetables that meet the requirements of teachers and classes according to the various applied constraints. Full article
(This article belongs to the Special Issue Novel Evolutionary Computation Approaches to Scheduling and Timetabling)
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