A review of hyper-heuristics for educational timetabling

被引:53
|
作者
Pillay, Nelishia [1 ]
机构
[1] Univ KwaZulu Natal, Sch Math Stat & Comp Sci, Pietermaritzburg, South Africa
关键词
Hyper-heuristics; Educational timetabling; University examination timetabling; University course timetabling; School timetabling; SELECTION; FRAMEWORK;
D O I
10.1007/s10479-014-1688-1
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
Educational timetabling problems, namely, university examination timetabling, university course timetabling and school timetabling, are combinatorial optimization problems requiring the allocation of resources so as to satisfy a specified set of constraints. Hyper-heuristics have been successfully applied to a variety of combinatorial optimization problems. This is a rapidly growing field which aims at providing generalized solutions to combinatorial optimization problems by exploring a heuristic space instead of a solution space. From the research conducted thus far it is evident that hyper-heuristics are effective at solving educational timetabling problems and have the potential of advancing this field by providing a generalized solution to educational timetabling as a whole. Given this, the paper provides an overview and critical analysis of hyper-heuristics for educational timetabling and proposes future research directions, focusing on using hyper-heuristics to provide a generalized solution to educational timetabling.
引用
收藏
页码:3 / 38
页数:36
相关论文
共 50 条
  • [21] Generalizing Hyper-heuristics via Apprenticeship Learnin
    Asta, Shahriar
    Oezcan, Ender
    Parkes, Andrew J.
    Etaner-Uyar, A. Sima
    EVOLUTIONARY COMPUTATION IN COMBINATORIAL OPTIMIZATION (EVOCOP 2013), 2013, 7832 : 169 - +
  • [22] Unified encoding for hyper-heuristics with application to bioinformatics
    Aleksandra Swiercz
    Edmund K. Burke
    Mateusz Cichenski
    Grzegorz Pawlak
    Sanja Petrovic
    Tomasz Zurkowski
    Jacek Blazewicz
    Central European Journal of Operations Research, 2014, 22 : 567 - 589
  • [23] Hyper-Heuristics with Low Level Parameter Adaptation
    Ren, Zhilei
    Jiang, He
    Xuan, Jifeng
    Luo, Zhongxuan
    EVOLUTIONARY COMPUTATION, 2012, 20 (02) : 189 - 227
  • [24] Unified encoding for hyper-heuristics with application to bioinformatics
    Swiercz, Aleksandra
    Burke, Edmund K.
    Cichenski, Mateusz
    Pawlak, Grzegorz
    Petrovic, Sanja
    Zurkowski, Tomasz
    Blazewicz, Jacek
    CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH, 2014, 22 (03) : 567 - 589
  • [25] An Application of Hyper-Heuristics to Flexible Manufacturing Systems
    Linard, Alexis
    van Pinxten, Joost
    2019 22ND EUROMICRO CONFERENCE ON DIGITAL SYSTEM DESIGN (DSD), 2019, : 343 - 350
  • [26] Hyper-heuristics for cross-domain search
    Cichowicz, T.
    Drozdowski, M.
    Frankiewicz, M.
    Pawlak, G.
    Rytwinski, F.
    Wasilewski, J.
    BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES, 2012, 60 (04) : 801 - 808
  • [27] Parallel hyper-heuristics for process engineering optimization
    Oteiza, Paola P.
    Ardenghi, Juan, I
    Brignole, Nelida B.
    COMPUTERS & CHEMICAL ENGINEERING, 2021, 153
  • [28] On the investigation of hyper-heuristics on a financial forecasting problem
    Michael Kampouridis
    Abdullah Alsheddy
    Edward Tsang
    Annals of Mathematics and Artificial Intelligence, 2013, 68 : 225 - 246
  • [29] Hyper-heuristics Reversed: Learning to Combine Solvers by Evolving Instances
    Amaya, Ivan
    Carlos Ortiz-Bayliss, Jose
    Conant-Pablos, Santiago
    Terashima-Marin, Hugo
    2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2019, : 1790 - 1797
  • [30] Automatic design for shop scheduling strategies based on hyper-heuristics: A systematic review
    Guo, Haoxin
    Liu, Jianhua
    Zhuang, Cunbo
    ADVANCED ENGINEERING INFORMATICS, 2022, 54