Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3520304.3533667acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
tutorial

Introduction to automated design of scheduling heuristics with genetic programming

Published: 19 July 2022 Publication History
First page of PDF

References

[1]
Mazhar Ansari Ardeh, Yi Mei, and Mengjie Zhang. Genetic programming with knowledge transfer and guided search for uncertain capacitated arc routing problem. IEEE Transactions on Evolutionary Computation, pages 1--1, 2021.
[2]
Jürgen Branke, Torsten Hildebrandt, and Bernd Scholz-Reiter. Hyper-heuristic evolution of dispatching rules: A comparison of rule representations. Evolutionary Computation, 23(2):249--277, June 2015.
[3]
Jurgen Branke, Su Nguyen, Christoph W. Pickardt, and Mengjie Zhang. Automated design of production scheduling heuristics: A review. IEEE Transactions on Evolutionary Computation, 20(1):110--124, February 2016.
[4]
G. Celano, A. Costa, and S. Fichera. Scheduling of unrelated parallel manufacturing cells with limited human resources. International Journal of Production Research, 46(2):405--427, 2008.
[5]
Shelvin Chand, Quang Huynh, Hemant Singh, Tapabrata Ray, and Markus Wagner. On the use of genetic programming to evolve priority rules for resource constrained project scheduling problems. Information Sciences, 432:146--163, March 2018.
[6]
Gabriel Duflo, Emmanuel Kieffer, Matthias R. Brust, Grégoire Danoy, and Pascal Bouvry. A gp hyper-heuristic approach for generating tsp heuristics. In 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pages 521--529, 2019.
[7]
A.T Ernst, H Jiang, M Krishnamoorthy, and D Sier. Staff scheduling and rostering: A review of applications, methods and models. European Journal of Operational Research, 153(1):3--27, 2004. Timetabling and Rostering.
[8]
Luis Fanjul-Peyro. Models and an exact method for the unrelated parallel machine scheduling problem with setups and resources. Expert Systems with Applications: X, 5:100022, 2020.
[9]
Francisco J. Gil-Gala, Carlos Mencía, María R. Sierra, and Ramiro Varela. Evolving priority rules for on-line scheduling of jobs on a single machine with variable capacity over time. Applied Soft Computing, 85:105782, December 2019.
[10]
Francisco J. Gil-Gala, Carlos Mencía, María R. Sierra, and Ramiro Varela. Learning ensembles of priority rules for online scheduling by hybrid evolutionary algorithms. Integrated Computer-Aided Engineering, 28(1):65--80, December 2020.
[11]
Francisco J. Gil-Gala, María R. Sierra, Carlos Mencía, and Ramiro Varela. Combining hyper-heuristics to evolve ensembles of priority rules for on-line scheduling. Natural Computing, June 2020.
[12]
Francisco J. Gil-Gala, María R. Sierra, Carlos Mencía, and Ramiro Varela. Genetic programming with local search to evolve priority rules for scheduling jobs on a machine with time-varying capacity. Swarm and Evolutionary Computation, 66:100944, October 2021.
[13]
Emma Hart, Peter Ross, and David Corne. Evolutionary scheduling: A review. Genetic Programming and Evolvable Machines, 6(2):191--220, June 2005.
[14]
Torsten Hildebrandt and Jürgen Branke. On using surrogates with genetic programming. Evolutionary Computation, 23(3):343--367, September 2015.
[15]
Torsten Hildebrandt, Jens Heger, and Bernd Scholz-Reiter. Towards improved dispatching rules for complex shop floor scenarios - a genetic programming approach. pages 257--264, 01 2010.
[16]
Rachel Hunt, Mark Johnston, and Mengjie Zhang. Evolving "less-myopic" scheduling rules for dynamic job shop scheduling with genetic programming. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, GECCO '14, page 927--934, New York, NY, USA, 2014. Association for Computing Machinery.
[17]
Josiah Jacobsen-Grocott, Yi Mei, Gang Chen, and Mengjie Zhang. Evolving heuristics for dynamic vehicle routing with time windows using genetic programming. In 2017 IEEE Congress on Evolutionary Computation (CEC), pages 1948--1955, 2017.
[18]
Kristijan Jaklinović, Marko Ðurasević, and Domagoj Jakobović. Designing dispatching rules with genetic programming for the unrelated machines environment with constraints. Expert Systems with Applications, 172:114548, June 2021.
[19]
Domagoj Jakobović and Kristina Marasović. Evolving priority scheduling heuristics with genetic programming. Applied Soft Computing, 12(9):2781--2789, September 2012.
[20]
Deepak Karunakaran, Yi Mei, Gang Chen, and Mengjie Zhang. Evolving dispatching rules for dynamic job shop scheduling with uncertain processing times. In 2017 IEEE Congress on Evolutionary Computation (CEC), pages 364--371, 2017.
[21]
Deepak Karunakaran, Yi Mei, Gang Chen, and Mengjie Zhang. Toward evolving dispatching rules for dynamic job shop scheduling under uncertainty. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '17, page 282--289, New York, NY, USA, 2017. Association for Computing Machinery.
[22]
Jae-Ho Lee, Jae-Min Yu, and Dong-Ho Lee. A tabu search algorithm for unrelated parallel machine scheduling with sequence- and machine-dependent setups: minimizing total tardiness. The International Journal of Advanced Manufacturing Technology, 69(9--12):2081--2089, July 2013.
[23]
Jan Karel Lenstra, David B. Shmoys, and Éva Tardos. Approximation algorithms for scheduling unrelated parallel machines. Mathematical Programming, 46(1--3):259--271, January 1990.
[24]
T.W. Liao, P.C. Chang, R.J. Kuo, and C.-J. Liao. A comparison of five hybrid metaheuristic algorithms for unrelated parallel-machine scheduling and inbound trucks sequencing in multi-door cross docking systems. Applied Soft Computing, 21:180--193, 2014.
[25]
Eunice López-Camacho, Hugo Terashima-Marin, Peter Ross, and Gabriela Ochoa. A unified hyper-heuristic framework for solving bin packing problems. Expert Systems with Applications, 41(15):6876--6889, 2014.
[26]
Yi Mei, Su Nguyen, Bing Xue, and Mengjie Zhang. An efficient feature selection algorithm for evolving job shop scheduling rules with genetic programming. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(5):339--353, October 2017.
[27]
Yi Mei, Su Nguyen, and Mengjie Zhang. Constrained dimensionally aware genetic programming for evolving interpretable dispatching rules in dynamic job shop scheduling. In Lecture Notes in Computer Science, pages 435--447. Springer International Publishing, 2017.
[28]
Su Nguyen. A learning and optimizing system for order acceptance and scheduling. The International Journal of Advanced Manufacturing Technology, 86(5--8):2021--2036, January 2016.
[29]
Su Nguyen, Yi Mei, Bing Xue, and Mengjie Zhang. A hybrid genetic programming algorithm for automated design of dispatching rules. Evolutionary Computation, 27:1--31, 06 2018.
[30]
Su Nguyen, Yi Mei, and Mengjie Zhang. Genetic programming for production scheduling: a survey with a unified framework. Complex & Intelligent Systems, 3(1):41--66, February 2017.
[31]
Su Nguyen, Mengjie Zhang, Mark Johnston, and Kay Chen Tan. A computational study of representations in genetic programming to evolve dispatching rules for the job shop scheduling problem. IEEE Transactions on Evolutionary Computation, 17(5):621--639, 2013.
[32]
Su Nguyen, Mengjie Zhang, Mark Johnston, and Kay Chen Tan. Dynamic multi-objective job shop scheduling: A genetic programming approach. In Studies in Computational Intelligence, pages 251--282. Springer Berlin Heidelberg, 2013.
[33]
Su Nguyen, Mengjie Zhang, Mark Johnston, and Kay Chen Tan. Learning iterative dispatching rules for job shop scheduling with genetic programming. The International Journal of Advanced Manufacturing Technology, 67(1--4):85--100, February 2013.
[34]
Su Nguyen, Mengjie Zhang, Mark Johnston, and Kay Chen Tan. Automatic design of scheduling policies for dynamic multi-objective job shop scheduling via cooperative coevolution genetic programming. IEEE Transactions on Evolutionary Computation, 18(2):193--208, April 2014.
[35]
Su Nguyen, Mengjie Zhang, Mark Johnston, and Kay Chen Tan. Genetic programming for evolving due-date assignment models in job shop environments. Evolutionary Computation, 22(1):105--138, March 2014.
[36]
Su Nguyen, Mengjie Zhang, and Kay Chen Tan. Enhancing genetic programming based hyper-heuristics for dynamic multi-objective job shop scheduling problems. In 2015 IEEE Congress on Evolutionary Computation (CEC), pages 2781--2788, 2015.
[37]
Su Nguyen, Mengjie Zhang, and Kay Chen Tan. Surrogate-assisted genetic programming with simplified models for automated design of dispatching rules. IEEE Transactions on Cybernetics, 47(9):2951--2965, September 2017.
[38]
John Park, Yi Mei, Su Nguyen, Gang Chen, and Mengjie Zhang. Investigating the generality of genetic programming based hyper-heuristic approach to dynamic job shop scheduling with machine breakdown. In Lecture Notes in Computer Science, pages 301--313. Springer International Publishing, December 2016.
[39]
John Park, Yi Mei, Su Nguyen, Gang Chen, and Mengjie Zhang. Investigating a machine breakdown genetic programming approach for dynamic job shop scheduling. In Lecture Notes in Computer Science, pages 253--270. Springer International Publishing, 2018.
[40]
John Park, Yi Mei, Su Nguyen, Gang Chen, and Mengjie Zhang. An investigation of ensemble combination schemes for genetic programming based hyper-heuristic approaches to dynamic job shop scheduling. Applied Soft Computing, 63:72--86, February 2018.
[41]
John Park, Su Nguyen, Mengjie Zhang, and Mark Johnston. Evolving ensembles of dispatching rules using genetic programming for job shop scheduling. In Lecture Notes in Computer Science, pages 92--104. Springer International Publishing, 2015.
[42]
Michael L. Pinedo. Scheduling. Springer US, 2012.
[43]
Erik Pitzer, Andreas Beham, Michael Affenzeller, Helga Heiss, and Markus Vorderwinkler. Production fine planning using a solution archive of priority rules. In 3rd IEEE International Symposium on Logistics and Industrial Informatics. IEEE, August 2011.
[44]
Lucija Planinic, Hrvoje Backovic, Marko Durasevic, and Domagoj Jakobovic. A comparative study of dispatching rule representations in evolutionary algorithms. IEEE Access, pages 1--1, 2022.
[45]
Lucija Planinic, Marko Durasevic, and Domagoj Jakobovic. Towards interpretable dispatching rules: Application of expression simplification methods. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, December 2021.
[46]
Lucija Planinić, Marko Ðurasević, and Domagoj Jakobović. On the application of ϵ-lexicase selection in the generation of dispatching rules. In 2021 IEEE Congress on Evolutionary Computation (CEC), pages 2125--2132, 2021.
[47]
Riccardo Poli, William B. Langdon, and Nicholas Freitag McPhee. A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, 2008. (With contributions by J. R. Koza).
[48]
Li-Ya Tseng, Yeh-Hao Chin, and Shu-Ching Wang. A minimized makespan scheduler with multiple factors for grid computing systems. Expert Systems with Applications, 36(8):11118--11130, 2009.
[49]
Lucija Ulaga, Marko Ðurasević, and Domagoj Jakobović. Local search based methods for scheduling in the unrelated parallel machines environment. Expert Systems with Applications, 199:116909, 2022.
[50]
Mateja Ðumić and Domagoj Jakobović. Ensembles of priority rules for resource constrained project scheduling problem. Applied Soft Computing, 110:107606, October 2021.
[51]
Mateja Ðumić, Dominik Šišejković, Rebeka Čorić, and Domagoj Jakobović. Evolving priority rules for resource constrained project scheduling problem with genetic programming. Future Generation Computer Systems, 86:211--221, September 2018.
[52]
Marko Ðurasević and Domagoj Jakobović. Comparison of ensemble learning methods for creating ensembles of dispatching rules for the unrelated machines environment. Genetic Programming and Evolvable Machines, 19(1--2):53--92, April 2017.
[53]
Marko Ðurasević and Domagoj Jakobović. A survey of dispatching rules for the dynamic unrelated machines environment. Expert Systems with Applications, 113:555--569, December 2018.
[54]
Marko Ðurasević and Domagoj Jakobović. Creating dispatching rules by simple ensemble combination. Journal of Heuristics, 25(6):959--1013, May 2019.
[55]
Marko Ðurasević and Domagoj Jakobović. Automatic design of dispatching rules for static scheduling conditions. Neural Computing and Applications, 33(10):5043--5068, August 2020.
[56]
Marko Ðurasević and Domagoj Jakobović. Comparison of schedule generation schemes for designing dispatching rules with genetic programming in the unrelated machines environment. Applied Soft Computing, 96:106637, November 2020.
[57]
Marko Ðurasević, Domagoj Jakobović, and Karlo Knežević. Adaptive scheduling on unrelated machines with genetic programming. Applied Soft Computing, 48:419--430, November 2016.
[58]
Marko Ðurasević and Domagoj Jakobović. Evolving dispatching rules for optimising many-objective criteria in the unrelated machines environment. Genetic Programming and Evolvable Machines, 19(1--2):9--51, September 2017.
[59]
Rebeka Čorić, Mateja Ðumić, and Domagoj Jakobović. Genetic programming hyperheuristic parameter configuration using fitness landscape analysis. Applied intelligence (Boston), 51 (10):7402--7426, 2021.
[60]
Ivan Vlašić, Marko Ðurasević, and Domagoj Jakobović. Improving genetic algorithm performance by population initialisation with dispatching rules. Computers & Industrial Engineering, 137:106030, November 2019.
[61]
Ivan Vlašić, Marko Ðurasević, and Domagoj Jakobović. A comparative study of solution representations for the unrelated machines environment. Computers & Operations Research, 123:105005, November 2020.
[62]
Daniel Yska, Yi Mei, and Mengjie Zhang. Feature construction in genetic programming hyper-heuristic for dynamic flexible job shop scheduling. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM, July 2018.
[63]
Fangfang Zhang, Yi Mei, Su Nguyen, Kay Chen Tan, and Mengjie Zhang. Multitask genetic programming-based generative hyperheuristics: A case study in dynamic scheduling. IEEE Transactions on Cybernetics, pages 1--14, 2021.
[64]
Fangfang Zhang, Yi Mei, Su Nguyen, and Mengjie Zhang. Guided subtree selection for genetic operators in genetic programming for dynamic flexible job shop scheduling. In Lecture Notes in Computer Science, pages 262--278. Springer International Publishing, 2020.
[65]
Fangfang Zhang, Yi Mei, Su Nguyen, and Mengjie Zhang. Collaborative multifidelity-based surrogate models for genetic programming in dynamic flexible job shop scheduling. IEEE Transactions on Cybernetics, pages 1--15, 2021.
[66]
Fangfang Zhang, Yi Mei, Su Nguyen, and Mengjie Zhang. Correlation coefficient-based recombinative guidance for genetic programming hyperheuristics in dynamic flexible job shop scheduling. IEEE Transactions on Evolutionary Computation, 25(3):552--566, 2021.
[67]
Fangfang Zhang, Yi Mei, Su Nguyen, and Mengjie Zhang. Evolving scheduling heuristics via genetic programming with feature selection in dynamic flexible job-shop scheduling. IEEE Transactions on Cybernetics, 51(4):1797--1811, April 2021.
[68]
Fangfang Zhang, Yi Mei, and Mengjie Zhang. Surrogate-assisted genetic programming for dynamic flexible job shop scheduling. In AI 2018: Advances in Artificial Intelligence, pages 766--772. Springer International Publishing, 2018.
[69]
Fangfang Zhang, Yi Mei, and Mengjie Zhang. A two-stage genetic programming hyper-heuristic approach with feature selection for dynamic flexible job shop scheduling. In Proceedings of the Genetic and Evolutionary Computation Conference. ACM, July 2019.
[70]
Fangfang Zhang, Su Nguyen, Yi Mei, and Mengjie Zhang. Genetic Programming for Production Scheduling. Springer Singapore, 2021.
[71]
Marko Ðurasević and Domagoj Jakobović. Selection of dispatching rules evolved by genetic programming in dynamic unrelated machines scheduling based on problem characteristics. Journal of Computational Science, 61:101649, 2022.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2022
2395 pages
ISBN:9781450392686
DOI:10.1145/3520304
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2022

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Tutorial

Funding Sources

Conference

GECCO '22
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 69
    Total Downloads
  • Downloads (Last 12 months)14
  • Downloads (Last 6 weeks)3
Reflects downloads up to 23 Dec 2024

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media