ABSTRACT
Dynamic scheduling problems are important optimisation problems with many real-world applications. Since in dynamic scheduling not all information is available at the start, such problems are usually solved by dispatching rules (DRs), which create the schedule as the system executes. Recently, DRs have been successfully developed using genetic programming. However, a single DR may not efficiently solve different problem instances. Therefore, much research has focused on using DRs collaboratively by forming ensembles. In this paper, a novel ensemble collaboration method for dynamic scheduling is proposed. In this method, DRs are applied independently at each decision point to create a simulation of the schedule for all currently released jobs. Based on these simulations, it is determined which DR makes the best decision and that decision is applied. The results show that the ensembles easily outperform individual DRs for different ensemble sizes. Moreover, the results suggest that it is relatively easy to create good ensembles from a set of independently evolved DRs.
- Jürgen Branke, Su Nguyen, Christoph W. Pickardt, and Mengjie Zhang. 2016. Automated Design of Production Scheduling Heuristics: A Review. IEEE Transactions on Evolutionary Computation 20, 1 (2016), 110--124. Google ScholarDigital Library
- C. Dimopoulos and A.M.S. Zalzala. 2001. Investigating the use of genetic programming for a classic one-machine scheduling problem. Advances in Engineering Software 32, 6 (2001), 489--498. Google ScholarCross Ref
- Francisco J. Gil-Gala, Carlos Mencía, María R. Sierra, and Ramiro Varela. 2020. Learning ensembles of priority rules for online scheduling by hybrid evolutionary algorithms. Integrated Computer-Aided Engineering 28, 1 (Dec. 2020), 65--80. Google ScholarCross Ref
- Francisco J. Gil-Gala, María R. Sierra, Carlos Mencía, and Ramiro Varela. 2020. Combining hyper-heuristics to evolve ensembles of priority rules for on-line scheduling. Natural Computing (June 2020). Google ScholarDigital Library
- Francisco J. Gil-Gala, María R. Sierra, Carlos Mencía, and Ramiro Varela. 2021. Genetic programming with local search to evolve priority rules for scheduling jobs on a machine with time-varying capacity. Swarm and Evolutionary Computation 66 (2021), 100944. Google ScholarCross Ref
- Francisco J. Gil-Gala and Ramiro Varela. 2019. Genetic Algorithm to Evolve Ensembles of Rules for On-Line Scheduling on Single Machine with Variable Capacity. In From Bioinspired Systems and Biomedical Applications to Machine Learning. Springer International Publishing, 223--233. Google ScholarCross Ref
- Emma Hart and Kevin Sim. 2016. A Hyper-Heuristic Ensemble Method for Static Job-Shop Scheduling. Evolutionary Computation 24, 4 (2016), 609--635. Google ScholarDigital Library
- Kristijan Jaklinović, Marko Đurasević, and Domagoj Jakobović. 2021. Designing dispatching rules with genetic programming for the unrelated machines environment with constraints. Expert Systems with Applications 172 (2021), 114548. Google ScholarCross Ref
- Domagoj Jakobovic and Leo Budin. 2006. Dynamic Scheduling with Genetic Programming. 73--84. Google ScholarDigital Library
- Atiya Masood, Gang Chen, Yi Mei, Harith Al-Sahaf, and Mengjie Zhang. 2020. A Fitness-based Selection Method for Pareto Local Search for Many-Objective Job Shop Scheduling. In 2020 IEEE Congress on Evolutionary Computation (CEC). 1--8. Google ScholarDigital Library
- Kazuo Miyashita. 2000. Job-Shop Scheduling with Genetic Programming. In Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation (Las Vegas, Nevada) (GECCO'00). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 505--512.Google ScholarDigital Library
- Su Nguyen, Yi Mei, Bing Xue, and Mengjie Zhang. 2019. A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules. Evolutionary Computation 27, 3 (09 2019), 467--496. Google ScholarDigital Library
- Su Nguyen, Yi Mei, and Mengjie Zhang. 2017. Genetic programming for production scheduling: a survey with a unified framework. Complex & Intelligent Systems 3, 1 (Feb. 2017), 41--66. Google ScholarCross Ref
- Su Nguyen, Mengjie Zhang, Mark Johnston, and Kay Chen Tan. 2013. Dynamic Multi-objective Job Shop Scheduling: A Genetic Programming Approach. In Studies in Computational Intelligence. Springer Berlin Heidelberg, 251--282. Google ScholarCross Ref
- Su Nguyen, Mengjie Zhang, and Kay Chen Tan. 2015. Enhancing genetic programming based hyper-heuristics for dynamic multi-objective job shop scheduling problems. In 2015 IEEE Congress on Evolutionary Computation (CEC). 2781--2788. Google ScholarCross Ref
- Su Nguyen, Mengjie Zhang, and Kay Chen Tan. 2017. Surrogate-Assisted Genetic Programming With Simplified Models for Automated Design of Dispatching Rules. IEEE Transactions on Cybernetics 47, 9 (2017), 2951--2965. Google ScholarCross Ref
- John Park, Yi Mei, Su Nguyen, Gang Chen, Mark Johnston, and Mengjie Zhang. 2016. Genetic Programming Based Hyper-heuristics for Dynamic Job Shop Scheduling: Cooperative Coevolutionary Approaches. In Lecture Notes in Computer Science. Springer International Publishing, 115--132. Google ScholarCross Ref
- John Park, Yi Mei, Su Nguyen, Gang Chen, and Mengjie Zhang. 2017. An Investigation of Ensemble Combination Schemes for Genetic Programming based Hyper-heuristic Approaches to Dynamic Job Shop Scheduling. Applied Soft Computing 63 (11 2017). Google ScholarDigital Library
- John Park, Yi Mei, Su Nguyen, Gang Chen, and Mengjie Zhang. 2018. Investigating a Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling. In Lecture Notes in Computer Science. Springer International Publishing, 253--270. Google ScholarCross Ref
- John Park, Su Nguyen, Mengjie Zhang, and Mark Johnston. 2015. Evolving Ensembles of Dispatching Rules Using Genetic Programming for Job Shop Scheduling. 92--104. Google ScholarCross Ref
- Michael L. Pinedo. 2012. Scheduling. Springer US. Google ScholarCross Ref
- Lucija Planinić, Marko Đurasević, and Domagoj Jakobović. 2021. On the Application of ∊-Lexicase Selection in the Generation of Dispatching Rules. In 2021 IEEE Congress on Evolutionary Computation (CEC). 2125--2132. Google ScholarDigital Library
- Riccardo Poli, William B. Langdon, and Nicholas Freitag McPhee. 2008. A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd.Google Scholar
- Mateja Đumić and Domagoj Jakobović. 2021. Ensembles of priority rules for resource constrained project scheduling problem. Applied Soft Computing 110 (2021), 107606. Google ScholarDigital Library
- Marko Đurasević and Domagoj Jakobović. 2020. Automatic design of dispatching rules for static scheduling conditions. Neural Computing and Applications 33, 10 (Aug. 2020), 5043--5068. Google ScholarDigital Library
- Marko Đurasević and Domagoj Jakobović. 2017. Comparison of ensemble learning methods for creating ensembles of dispatching rules for the unrelated machines environment. Genetic Programming and Evolvable Machines 19, 1-2 (April 2017), 53--92. Google ScholarDigital Library
- Marko Đurasević and Domagoj Jakobović. 2017. Evolving dispatching rules for optimising many-objective criteria in the unrelated machines environment. Genetic Programming and Evolvable Machines 19, 1-2 (Sept. 2017), 9--51. Google ScholarDigital Library
- Marko Đurasević and Domagoj Jakobović. 2019. Creating dispatching rules by simple ensemble combination. Journal of Heuristics 25, 6 (May 2019), 959--1013. Google ScholarDigital Library
- Marko Đurasević, Domagoj Jakobović, and Karlo Knežević. 2016. Adaptive scheduling on unrelated machines with genetic programming. Applied Soft Computing 48 (2016), 419--430. Google ScholarDigital Library
- Ivan Vlašić, Marko Đurasević, and Domagoj Jakobović. 2019. Improving genetic algorithm performance by population initialisation with dispatching rules. Computers & Industrial Engineering 137 (2019), 106030. Google ScholarCross Ref
- D.H. Wolpert and W.G. Macready. 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 1 (1997), 67--82. Google ScholarDigital Library
- Fangfang Zhang, Yi Mei, Su Nguyen, Kay Chen Tan, and Mengjie Zhang. 2021. Multitask Genetic Programming-Based Generative Hyperheuristics: A Case Study in Dynamic Scheduling. IEEE Transactions on Cybernetics (2021), 1--14. Google ScholarCross Ref
- Fangfang Zhang, Yi Mei, Su Nguyen, and Mengjie Zhang. 2020. Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling. In Lecture Notes in Computer Science. Springer International Publishing, 262--278. Google ScholarDigital Library
- Fangfang Zhang, Yi Mei, Su Nguyen, and Mengjie Zhang. 2021. Collaborative Multifidelity-Based Surrogate Models for Genetic Programming in Dynamic Flexible Job Shop Scheduling. IEEE Transactions on Cybernetics (2021), 1--15. Google ScholarCross Ref
- Fangfang Zhang, Yi Mei, Su Nguyen, and Mengjie Zhang. 2021. Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling. IEEE Transactions on Cybernetics 51, 4 (2021), 1797--1811. Google ScholarCross Ref
- Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang, and Kay Chen Tan. 2021. Surrogate-Assisted Evolutionary Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling. IEEE Transactions on Evolutionary Computation 25, 4 (2021), 651--665. Google ScholarDigital Library
- Fangfang Zhang, Yi Mei, and Mengjie Zhang. 2019. Evolving Dispatching Rules for Multi-objective Dynamic Flexible Job Shop Scheduling via Genetic Programming Hyper-heuristics. In 2019 IEEE Congress on Evolutionary Computation (CEC). 1366--1373. Google ScholarDigital Library
- Fangfang Zhang, Yi Mei, and Mengjie Zhang. 2019. 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 (Prague, Czech Republic) (GECCO '19). Association for Computing Machinery, New York, NY, USA, 347--355. Google ScholarDigital Library
- Marko Đurasević and Domagoj Jakobović. 2018. A survey of dispatching rules for the dynamic unrelated machines environment. Expert Systems with Applications 113 (2018), 555--569. Google ScholarDigital Library
- Marko Đurasević and Domagoj Jakobović. 2020. Comparison of schedule generation schemes for designing dispatching rules with genetic programming in the unrelated machines environment. Applied Soft Computing 96 (2020), 106637. Google ScholarDigital Library
Index Terms
Novel ensemble collaboration method for dynamic scheduling problems
Recommendations
Collaboration methods for ensembles of dispatching rules for the dynamic unrelated machines environment
AbstractDynamic scheduling represents an important combinatorial optimisation problem that often appears in the real world. The difficulty in solving these problems arises from their dynamic nature, which limits the applicability of improvement based ...
Constructing Ensembles of Dispatching Rules for Multi-objective Problems
Bio-inspired Systems and Applications: from Robotics to Ambient IntelligenceAbstractScheduling represents an important aspect of many real-world processes, which is why such problems have been well studied in the literature. Such problems are often dynamic and require that multiple criteria be optimised simultaneously. ...
Scheduling rules for dynamic shops that manufacture multi-level jobs
The problem of scheduling in dynamic conventional jobshops has been extensively investigated over many years. However, the problem of scheduling in assembly jobshops (i.e. shops that manufacture multi-level jobs with components and subassemblies) has ...
Comments