Abstract
This paper considers an application of a new variant of a multi-objective flexible job-shop scheduling problem, featuring multisubset selection of manufactured recipes, to a real-world chemical plant. The problem is optimised using a multi-objective genetic algorithm with customised mutation and elitism operators that minimises both the total production time and the produced commodity surplus. The algorithm evaluation is performed with both random and historic manufacturing orders. The latter demonstrated that the proposed system can lead to more than 10 % makespan improvements in comparison with human operators.
Zusammenfassung
Dieser Artikel beschreibt die Anwendung einer neuen Variante eines mehrdimensionalen Optimierungsproblems in der flexiblen Fertigungsplanung mit mehreren Teilmengen von Fertigungsrezepten in einer realen Fabrik zur Herstellung von Farben. Das Problem wird mithilfe eines mehrdimensionalen genetischen Algorithmus mit angepassten Mutations- und Elitismus-Operatoren optimiert. Dieser Algorithmus minimiert sowohl die Gesamtproduktionszeit als auch den produzierten Warenüberschuss. Die Bewertung des Algorithmus wird sowohl mit zufällig generierten als auch mit realen historischen Fertigungsaufträgen durchgeführt. Letztere haben gezeigt, dass das vorgeschlagene System im Vergleich zum menschlichen Bediener zu einer Verbesserung der Produktionsdauer um mehr als 10 % führen kann.
Funding source: Horizon 2020 Framework Programme
Award Identifier / Grant number: 723634
Funding statement: The authors acknowledge the support of the EU H2020 SAFIRE project (Ref. 723634).
About the authors
Piotr Dziurzanski is a research fellow with the RTS group at York. He holds a PhD degree in Computer Science issued by West Pomeranian University of Technology (Szczecin, Poland) in 2003. After the completion of his doctorate degree, he worked as an assistant professor in West Pomeranian University of Technology, Poland and as a lecturer in the Staffordshire University, UK. His main research interest is related to resource allocation, cloud computing, parallel processing and reconfigurable hardware. He has published more than 50 papers in international journals and conferences.
Shuai Zhao is a research staff in real-time system group, University of York, UK. His research interests include multiprocessor resource sharing, schedulability analysis, task priority ordering, and safety-critical programming languages.
Sebastian Scholze (Dipl.-Inf.) Studied Computer Science at the University of Bremen. Since 2000, he is working as scientific staff member at ATB. He is Involved in diverse CEC funded RTD projects since the 5th FP. He is active in researching on context aware approaches and systems, object-based software models and methodologies for optimising the software development process for distributed, SOA, agent-based and interoperable and context aware systems and web-based applications. He is working as research coordinator at ATB and as project coordinator and local project manager in several EU and direct research projects. He has more than 50 publications on technical and research topics.
Albert Zilverberg (MSc.) studied Computer Science at the University of Bremen. Since 2017 he works as a scientific staff member at ATB. He gathered rich experience in the field of cyber physical systems and ambient intelligent systems as well as formal modelling emerging from a high involvement in BMBF funded projects in collaboration with the DFKI and University of Bremen. He is further involved in Cross-CPP, which has the objective to establish an IT environment offering data streams coming from mass products, and SAFIRE as a project strongly addressing process optimisation in industry.
Karl Krone active in software development for more than 30 years. Involved in development of CAD system offering solutions to diversified branches. Since 1990 employed as a SW developer at OAS and since 1994 manager of the computer technology department. Responsible for development of several systems for monitoring and process control of highly complex facilities and head of development teams for project tasks in the area of weighing and process technology. His responsibility also includes after sales services for software parts of the OAS products. Mr. Krone was involved in numerous EU projects as a local coordinator.
Leandro Soares Indrusiak has been an academic with the RTS group at York for the past 10 years (Lecturer/Assistant Prof 2008, Senior Lecturer/Associate Prof 2013, Reader 2016). His main research interests include real-time systems and networks, evolutionary optimisation, on-chip multiprocessing, distributed embedded systems, cloud and high- performance computing, and several types of resource allocation problems (in computing, manufacturing and transportation). He has published more than 150 peer-reviewed papers in the main international conferences and journals covering those topics, with more than 1400 citations and 9 best paper awards. He is or has been the principal investigator on projects funded by the EU (DreamCloud, SAFIRE), EPSRC (LowPowNoC), DFG (MPSoCMap), British Council (MapNoC) and industry (Vitronic, Fujitsu), and a co-investigator with colleagues of the RTS group in several other projects. He has graduated 9 PhD students, held visiting faculty positions in 5 different countries, and been a keynote or invited speaker in international conferences, 7 and 10 times respectively. He is a member of the EPSRC College, a member of the HiPEAC European Network of Excellence, a senior member of the IEEE, a member of the editorial board of ACM Transactions on Cyber-Physical Systems, and a member of York’s Sciences Faculty Board.
References
1. Chaudhry Imran Ali and Khan Abid Ali. A research survey: review of flexible job shop scheduling techniques. Int. Transactions in Operational Research, 23(3):551–591, 2015.Search in Google Scholar
2. Kamal Muhammad Amjad et al. Recent research trends in genetic algorithm based flexible job shop scheduling problems. Mathematical Problems in Engineering, pages 1–32, 2018.10.1155/2018/9270802Search in Google Scholar
3. Piotr Dziurzanski, Jerry Swan, Leandro Soares Indrusiak and Jose Ramos. Implementing digital twins of smart factories with interval algebra. In IEEE International Conference on Industrial Technology, ICIT, 2019.10.1109/ICIT.2019.8755026Search in Google Scholar
4. Piotr Dziurzanski, Shuai Zhao, Jerry Swan, Leandro Soares Indrusiak, Sebastian Scholze and Karl Krone. Solving the multi-objective flexible job-shop scheduling problem with alternative recipes for a chemical production process. In Applications of Evolutionary Computation, pages 33–48. Springer International Publishing, 2019.10.1007/978-3-030-16692-2_3Search in Google Scholar
5. Michael R. Garey and David S. Johnson. Computers and Intractability; A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York, NY, USA, 1990.Search in Google Scholar
6. Mitsuo Gen and Lin Lin. Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey. Journal of Intelligent Manufacturing, 25(5):849–866, Oct 2014.10.1007/s10845-013-0804-4Search in Google Scholar
7. Andy Ham. Flexible job shop scheduling problem for parallel batch processing machine with compatible job families. Applied Mathematical Modelling, 45:551–562, 2017.10.1016/j.apm.2016.12.034Search in Google Scholar
8. Nhat-To Huynh and Chen-Fu Chien. A hybrid multi-subpopulation genetic algorithm for textile batch dyeing scheduling and an empirical study. Computers & Industrial Engineering, 125:615–627, 2018.10.1016/j.cie.2018.01.005Search in Google Scholar
9. H. Ishibuchi and T. Murata. A multi-objective genetic local search algorithm and its application to flowshop scheduling. Trans. Sys. Man Cyber Part C, 28(3):392–403, August 1998.10.1109/5326.704576Search in Google Scholar
10. Lin Li and Jia-Zhen Huo. Multi-objective flexible job-shop scheduling problem in steel tubes production. Systems Engineering – Theory & Practice, 29(8):117–126, 2009.10.1016/S1874-8651(10)60063-4Search in Google Scholar
11. Carlos A Méndez et al.State-of-the-art review of optimization methods for short-term scheduling of batch processes. Computers & Chemical Engineering, 30(6-7):913–946, 2006.10.1016/j.compchemeng.2006.02.008Search in Google Scholar
12. Cemal Ozguven, Lale Ozbakır and Yasemin Yavuz. Mathematical models for job-shop scheduling problems with routing and process plan flexibility. Applied Mathematical Modelling, 34(6):1539–1548, 2010.10.1016/j.apm.2009.09.002Search in Google Scholar
13. C. S. Thomalla. Job shop scheduling with alternative process plans. International Journal of Production Economics, 74(1-3):125–134, 2001.10.1016/S0925-5273(01)00119-0Search in Google Scholar
14. Q. Zhang and H. Li. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. on Evolutionary Computation, 11(6):712–731, Dec 2007.10.1109/TEVC.2007.892759Search in Google Scholar
© 2020 Walter de Gruyter GmbH, Berlin/Boston