Abstract
Job-shop scheduling problems constitute a big challenge in nowadays industrial manufacturing environments. Because of the size of realistic problem instances, applied methods can only afford low computational costs. Furthermore, because of highly dynamic production regimes, adaptability is an absolute must. In state-of-the-art production factories the large-scale problem instances are split into subinstances, and greedy dispatching rules are applied to decide which job operation is to be loaded next on a machine. In this paper we propose a novel scheduling approach inspired by those hand-crafted scheduling routines. Our approach builds on problem decomposition for keeping computational costs low, dispatching rules for effectiveness and declarative programming for high adaptability and maintainability. We present first results proving the concept of our novel scheduling approach based on a new large-scale job-shop benchmark with proven optimal solutions.
The research for this paper was conducted in the scope of the project Heuristic Intelligence (HINT) in cooperation with Infineon Technologies Austria AG and Siemens AG Österreich funded by the Austrian research fund FFG under grant 840242. Authors are given in alphabetical order and contributed equally to this paper.
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Benchmark and prototype at http://isbi.aau.at/hint/scheduling-prototype.
References
Azi, N., Gendreau, M., Potvin, J.Y.: A dynamic vehicle routing problem with multiple delivery routes. Ann. Oper. Res. 199(1), 103–112 (2012)
Balduccini, M.: Representing constraint satisfaction problems in answer set programming. In: ICLP09 Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP 2009) (2009)
Balduccini, M.: Industrial-size scheduling with ASP+CP. In: Delgrande, J.P., Faber, W. (eds.) LPNMR 2011. LNCS, vol. 6645, pp. 284–296. Springer, Heidelberg (2011)
Bellman, R.: Mathematical aspects of scheduling theory. SIAM J. Soc. Ind. Appl. Math. 4, 168–205 (1956)
Bent, R., Van Hentenryck, P.: Spatial, temporal, and hybrid decompositions for large-scale vehicle routing with time windows. In: Cohen, D. (ed.) CP 2010. LNCS, vol. 6308, pp. 99–113. Springer, Heidelberg (2010)
Blazewicz, J., Ecker, K., Pesch, E., Schmidt, G., Weglarz, J.: Handbook on Scheduling: Models and Methods for Advanced Planning (International Handbooks on Information Systems). Springer, Secaucus (2007)
Carchrae, T., Beck, J.C.: Principles for the design of large neighborhood search. J. Math. Model. Algorithms 8(3), 245–270 (2009)
Easton, T., Singireddy, A.: A large neighborhood search heuristic for the longest common subsequence problem. J. Heuristics 14(3), 271–283 (2008)
Fogliatto, F.S., Da Silveira, G.J.C., Borenstein, D.: The mass customization decade: an updated review of the literature. Int. J. Prod. Econ. 138(1), 14–25 (2012)
Garey, M.R., Johnson, D.S.: Computers and Intractability. A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, New York (1979)
Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Answer Set Solving in Practice Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool Publishers, San Rafael (2012)
Gebser, M., Ostrowski, M., Schaub, T.: Constraint answer set solving. In: Hill, P.M., Warren, D.S. (eds.) ICLP 2009. LNCS, vol. 5649, pp. 235–249. Springer, Heidelberg (2009)
Hildebrandt, T., Goswami, D., Freitag, M.: Large-scale simulation-basedoptimization of semiconductor dispatching rules. In: Proceedings of the 2014 Winter Simulation Conference, WSC 2014, pp. 2580–2590. IEEE Press, Piscataway (2014). http://dl.acm.org/citation.cfm?id=2693848.2694175
Holweg, M.: The genealogy of lean production. J. Oper. Manag. 25(2), 420–437 (2007). Special Issue Evolution of the Field of Operations Management SI/ Special Issue Organisation Theory and Supply Chain Management
Hurink, J., Jurisch, B., Thole, M.: Tabu search for the job-shop scheduling problem with multi-purpose machines. Oper.-Res.-Spektrum 15(4), 205–215 (1994)
Kaban, A.K., Othman, Z., Rohmah, D.S.: Comparison of dispatching rules in job-shop scheduling problem using simulation: a case study. Int. J. Simul. Model. 11(3), 129–140 (2012)
Pacino, D., Van Hentenryck, P.: Large neighborhood search and adaptive randomized decompositions for flexible jobshop scheduling. In: Proceedings of the International Joint Conference on Artificial Intelligence (2011)
Teppan, E.C., Friedrich, G.: Heuristic constraint answer set programming. In: Proceedings of the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA16 at ECAI16) (2016)
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Da Col, G., Teppan, E.C. (2016). Declarative Decomposition and Dispatching for Large-Scale Job-Shop Scheduling. In: Friedrich, G., Helmert, M., Wotawa, F. (eds) KI 2016: Advances in Artificial Intelligence. KI 2016. Lecture Notes in Computer Science(), vol 9904. Springer, Cham. https://doi.org/10.1007/978-3-319-46073-4_11
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