Abstract
The complex production processes in modern semiconductor manufacturing involve hundreds of operations on the route of a production lot, so that the period from lot release to completion can stretch over several months. Moreover, high-tech machines performing each of the operations are heterogeneous, may operate on individual wafers, lots or batches of lots in several stages, and require product-specific setups as well as dedicated maintenance procedures. This industrial setting is in sharp contrast to classical job-shop scheduling scenarios, where the production processes and machines are way less diverse and the primary focus is on solving methods for highly combinatorial yet abstract scheduling problems. In this work, we tackle the scheduling of realistic semiconductor manufacturing processes and model their elaborate requirements in hybrid Answer Set Programming, taking advantage of difference logic to incorporate machine processing, setup as well as maintenance times. While existing approaches schedule semiconductor manufacturing processes only locally, by applying greedy heuristics or isolatedly optimizing the allocation of particular machine groups, we study the prospects and limitations of scheduling at large scale.
This work was partially funded by KWF project 28472, cms electronics GmbH, FunderMax GmbH, Hirsch Armbänder GmbH, incubed IT GmbH, Infineon Technologies Austria AG, Isovolta AG, Kostwein Holding GmbH, and Privatstiftung Kärntner Sparkasse.
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Notes
- 1.
Our encoding as well as test instances are available online at: https://github.com/prosysscience/FJSP-SMT2020.
References
Abels, D., Jordi, J., Ostrowski, M., Schaub, T., Toletti, A., Wanko, P.: Train scheduling with hybrid answer set programming. Theory Pract. Logic Program. 21(3), 317–347 (2021)
Abseher, M., Gebser, M., Musliu, N., Schaub, T., Woltran, S.: Shift design with answer set programming. Fundamenta Informaticae 147(1), 1–25 (2016)
Balduccini, M.: Industrial-size scheduling with ASP+CP. In: Delgrande, J.P., Faber, W. (eds.) LPNMR 2011. LNCS (LNAI), vol. 6645, pp. 284–296. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20895-9_33
Banbara, M., et al.: teaspoon: solving the curriculum-based course timetabling problems with answer set programming. Ann. Oper. Res. 275(1), 3–37 (2019)
Brucker, P., Schlie, R.: Job-shop scheduling with multi-purpose machines. Computing 45(4), 369–375 (1990)
Ceylan, Z., Tozan, H., Bulkan, S.: A coordinated scheduling problem for the supply chain in a flexible job shop machine environment. Oper. Res. 21(2), 875–900 (2021). https://doi.org/10.1007/s12351-020-00615-0
Chaudhry, I., Khan, A.: A research survey: review of flexible job shop scheduling techniques. Int. Trans. Oper. Res. 23(3), 551–591 (2015)
Da Col, G., Teppan, E.C.: Industrial size job shop scheduling tackled by present day CP solvers. In: Schiex, T., de Givry, S. (eds.) CP 2019. LNCS, vol. 11802, pp. 144–160. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30048-7_9
Dodaro, C., Galatà, G., Grioni, A., Maratea, M., Mochi, M., Porro, I.: An ASP-based solution to the chemotherapy treatment scheduling problem. Theory Pract. Logic Program. 21(6), 835–851 (2021)
Eiter, T., Geibinger, T., Musliu, N., Oetsch, J., Skocovský, P., Stepanova, D.: Answer-set programming for lexicographical makespan optimisation in parallel machine scheduling. In: Proceedings of the Eighteenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2021), pp. 280–290. AAAI Press (2021)
El-Kholany, M.M.S., Gebser, M., Schekotihin, K.: Problem decomposition and multi-shot ASP solving for job-shop scheduling. Theory Pract. Logic Program. 22(4), 623–639 (2022)
Francescutto, G., Schekotihin, K., El-Kholany, M.M.S.: Solving a multi-resource partial-ordering flexible variant of the job-shop scheduling problem with hybrid ASP. In: Faber, W., Friedrich, G., Gebser, M., Morak, M. (eds.) JELIA 2021. LNCS (LNAI), vol. 12678, pp. 313–328. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75775-5_21
Gebser, M., et al.: Potassco user guide (2019). https://potassco.org
Hassanzadeh, A., Rasti-Barzoki, M., Khosroshahi, H.: Two new meta-heuristics for a bi-objective supply chain scheduling problem in flow-shop environment. Appl. Soft Comput. 49, 335–351 (2016)
Holthaus, O.: Efficient dispatching rules for scheduling in a job shop. Int. J. Prod. Econ. 48(1), 87–105 (1997)
Janhunen, T., Kaminski, R., Ostrowski, M., Schaub, T., Schellhorn, S., Wanko, P.: Clingo goes linear constraints over reals and integers. Theory Pract. Logic Program. 17(5–6), 872–888 (2017)
Kopp, D., Hassoun, M., Kalir, A., Mönch, L.: SMT2020–a semiconductor manufacturing testbed. IEEE Trans. Semiconductor Manuf. 33(4), 522–531 (2020)
Lifschitz, V.: Answer Set Programming. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-24658-7
Ricca, F., et al.: Team-building with answer set programming in the Gioia-Tauro seaport. Theory Pract. Logic Program. 12(3), 361–381 (2012)
Sahraeian, R., Rohaninejad, M., Fadavi, M.: A new model for integrated lot sizing and scheduling in flexible job shop problem. J. Ind. Syst. Eng. 10(3), 72–91 (2017)
Taillard, E.: Benchmarks for basic scheduling problems. Eur. J. Oper. Res. 64(2), 278–285 (1993)
Tassel, P., Rbaia, M.: A multi-shot ASP encoding for the aircraft routing and maintenance planning problem. In: Faber, W., Friedrich, G., Gebser, M., Morak, M. (eds.) JELIA 2021. LNCS (LNAI), vol. 12678, pp. 442–457. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75775-5_30
Waschneck, B., et al.: Optimization of global production scheduling with deep reinforcement learning. Procedia CIRP 72, 1264–1269 (2018)
Xing, L., Chen, Y., Wang, P., Zhao, Q., Xiong, J.: A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl. Soft Comput. 10(3), 888–896 (2010)
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Ali, R., El-Kholany, M.M.S., Gebser, M. (2023). Flexible Job-shop Scheduling for Semiconductor Manufacturing with Hybrid Answer Set Programming (Application Paper). In: Hanus, M., Inclezan, D. (eds) Practical Aspects of Declarative Languages. PADL 2023. Lecture Notes in Computer Science, vol 13880. Springer, Cham. https://doi.org/10.1007/978-3-031-24841-2_6
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