1. Conforti, D., Guerriero, F., Guido, R.: Optimization models for radiotherapy patient scheduling. 4OR6(3), 263–278 (2008)

   Article MathSciNet  Google Scholar 

2. Hansen, P., Mladenović, N., Pérez, J.A.M.: Variable neighbourhood search: methods and applications. Ann. Oper. Res.175(1), 367–407 (2010)

   Article MathSciNet  Google Scholar 

3. Hansen, P., Mladenović, N., Todosijević, R., Hanafi, S.: Variable neighborhood search: basics and variants. EURO J. Comput. Optim.5(3), 423–454 (2016).https://doi.org/10.1007/s13675-016-0075-x

   Article MathSciNet MATH  Google Scholar 

4. Horn, M., Maschler, J., Raidl, G., Rönnberg, E.: A*-based construction of decision diagrams for a prize-collecting scheduling problem. Technical report AC-TR-18-011, Algorithms and Complexity Group, TU Wien (2018)

   Google Scholar 

5. Horn, M., Raidl, G., Blum, C.: Job sequencing with one common and multiple secondary resources: an A*/Beam Search based anytime algorithm. Artif. Intell.277(103173) (2019)

   Google Scholar 

6. Horn, M., Raidl, G., Rönnberg, E.: A* search for prize-collecting job sequencing with one common and multiple secondary resources. Ann. Oper. Res. (2020)

   Google Scholar 

7. Horn, M., Raidl, G.R., Rönnberg, E.: An A\(^*\) algorithm for solving a prize-collecting sequencing problem with one common and multiple secondary resources and time windows. In: Proceedings of the 12th International Conference of the Practice and Theory of Automated Timetabling, PATAT 2018, pp. 235–256 (2018)

   Google Scholar 

8. Horn, M., Raidl, G., Blum, C.: Job sequencing with one common and multiple secondary resources: a problem motivated from particle therapy for cancer treatment. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds.) MOD 2017. LNCS, vol. 10710, pp. 506–518. Springer, Cham (2018).https://doi.org/10.1007/978-3-319-72926-8_42

   Chapter  Google Scholar 

9. Kapamara, T., Sheibani, K., Haas, O., Petrovic, D., Reeves, C.: A review of scheduling problems in radiotherapy. In: Proceedings of the International Control Systems Engineering Conference, pp. 207–211. Coventry University Publishing (2006)

   Google Scholar 

10. Kaufmann, T.: A variable neighborhood search for the job sequencing with one common and multiple secondary resources problem. Master’s thesis, TU Wien, Vienna, Austria (2019)

    Google Scholar 

11. López-Ibáńez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Stützle, T., Birattari, M.: The irace package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect.3, 43–58 (2016)

    Article MathSciNet  Google Scholar 

12. Maschler, J., Raidl, G.R.: Multivalued decision diagrams for a prize-collecting sequencing problem. In: Proceedings of the 12th International Conference of the Practice and Theory of Automated Timetabling, PATAT 2018, pp. 375–397 (2018)

    Google Scholar 

13. Maschler, J., Riedler, M., Stock, M., Raidl, G.R.: Particle therapy patient scheduling: first heuristic approaches. In: Proceedings of the 11th International Conference of the Practice and Theory of Automated Timetabling, PATAT 2016, pp. 223–244 (2016)

    Google Scholar 

14. Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res.24(11), 1097–1100 (1997)

    Article MathSciNet  Google Scholar 

15. Schiavinotto, T., Stützle, T.: A review of metrics on permutations for search landscape analysis. Comput. Oper. Res.34(10), 3143–3153 (2007).https://doi.org/10.1016/j.cor.2005.11.022

    Article MATH  Google Scholar 

16. Van der Veen, J.A.A., Wöginger, G.J., Zhang, S.: Sequencing jobs that require common resources on a single machine: a solvable case of the TSP. Math. Program.82(1–2), 235–254 (1998)

    MathSciNet MATH  Google Scholar 

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