Hostname: page-component-745bb68f8f-grxwn Total loading time: 0 Render date: 2025-01-12T08:33:45.307Z Has data issue: false hasContentIssue false
  • English
  • Français

An ASP-based Solution to the Chemotherapy Treatment Scheduling problem

Published online by Cambridge University Press:  24 September 2021

CARMINE DODARO Open the ORCID record for CARMINE DODARO [Opens in a new window] ,
GIUSEPPE GALATÁ ,
ANDREA GRIONI ,
MARCO MARATEA Open the ORCID record for MARCO MARATEA [Opens in a new window] ,
MARCO MOCHI  and
IVAN PORRO
CARMINE DODARO
Affiliation:
University of Calabria, Italy (e-mail: [email protected])
GIUSEPPE GALATÁ
Affiliation:
SurgiQ srl, Italy (e-mail: [email protected])
ANDREA GRIONI
Affiliation:
San Martino Hospital, Italy (e-mail: [email protected])
MARCO MARATEA
Affiliation:
University of Genoa, Italy (e-mail: [email protected])
MARCO MOCHI
Affiliation:
University of Genoa, Italy (e-mail: [email protected])
IVAN PORRO
Affiliation:
SurgiQ srl, Italy (e-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

The problem of scheduling chemotherapy treatments in oncology clinics is a complex problem, given that the solution has to satisfy (as much as possible) several requirements such as the cyclic nature of chemotherapy treatment plans, maintaining a constant number of patients, and the availability of resources, for example, treatment time, nurses, and drugs. At the same time, realizing a satisfying schedule is of upmost importance for obtaining the best health outcomes. In this paper we first consider a specific instance of the problem which is employed in the San Martino Hospital in Genova, Italy, and present a solution to the problem based on Answer Set Programming (ASP). Then, we enrich the problem and the related ASP encoding considering further features often employed in other hospitals, desirable also in S. Martino, and/or considered in related papers. Results of an experimental analysis, conducted on the real data provided by the San Martino Hospital, show that ASP is an effective solving methodology also for this important scheduling problem.

Keywords

healthcarechemotherapy treatment schedulinganswer set programming
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 21 , Special Issue 6: 37th International Conference on Logic Programming Special Issue II , November 2021 , pp. 835 - 851
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alviano, M., Amendola, G., Dodaro, C., Leone, N., Maratea, M. and Ricca, F. 2019. Evaluation of disjunctive programs in WASP. In LPNMR 2019. LNCS, vol. 11481. Springer, 241–255.Google Scholar
Alviano, M., Bertolucci, R., Cardellini, M., Dodaro, C., Galatà, G., Khan, M. K., Maratea, M., Mochi, M., Morozan, V., Porro, I. and Schouten, M. 2020. Answer set programming in healthcare: Extended overview. In IPS and RCRA 2020. CEUR Workshop Proceedings, vol. 2745. CEUR-WS.org.Google Scholar
Ansótegui, C., Pacheco, T. and Pon, J. 2019. Pypblib.Google Scholar
Brewka, G., Delgrande, J. P., Romero, J. and Schaub, T. 2015. asprin: Customizing answer set preferences without a headache. In AAAI 2015. AAAI Press, 1467–1474.Google Scholar
Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Maratea, M., Ricca, F. and Schaub, T. 2020. Asp-core-2 input language format. Theory and Practice of Logic Programming 20, 2, 294–309.CrossRefGoogle Scholar
Davies, J. 2013. Solving maxsat by decoupling optimization and satisfaction. Ph.D. thesis, University of Toronto.Google Scholar
Dodaro, C., Galatà, G., Maratea, M., Mochi, M. and Porro, I. 2020. Chemotherapy treatment scheduling via answer set programming. In CILC 2020. CEUR Workshop Proceedings, vol. 2710. CEUR-WS.org, 342–356.Google Scholar
Dodaro, C., Galatà, G., Maratea, M. and Porro, I. 2018. Operating room scheduling via answer set programming. In AI*IA. LNCS, vol. 11298. Springer, 445–459.Google Scholar
Erdem, E., Gelfond, M. and Leone, N. 2016. Applications of answer set programming. AI Magazine 37, 3, 53–68.CrossRefGoogle Scholar
Falkner, A. A., Friedrich, G., Schekotihin, K., Taupe, R. and Teppan, E. C. 2018. Industrial applications of answer set programming. Künstliche Intelligenz 32, 2–3, 165–176.CrossRefGoogle Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T. and Wanko, P. 2016. Theory solving made easy with clingo 5. In ICLP (Technical Communications). OASICS, vol. 52. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2:1–2:15.Google Scholar
Gebser, M., Kaufmann, B. and Schaub, T. 2012. Conflict-driven answer set solving: From theory to practice. Artificial Intelligence 187, 52–89.Google Scholar
Gebser, M., Obermeier, P., Schaub, T., Ratsch-Heitmann, M. and Runge, M. 2018. Routing driverless transport vehicles in car assembly with answer set programming. Theory and Practice of Logic Programming 18, 3-4, 520–534.CrossRefGoogle Scholar
Gurobi Optimization, LLC. 2021. Gurobi Optimizer Reference Manual.Google Scholar
Hahn-Goldberg, S., Carter, M. W., Beck, J. C., Trudeau, M., Sousa, P. and Beattie, K. 2014. Dynamic optimization of chemotherapy outpatient scheduling with uncertainty. Health Care Management Science 17, 4, 379–392.CrossRefGoogle ScholarPubMed
Heshmat, M. and Eltawil, A. 2021. Solving operational problems in outpatient chemotherapy clinics using mathematical programming and simulation. Annals of Operations Research 298, 1–18.CrossRefGoogle Scholar
Huang, Y.-L., Bryce, A. H., Culbertson, T., Connor, S. L. and Looker, S. A. e. a. 2017. Alternative Outpatient Chemotherapy Scheduling Method to Improve Patient Service Quality and Nurse Satisfaction. Journal of Oncology Practice 14, 2, 82–91.CrossRefGoogle ScholarPubMed
Huggins, A., Claudio, D. and Pérez, E. 2014. Improving resource utilization in a cancer clinic: An optimization model. In IIE Annual Conference and Expo 2014.Google Scholar
Ignatiev, A., Morgado, A. and Marques-Silva, J. 2019. RC2: an efficient maxsat solver. J. Satisf. Boolean Model. Comput. 11, 1, 53–64.Google Scholar
Kumar, D. and Dey, T. 2020. Treatment delays in oncology patients during COVID-19 pandemic: A perspective. Journal of Global Health 10, 1. International Society of Global Health.CrossRefGoogle Scholar
Lamé, G., Jouini, O. and Cardinal, J. 2016. Outpatient chemotherapy planning: a literature review with insights from a case study. IIE Transactions on Healthcare Systems Engineering 6, 3, 127–139.CrossRefGoogle Scholar
Martins, R., Manquinho, V. M. and Lynce, I. 2014. Open-wbo: A modular maxsat solver,. In SAT 2014. LNCS, vol. 8561. Springer, 438–445.Google Scholar
Morgado, A., Dodaro, C. and Marques-Silva, J. 2014. Core-Guided MaxSAT with Soft Cardinality Constraints. In CP 2014. Springer, Lyon, France, 564–573.Google Scholar
Olivier, Roussel and Vasco, Manquinho. 2012. Input/Output Format and Solver Requirements for the Competitions of Pseudo-Boolean Solvers.Google Scholar
Schüller, P. 2018. Answer set programming in linguistics. Künstliche Intelligence 32, 2–3, 151–155.Google Scholar
Sevinc, S., Sanli, U. A. and Goker, E. 2013. Algorithms for scheduling of chemotherapy plans. Computers in Biology and Medicine 43, 12, 2103–2109.CrossRefGoogle ScholarPubMed
Sud, A., Jones, M. E., Broggio, J., Loveday, C. and Torr, B. e. a. 2020. Collateral damage: the impact on outcomes from cancer surgery of the COVID-19 pandemic. Annals of Oncology 31, 8, 1065–1074. Elsevier.CrossRefGoogle ScholarPubMed
Turkcan, A., Zeng, B. and Lawley, M. 2012. Chemotherapy operations planning and scheduling. IIE Transactions on Healthcare Systems Engineering 2, 1, 31–49.CrossRefGoogle Scholar