A column generation approach to intraday scheduling of chemotherapy patients

AbstractChemotherapy scheduling at cancer treatment centres is a complex problem due to high and growing demand, diversity of treatment protocols, limitations on resources and the need to coordinate treatment session times with laboratory preparation of medication. Over a given planning horizon, treatment centres assign patients first to specific days (interday scheduling) and then to specific times within each day (intraday scheduling), the latter process including the definition of medication preparation time. This paper addresses the intraday scheduling problem using an integer programming model that attempts to schedule all patients assigned to the horizon, and the preparation of the medication to be administered, simultaneously. The linear relaxation of the model formulation, which is based on treatment patterns, is solved using column generation. The proposed approach allows for medication preparation on the day of treatment or a previous day subject to time slot availability. A case study is conducted using actual data from a Chilean cancer centre to compare through simulation the schedules generated by the proposed approach and the centre's manual method. The results show that the proposed approach performs better on makespan, treatment chair occupancy, number of overtime hours and finding solutions at high demand levels.Keywords: Chemotherapyintraday schedulingmedication preparationtreatment patternscolumn generation AcknowledgmentsThe authors would like to thank the Adult Chemotherapy Unit of the Red de Salud UC CHRISTUS (CECA) and Dr César Sánchez for generously supplying the necessary data to carry out the practical application discussed in this paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe authors confirm that most of the data supporting the findings of this study are available within the article. Additional information is available from the corresponding author, AC, upon reasonable request.Additional informationFundingThis research was partially supported by the Chilean National Agency for Research and Development (ANID-Fondecyt) [grant Iniciación en Investigación 2019-11190633], by the Vice-Rectorate of Research at the Pontificia Universidad Católica de Chile [grants Inicio 003-2018, Investigación Interdisciplinaria 2018-II180004 and Investigación Interdisciplinaria 2020-II20001], and by the Industry Liaison Office of the Engineering School of the Pontificia Universidad Católica de Chile [grant 14ENI2–26862].Notes on contributorsGabriel LyonGabriel Lyon has a professional degree in Engineering with specialisation in Operations Research and Computer Science and a Master's degree in Operations Research from the Pontificia Universidad Católica de Chile. His professional interests include the development and implementation of decision support systems for practical problems including chemotherapy scheduling.Alejandro CataldoAlejandro Cataldo is an assistant professor at the Institute for Mathematical and Computational Engineering, School of Engineering, Pontificia Universidad Católica de Chile. His research interests include stochastic programming and evidence-based decision making under uncertainty. He has worked on the development and application of numerous solution methodologies for large-scale problems in industries such as health care, agriculture, and mining. More recently, he has collaborated with the Government of Chile in a number of research and development projects involving public services.Gustavo AnguloGustavo Angulo is an assistant professor at the Department of Industrial and Systems Engineering of Pontificia Universidad Católica de Chile. He received his Ph.D. in Operations Research from the Georgia Institute of Technology. Prior attending Georgia Tech, he received a Mathematical Engineering degree and a Master's degree in Operations Management from Universidad de Chile. His main research interests are in integer and stochastic programming, with an emphasis on decomposition methods and computational implementations.Pablo A. ReyPablo A. Rey is Assistant Professor at the Department of Industry and an associate researcher of the Programa Institucional de Fomento a la Investigación, Desarrollo e Innovación at the Universidad Tecnológica Metropolitana, Chile. He holds a B.Sc. degree in Mathematics from the National University of Crdoba, Argentina, and a Ph.D. in Electrical Engineering from the Catholic University of Rio de Janeiro, Brazil. His research interests include optimisation, simulation, and transportation.Antoine SauréAntoine Sauré is Assistant Professor at the Telfer School of Management at the University of Ottawa. His research interests include stochastic modelling, dynamic optimisation, and decision-making under uncertainty. He has more than 15 years of experience developing and applying advanced analytics techniques to large-scale problems in several industries. He has worked on the development of numerous capacity planning and patient scheduling systems aimed to provide timely access to quality cancer care.