Optimization-Based Chemotherapy Scheduling

Table 2.3 contains a summary of the literature on the chemotherapy appointment scheduling problem using optimization-based approaches. In the past three years, researchers started using optimization models to address the chemotherapy schedul- ing problem. One model producing near optimal solutions was implemented in a real clinic setting [46], but the results are not necessarily applicable to other clinics. Although one study did consider uncertainty in real time decision requests [17], none of the works incorporated uncertainty in the problem parameters.

Table 2.3: Literature Review on Optimization-based Techniques for Chemotherapy Appointment Scheduling

Paper Brief Description Santib´a˜nez

et al. [36]

Developed chemotherapy appointment scheduling software (Chemo Smartbook) to determine near-optimal schedules. Chan [8] Used an inverse optimization model to determine nurses’ pref-

erences in the Chemo Smartbook.

Sadki et al. [35] Oncology appointment scheduling with oncology and bed re- sources using lagrangian-based heuristic and local optimization heuristics.

Turkcan et al. [46]

Multi-period time horizon chemotherapy schedule with rolling horizon methodology.

Sevinc et al. [42]

Negative feed-back algorithm for laboratory scheduling and heuristic for multiple knapsack problem of scheduling infusion appointments.

Woodall et al. [48]

Mixed-integer programming model for nurse weekly and monthly scheduling and simulation-optimization model for daily nurse shifts.

Hahn-Goldberg et al. [17]

Dynamic template scheduling to accommodate online appoint- ment requests and cancellations.

Gocgun and Puterman [13]

Used Markov decision processes and approximate dynamic pro- gramming to schedule patient appointments within specific time windows.

This work Uses stochastic optimization and simulation to account for uncertainty in three problem parameters when scheduling chemotherapy appointments and clinic resources.

The paper-based scheduling system at the British Columbia Cancer Agency’s (BCCA) Vancouver Cancer Centre was insufficient to handle increased demand and more complex treatments. A Chemo Smartbook [36] scheduling system was devel- oped as an innovative software approach that offered customized, flexible scheduling and considered patient time preferences, appointments from different departments, system capacity, nurse workload, and staff schedules. The implementation of the Chemo Smartbook led to 58% reduction in late patient appointment confirmations and a wait list reduction of 84%. Overall patient satisfaction increased, staff workload became more balanced, and stress levels were reduced. Furthermore, the wait-list size decreased by 84% and the number of days to first appointment decreased from eleven days to five days. Later, Chan [8] used an inverse optimization model to determine nurses’ preferences in order to create better schedules in the Chemo Smartbook.

Another study also addressed the appointment scheduling problem in an outpa- tient oncology clinic [35]. Their work is one of the few that consider the oncologist consultation in the problem setting. After mathematically modeling this problem, two solution methods were considered: a Lagrangian relaxation-based heuristic and local optimization heuristic. Numerical tests showed the Lagrangian relaxation-based heuristic to be best.

A multi-period time horizon approach to address the problem of scheduling pa- tients and resources for an oncology outpatient clinic was developed by Turkcan et al. [46]. The objectives were to minimize the treatment delay, patient waiting times, and staff overtime while simultaneously maximizing the staff utilization. In this two- stage problem, the first stage determined the treatment start day for the patients. In the second stage of the problem, the daily schedule was determined for all patients. Turkcan et al. [46] proposed an algorithm for solving their two-stage problem and used a rolling horizon methodology. The optimization model is closely related to

the one presented in this dissertation, but it did not include mean-risk measures or uncertain problem parameters.

Algorithms for scheduling chemotherapy regimens were developed by Sevinc et al. [42] with the goal of maintaining the treatment regimen specifications, minimizing patient waiting time, and optimizing chair utilization. This was one of the few papers to consider lab appointments along with infusion appointments. The plan for laboratory tests used an adaptive negative-feedback algorithm and target infusion chair utilization to control the load on the system. If the laboratory test results were approved by the oncologist, the second-phase determined infusion seat allocation. The second-phase was modeled using a multiple knapsack problem and solved using on-line heuristics. A simulation model was used to evaluate the scheduling methods. The main contribution of [42] was that this work addressed infusion appointment cancellations and delays due to poor laboratory test results.

Recall that Woodall et al. [48] used a mixed-integer programming model to op- timize nurse schedules on a weekly and monthly basis. The problem considered a combination of full-time and part-time nurses from different cancer disease groups. A simulation-optimization model determined a near-optimal daily schedule for the nurse shifts with the objective of minimizing the expected patient wait time. Because the model’s objective was intractable, a simulation was used to sample expected pa- tient waiting times.

Recently, a dynamic optimization model was developed by Hahn-Goldberg et al. [17] to schedule chemotherapy appointments. Their work considered uncertainty through real-time requests for appointments and uncertainty due to last-minute scheduling changes. This work used a scheduling template and online optimiza- tion in a novel technique they refer to as dynamic template scheduling. A sample of appointments were used in a deterministic optimization model to create a schedul-

ing template for the day. As appointment requests arrived, the appointments were allocated to available slots in the template. When a request arrived that did not fit the template, a new, smaller sample of appointments were generated and the open time slots were once again optimized to include the latest scheduling request. To accommodate last minute cancellations and requests, a shuffling algorithm moved appointment start times within a predefined time limit. Results show that their approach improved the makespan by 20% compared to the current practice.

Finally, recall that Gocgun and Puterman [13] used simulation and Markov deci- sion processes (MDP) to dynamically schedule chemotherapy patient appointments. In their problem, each appointment in the treatment regimen was assumed to have a time window, within a few days, of a target appointment date. The schedul- ing problem determined the date for each appointment using a MDP. The MDP was intractable so a linear-programming approximate dynamic programming (ADP) model was used to obtain an approximate solution. The simulation was implemented in GAMS and used to evaluate the scheduling decisions of the ADP as compared to several heuristics. Although the ADP solution approach was valid, the earliest policy heuristic, which schedules patients on the earliest available day within each appoint- ment’s respective time window, also worked well and was faster computationally.