C OMPARECENCIAS ANTE LAS C OMISIONES

The work presented in this dissertation is a step forward in addressing oncology clinic scheduling problems, but there are a few remaining aspects to the problem which serve as motivation for future research. The scope of the work in this disserta- tion for oncology clinic scheduling was limited to the drug infusion appointment. One extension would be to consider patient appointments for blood work and visits with the oncologist as well. Scheduling the blood exams, oncologist visit, and infusion appointment sequentially would pose new challenges to the scheduler. Furthermore, rescheduling is an important component of the chemotherapy scheduling problem. As future work, rescheduling of appointments can be incorporated in the models by assuming a certain percentage of appointments are rescheduled or modified due to poor blood test results. The DEVS-CHEMO and SIP-CHEMO models would then release the chair and nurse resources for the rescheduled appointments and the

new appointments would then be considered a new treatment regimen, which can be formulated as a decision problem in SIP-CHEMO. Incorporating the reschedul- ing feature will more accurately reflect the challenges faced by the scheduler in the oncology clinic.

The current implementation assumes stochastic appointment duration based on drug infusion times in the historical data. In reality, how and when the appointment duration changes is also patient-dependent. This is because the patient may have adverse reactions to the drug or take a long time to begin treatment because the nurse has difficulty setting up the patient’s IV, which can also be captured with acuity levels. In light of this, another extension would be to model the patient as an atomic model in DEVS-CHEMO such that the patient’s health status impacts appointment duration and acuity levels.

In the optimization model, the current formulation determines appointment dates and start times. The scheduler determines how much time to allocate for the ap- pointment, which has been assumed to be the planned time provided by the oncology clinic. An extension for the SIP-CHEMO model would be to reformulate the prob- lem to also determine the amount of time to allocate to each appointment. The EE and ASD mean-risk measures implemented in SIP-CHEMO are deviation measures. Another extension would be to model the problem using other mean-risk measures such as quantile deviation (QDEV). Also, the SIP-CHEMO models take significantly longer to solve with CPLEX than with algorithms. Although steps have been taken to simplify these models, current implementations still solve the deterministic equiv- alent formulation. One future direction would be to implement a decomposition method to further improve the solution speed for the SIP-CHEMO models.

Finally, there are improvements that can be made to DEVS-SIP-CHEMO regard- ing the stopping criteria and modifications. Although a few other ideas were explored,

a more detailed analysis on other stopping criteria and modifications would help gain further insight on whether more substantial improvements can be made. Improving the solution speed of SIP-CHEMO would also help complete this analysis quickly. Additionally, a more detailed guide for how to select stopping criteria and choose modifications for DEVS-SIP would help users in other application areas determine how to adapt their application to the DEVS-SIP framework to obtain improvements in system performance.

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APPENDIX A

DEVS-CHEMO MODEL