Advisor/Chairperson: Prof. Chelsea C. White III

Committee Members: Prof. Turgay Ayer (ISYE), Prof. Alan Erera (ISYE), Prof. Julie Swann (ISYE) and Prof. David A. Bader (CSE)

Thesis Title: A MINIMUM COST AND RISK MITIGATION APPROACH FOR BLOOD COLLECTION

Date/Time/Location: July 14 2015, 11am-2pm, Groseclose Building, Rm 402

Abstract:

fac  Due to the limited supply and perishable nature of blood products, effective man-
agement of blood collection is critical for high quality healthcare delivery. Whole
blood is typically collected over a 6 to 8 hour collection window from volunteer donors
at sites, e.g., schools, universities, churches, companies, that are a significant distance
from the blood products processing facility and then transported from collection site
to processing facility by a blood mobile.

The length of time between collecting whole blood and processing it into cryo-
precipitate ("cryo"), a critical blood product for controlling massive hemorrhaging,
cannot take longer than 8 hours (the 8 hour collection to completion constraint),
while the collection to completion constraint for other blood products is 24 hours. In
order to meet the collection to completion constraint for cryo, it is often necessary
to have a "mid-drive collection"; i.e., for a vehicle other than the blood mobile to
pickup and transport, at extra cost, whole blood units collected during early in the
collection window to the processing facility.

In this dissertation, we develop Markov decision process (MDP) models to: (1)
analyze which collection sites should be designated as cryo collection sites to mini-
mize total collection costs while satisfying the collection to completion constraint and
meeting the weekly production target (the non-split case), (2) analyze the impact of
changing the current process to allow collection windows to be split into two intervals
and then determining which intervals should be designated as cryo collection intervals
(the split case), (3) use several methods to insure that the weekly production target
is met, and then build a decision support tool to provide operational decision support
to plan collection schedules.

These problems lead to MDP models with large state and action spaces and con-
straints to guarantee that the weekly production target is met with high probability.
These models are computationally intractable for problems having state and action
spaces of realistic cardinality.

We consider two approaches to guarantee that the weekly production target is met
with high probability: (1) a penalty function approach and (2) a chance constraint
approach. For the MDP with penalty function approach, we ?rst relax a constraint
that signi?cantly reduces the cardinality of the state space and provides a lower bound
on the optimal expected weekly cost of collecting whole blood for cryo while satisfying
the collection to completion constraint. We then present an action elimination proce-
dure that coupled with the constraint relaxation leads to a computationally tractable
lower bound. We then develop several heuristics that generate sub-optimal policies
and provide an analytical description of the difference between the upper and lower
bounds in order to determine the quality of the heuristics.

For the multiple decision epoch MDP model with chance constraint approach, we
first note by example that a straightforward application of dynamic programming can
lead to a sub-optimal policy. We then restrict the model to a single decision epoch.
We then use a computationally tractable rolling horizon procedure for policy deter-
mination. We also present a simple greedy heuristic (another rolling horizon decision
making procedure) based on ranking the collection intervals by mid-drive pickup cost
per unit of expected cryo collected, which results in a competitive sub-optimal solu-
tion and leads to the development of a practical decision support tool (DST). Using
real data from the American Red Cross (ARC), we estimate that this DST reduces
total cost by about 30% for the non-split case and 70% for the split case, compared
to the current practice. Initial implementation of the DST at the ARC Southern
regional manufacturing and service center supports our estimates and indicates the
potential for significant improvement in current practice.