Studia con noi
Decanato - Facoltà di scienze informatiche
Data: / -
USI Lugano Campus, room SI-003, Informatics building (Via G. Buffi 13)
You are cordially invited to attend the PhD Dissertation Defense of Thi Viet Ly Nguyen on Friday April 5th, 2019 at 13:30 in room SI-003 (Informatics building).
The rapid increase in demand of home health care (HHC) services has made this business more labor-intensive. An efficient workforce planning, which involves four interrelated decisions regarding timetable for nurses, nurse-patient assignment, treatment schedule, and routes planned for nurses to visit patients, is thus very crucial to provide services at high quality and low cost for patients. Despite technological advancements, HHC workforce planning is always a challenging task because of many conflicting criteria and unexpected events encountered when making decisions. Therefore, there is an urgent need for optimization algorithms that can be easily incorporated in decision support systems to provide practical and robust solutions for HHC services in reality. This thesis aims to provide optimization algorithms that jointly solve the four mentioned interrelated problems while taking uncertain availability of nurses and realistic features into account. We introduce a set of integrated robust HHC models that closely capture the problem faced by a real-life HHC company called SCuDo, operating in Canton Ticino, Switzerland. To solve these models, we propose algorithms that are a hybrid of mathematical programming, genetic algorithms, and robust optimization. The algorithms are implemented in C++ and the optimization solver Gurobi 6.5 is used to solve the embedded mathematical models. Experiments on the workforce planning instances generated using historical data from the company SCuDo show that our integrated robust HHC model can retrieve efficient and practical solutions. One of the important scientific contributions of this study is thus the development of a unified approach to dealing with multiple NP-hard HHC optimization problems, taking into account the uncertain availability of nurses. In the context of practical applications, HHC and other related industries could also benefit from this study for being able to prepare against uncertainty and making decisions that have less strain on the budget.