Unit:
Κατεύθυνση Στατιστική και Επιχειρησιακή ΈρευναLibrary of the School of Science
Author:
Karagianni Nikola
Supervisors info:
Αθανασία Μάνου, Επίκουρη Καθηγήτρια , Τμήμα Μαθηματικών, ΕΚΠΑ,
Απόστολος Μπουρνέτας, Καθηγητής, Τμήμα Μαθηματικών, ΕΚΠΑ,
Αντώνιος Οικονόμου, Καθηγητής, Τμήμα Μαθηματικών, ΕΚΠΑ
Original Title:
Allocation and scheduling of rescue units: Literature review and applications
Translated title:
Allocation and scheduling of rescue units: Literature review and applications
Summary:
This master’s thesis explores the optimization of resource allocation in
emergency response systems and healthcare facilities, with a focus on the
dispatching of rescue units and the scheduling of nurses’ appointments.
The first part of the thesis will focus on the allocation and programming of
rescue units. The importance of time in emergency situations and the need
for quick deployment of rescue units to incident locations is emphasized. To
achieve this objective, the thesis draws upon optimization concepts such as
the Travelling salesman problem, the multiple Travelling salesman problem,
and the vehicle routing problem. These concepts are incorporated into the
formulation of a mathematical model that addresses the resource allocation
optimization challenges in emergency response scheduling. Subsequently,
some heuristic methods will be described to solve the problem, as well as
metaheuristic methods to improve the heuristic solution.
The second part of the thesis focuses on a specific problem: the optimization of the scheduling of nurses’ appointments for optimum response to
medical emergencies. For this, there will be a description of the problem
and the model. A new adapted heuristic algorithm is presented as well as
a metaheuristic algorithm to improve the heuristic solution. Combining the
available theory, artificial intelligence and algorithmic methods, there will be
an application on real data.
Finally, the results of the application on real data will be analysed. The
performance of the proposed algorithm will be judged on simulated trials.
Main subject category:
Science
