The Study of Using Big Data to Solve Medical Volunteer Problem

Every year, on the world there are many people that can not afford to treat diseases or they have intractable diseases. Then, it appears some organizations for medical volunteers. They will travel the globe to provide health care and health education to thousands of children, women and families every year. Sacrificing time away from their families, hardearned vacation days and even their own funds, volunteers selflessly give their expertise, health care, to the people who need it most. The main purpose of this paper is the study of using Big data to solve Medical volunteer problem (MVP) so that all hospitals are treated diseases and total travel costs are minimal. This paper introduces six methods can be used to solve MVP is (1) Northwest corner method (2) Minimum cost method (3) Vogel's approximation method (4) Row minimum Method (5) Column minimum Method (6) Russell's approximation method. After reviewing the main literature in this area, Mathematical model of the MVP, Big data, this paper presents some examples is solved by six methods. This paper introduces some methods to improve the results from the methods that gave suboptimal results. Those methods are (1) Stepping stone method (2) Modified distribution method. Finally, the paper compares different situation of the examples and propose a comparison table to know which methods in different situation is the best. In six methods, the table shows that most results of Vogel's approximation method and Russell's approximation method give optimal results. In six methods, they are accepted to be the best methods; Minimum cost method, Row minimum method and Column minimum method are second methods and finally Northwest corner method gives an initial solution very far from the optimal solutions.

並列關鍵字

Transport problem ； Linear programming ； Medical volunteer problem (MVP)

國際替代計量

全文下載

主題瀏覽