Solving Multi-Depot Dial-a-Ride Problem by Adapted Large Neighborhood Search Algorithm

碩士 === 國立臺灣大學 === 土木工程學研究所 === 107 === Aging society and handicapped welfare are both crucial issues for developed and developing countries in recent years. According to National Development Council and Ministry of Health and Welfare (2018), the population of the handicapped is about 1.17 million; e...

Full description

Bibliographic Details
Main Authors: Hsuan-Wei Fu, 傅宣維
Other Authors: 張學孔
Format: Others
Language:zh-TW
Published: 2019
Online Access:http://ndltd.ncl.edu.tw/handle/44we53
Description
Summary:碩士 === 國立臺灣大學 === 土木工程學研究所 === 107 === Aging society and handicapped welfare are both crucial issues for developed and developing countries in recent years. According to National Development Council and Ministry of Health and Welfare (2018), the population of the handicapped is about 1.17 million; especially, Tainan has the highest handicapped rate among all municipalities. The aging rate has been reached 14.1%, and expected to approaching 25% when 2031, which means Taiwan will become a super-aged society. Currently, government provides handicapped bus, long-term care bus, universal taxi service to guarantee daily mobility for the handicapped. However, due to manual handling on feet management, each scheduling task takes about 1 to 3 days. This research mainly focuses on the operation of handicapped bus. It concludes four kinds of operation ways with considering company numbers and partitioning method on scheduling, while an automatic scheduling method by ALNS algorithm is developed for optimizing the operation. Two cases with small scale have been used to prove the average accuracy of solutions obtained by ALNS algorithm and exact method. It is shown that the difference between ALNS algorithm and exact solution is lower than 5%. An empirical case of Tainan handicapped bus has been conducted with its operational pattern of highly repeatability. The average compile time of ALNS algorithm is less than 10 minutes, and the solution can improve up to 12.8% compared with the solution of manual scheduling. In addition, 16.93% can be improved if it considers all the companies’ resources. It is also shown that about 70% of benefits comes from releasing the top 20% demands. Results of this study show that automatic scheduling method can effectively improve the current operation situation which can also be a good reference for formulating policy for accessible mobility.