An Interactive Expert System Based Decision Making Model for the Management of Transit System Alternate Fuel Vehicle Assets

• 作者: Ardeshir Faghri Michael L. Vaughan Mingxin Li
• 来源期刊: 智能信息管理(英文)
• 学科: 医学
• 关键词: EXPERT System Framework Alternative Fuel Bus Decision Making Process Information MANAGEMENT TRANSIT
• 年，卷（期）: 2017,（1）
• 页码范围: 1-20
• 页数: 20页
• 分类号: R73

摘要

Traditionally, the process used by public transportation entities to determine the acquisition strategy for new vehicle asset is based upon a broad range of criteria. Vehicle cost has been cited as one of the more critical factors which decision makers consider. It is currently a common practice to consider other factors (life-cycle cost, fuel efficiency, vehicle reliability, environmental effects, etc.) that contribute to a more comprehensive approach. This study investigates the next generation of advancements in decision making tools in the area of the application of methods to quantify and manage uncertainty. In particular, the uncertainty comes from the public policy arena where future policy and regulations are not always based upon logical and predictable processes. The fleet decision making process in most governmental agencies is a very complex and interdependent activity. There are always competing forces and agendas within the view of the decision maker. Rarely is the decision maker a single person although, within the transit environment, there is often one person charged with the responsibility of fleet management. The focus of this research examines the decision making of the general transit agency community via the development of an expert systems prototype tool. A computer-based prototype system is developed which provide an expert knowledge-based recommendation, based upon variable user inputs. The results shown in this study show that a decision making tool for the management of transit system alternate fuel vehicle assets can be modeled and tested. The direct users of this research are the transit agency administrations. The results can be used by the management teams as a reliable input to inform their urban transit buses expansion decision making process.