Optimizing limousine service with AI

Abstract

A common problem for companies with strong business growth is that it is hard to find enough experienced staff to support expansion needs. This problem is particularly pronounced for operations planners and controllers, who must be very highly knowledgeable and experienced with the business domain. This article is a case study of how one of the largest travel agencies in Hong Kong alleviated this problem by using AI to support decision making and problem solving so that its planners and controllers can work more effectively and efficiently to sustain business growth while maintaining consistent quality of service. AI is used in a mission-critical fleet management system (FMS) that supports the scheduling and management of a fleet of luxury limousines for business travelers. The AI problem was modeled as a constraint-satisfaction problem (CSP). The use of AI enabled the travel agency to sign up additional hotel partners, handle more orders, and expand its fleet with its existing team of planners and controllers. Using modern web 2.0 architecture and proven AI technology, the agency was able to achieve low-risk implementation and deployment success with concrete and measurable business benefits. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.