This paper describes how AI is used to plan, schedule, and optimize nightly engineering works for both the commuter and rapid transit lines in Hong Kong. The MTR Corporation Limited operates and manages all the rail lines in Hong Kong. Its “Engineering Works and Traffic Information Management System” (ETMS) is a mission critical system that manages all information related to engineering works and their related track possessions and engineering train movements. The AI Engine described in this paper is a component of this ETMS. In Hong Kong, the maintenance, inspection, repair, or installation works along the rail lines are done during the very short non-traffic hours (NTH) of roughly 4 to 5 hours each night. These engineering works can be along the running tracks, track-side, tunnel, freight yards, sub-depots, depot maintenance tracks, etc. The proper scheduling of necessary engineering works is crucial to maintaining a reliable and safe train service during normal hours. The AI Engine optimizes resource allocation to maximize the number of engineering works that can be performed, while ensuring all safety, environment, and operational rules and constraints are met. The work described is part of a project to redesign and replace the existing ETMS, deployed in 2004, with an updated technology platform and modern IT architecture, to provide a more robust and scalable system that potentially can be deployed to other cities around the world.

This paper outlines the design of a scheduling algorithm that allocates outstabling locations to railway trains. From time to time railway trains may need to be outstabled to temporary locations, such as stations, sidings, depots, etc., until they are needed for regular operations. This is common for urban rail transit, and especially so for those that do not operate 24 hours. During non-traffic hours (NTH), trains are outstabled to various locations along the rail network so that when operations start again next day, the trains will be nearby their originating station or conveniently located so that they can be put into service whenever needed. However, this is complicated by the fact that engineering works, such as rail testing, installation, regular maintenance, etc. are done during the NTH. Therefore, passenger trains must be outstabled in such a way that they do not interfere with night-time engineering works or the movements of associated engineering trains. Since the engineering works scheduling is done separate to outstabling, this is a mixed-system problem. This paper shows how we modeled this as a constraint-satisfaction problem (CSP) and implemented into an “Outstabling System” (OSS) for the Hong Kong Mass Transit Railway (MTR) using a two-stage search algorithm.

A common problem for companies with strong business growth is that it is hard to find enough experienced staff to support expansion needs. 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 their 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).

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 particular 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 their 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 their fleet with their existing team of planners and controllers. Using modern web 2.0 architecture and proven AI technology, we were able to achieve low-risk implementation and deployment success with concrete and measurable business benefits.

A common problem faced by expanding companies is the lack of skilled and experienced domain experts, especially planners and controllers. This can seriously slow down or impede growth. This paper describes how we worked with one of the largest travel agencies in Hong Kong to alleviate this problem by using AI to support decision-making and problem-solving so that their planners/controllers can be more productive in sustaining business growth while providing quality service. This paper describes a Web-based mission critical Fleet Management System (FMS) that supports the scheduling and management of a fleet of luxury limousines. Clientele is mainly business travelers. The use of AI allowed our client to increase their business volume and expand fleet size with the same team of planners/controllers while maintaining service quality. This paper also describes our experience in building modern AI systems leveraging on Web 2.0 open-source tools and libraries. Although we used a proven AI model and search algorithm, we believe our innovation is in striking the right balance and combination of AI with modern Web 2.0 techniques to achieve low-risk implementation and deployment success as well as concrete and measurable business benefits.

This article describes the architecture and AI technology behind an XML-based AI framework designed to streamline e-government form processing. The framework performs several crucial assessment and decision support functions, including workflow case assignment, automatic assessment, follow-up action generation, precedent case retrieval, and learning of current practices. To implement these services, several AI techniques were used, including rule-based processing, schema-based reasoning, AI clustering, case-based reasoning, data mining, and machine learning. The primary objective of using AI for e-government form processing is of course to provide faster and higher quality service as well as ensure that all forms are processed fairly and accurately.

The stand-allocation system (SAS) is an AI application developed for the Hong Kong International Airport (HKIA) at Chek Lap Kok. The system ensures a high standard of quality in customer service, airport safety, and use of stand resources. This article describes our experience in developing an AI system using standard off-the-shelf software components. SAS is an example of how development methodologies used to construct modern AI applications have become fully inline with mainstream practices.

In addition, to cope with conflicts caused by changes in actual operations, the airport authority also needs to make real-time problem-solving decisions on stand reassignments. the Hong Kong International Airport The stand-allocation system ( Figure world's busiest international airports in terms 1 is a snapshot of the The Although there were some initial hitches when system is installed and used in the Airport the new airport opened on 6 July 1998, operations Control Center (ACC), which is located in the quickly returned to normal within a control tower. Within a month, operational statistics management, and reactive scheduling capabilities surpassed those of the old airport--80 for stand management. The system supports percent of all flights were on time or within 15 concurrent use by multiple operators in minutes of schedule, all passengers cleared nonstop 24-hour-a-day operations because immigration within 15 minutes, and average HKIA is a 24-hour airport. Typically, a human operator must have several years of experience to acquire enough knowledge about airport operations before he/she can produce a "good" quality stand-assignment plan. Generating an allocation plan manually not only requires a highly experienced individual but is also very time consuming because it requires balancing many objectives against many possible alternatives.