Predictive and reactive approaches to the train-scheduling problem: A knowledge management perspective

Abstract: Predictive and reactive train scheduling are tactical and operational decision making, respectively, under constraints (e.g., resource capacity, managerial objectives) and under uncertainty (e.g., imprecise data and information, unforeseen events). Predictive scheduling produces timetables taking into account the market demand and resources utilization levels. Reactive scheduling challenges disruptions to timetables and schedules trains and operations with imprecise plans. Expert knowledge is indispensable for finding practical solutions for both predictive and reactive scheduling. Consequently, knowledge management (KM) strategies, processes and technologies can improve the decision-making process and outcomes. This paper focuses on the following issues. Five dimensions are introduced to distinguish predictive and reactive train-scheduling activities. The combined use of data and knowledge and the differences in uncertainty levels are used to position comparatively the two scheduling approaches. The intensity of reliance on explicit and tacit knowledge is highlighted via the elaboration and classification of knowledge used in either one or both scheduling environments. The significance of train-scheduling tacit knowledge elicitation is described by, first, presenting a real case analysis which resulted in the elicitation of rich and valuable tacit knowledge (timetabling heuristics) from explicit knowledge (timetable) and, second, generalizing lessons learned from this process. The contributions of the tacit knowledge elicitation process to the enhancement of the train-scheduling system which leads to better resource utilization and customer satisfaction are itemised characteristics and that most forms of relevant knowledge are mixed in these respects. Thirdly, we contest their implicit assumption that codification always represents progress. We conclude that for these reasons their intellectual exercise of extending definitions of what is codified and possible to codify, while in principle addressing very important issues related to innovation policy and knowledge management, ends up having limited practical implications for these areas.

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