Information Technologies and Analytical Models for Strategic Design of Transportation Infrastructure

Information Technologies and Analytical Models for Strategic Design of Transportation Infrastructure

L. Douglas Smith (University of Missouri – St. Louis, USA), Robert M. Nauss (University of Missouri – St. Louis, USA), Liang Xu (University of Missouri – St. Louis, USA), Juan Zhang (University of Missouri – St. Louis, USA), Jan Fabian Ehmke (Freie Universität Berlin, Germany) and Laura Hellmann (Freie Universität Berlin, Germany)
DOI: 10.4018/978-1-5225-1680-4.ch013
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Statistical modeling, deterministic optimization, heuristic scheduling procedures, and computer simulation enable the strategic design of service systems while considering complex interdependencies in system operations. Performance on multiple dimensions may be investigated under alternative physical configurations and operating procedures while accommodating time-varying mixes of traffic and demands for service. This paper discusses how analytical tools and a conceptual framework developed for inland waterway transportation were extended and applied to the more complex operating environment of commercial airports. Networks of staged queues constitute the conceptual framework and discrete-event simulation provides the integrating modeling platform. Within the simulation model, statistical models represent time-varying behavior, traffic intensity is adjusted, resources are allocated to system users, traffic is controlled according to prevailing conditions, and decision rules are tested in pursuit of optimal performance.
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Background: Prevalence Of Staged Queueing Systems

The strategic design of service systems requires a conceptual framework and an effective information infrastructure. Queueing systems or waiting lines are the means by which we organize activity involving shared resources. Fast-food restaurants, roadways, airport check-in stations, call-service centers, supermarket checkouts, and myriads of other common service systems require the management of queueing systems. In a Wall Street Journal article, Bialik (2012) reported how Alfred Blumstein suggested the queuing structure for UK passport control might be changed to reduce queuing times in anticipation of a flood of visitors to the Olympic Games. By staging individuals next in line more closely to individual passport control officers, one may eliminate the delays that occur as individuals walk from the head of a single queue to the next available agent. Doing so, however, exposes the travelers to a risk of being stuck behind a person who requires an unduly long processing time while individuals who arrived later proceed through another channel.

In cross-docking facilities, tractor-trailers are positioned for loading and unloading with different mixes of cargo. Manpower and equipment are allocated to unload incoming shipments and load outgoing shipments (Bartholdi & Gue, 2000; Gue & Kang, 2001). In production and maintenance facilities, there is often a preparatory step before an operation whereby the next entity to be processed receives a necessary pre-treatment (Guo et al., 2012; Hung & Chang, 2002). Port facilities require vessels to be positioned for loading and unloading with land-based resources (Shabayek & Yeung, 2002). In busy urban environments, signaling is used to grant priority to buses and regulate flows of other traffic through intersections (Wu & Hounsell, 1998). Medical facilities perform triage and assign patients to examination rooms, surgical theatres and diagnostic equipment (Oredsson et al., 2011). When queued entities have identifiable characteristics that affect the expected time to service them, efficiencies can be achieved by altering queueing structures and the sequences in which entities are served.

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