00983405

Component

https://doi.org/10.3141/1889-01

http://scholar.google.com...+Tan&publication_year=2004

The budget-allocation process in pavement management occurs at several levels of decision making. In normal practice, the central level is concerned with the allocation of the budget to regional agencies to improve the health of the overall road network, while the regions are responsible for the programming and scheduling of road maintenance in their respective networks. The information processed at different management levels differs, and information at one level may not be readily available to the other. As a result, the maintenance funds allocated by central management to regional highway agencies may not be best used. A budget-allocation procedure based on artificial-intelligence agents is presented; it takes into account information integration between the central and regional management levels by using multiagent systems. Agents are used to facilitate the interaction between decision makers at the two management levels and thus provide a means for vertical information integration. A hypothetical problem containing a two-level funds-allocation structure consisting of a central-level management and three regional highway agencies is investigated. Each regional agency has different road network profiles with different local-level objectives. A genetic algorithm is used as the optimization tool in the decision-making process carried out by each agent. The results are compared with conventional allocation approaches and a two-stage optimization approach. It is shown that, with better integration of information at the different management levels, the resulting budget-allocation strategy produces greater maintenance-cost savings for a given target level of pavement performance.

This paper appears in Transportation Research Record No. 1889, Pavement Management, Monitoring, Evaluation, and Data Storage 2004.

00983404

English

p. 3-12

2004

Transportation Research Record

0309094844

Figures (7) ; References (21) ; Tables (2)

Artificial intelligence; Budgeting; Costs; Decision making; Genetic algorithms; Multi-agent systems; Optimization; Pavement maintenance; Pavement management systems; Savings

Funds allocation; Information integration

Design; Finance; Highways; Pavements; I22: Design of Pavements, Railways and Guideways; I23: Properties of Road Surfaces

TRIS, TRB, ATRI

Dec 27 2004 12:00AM

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