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Genetic Algorithm‐Simulation Methodology for Pavement Maintenance Scheduling
Author(s) -
Cheu Ruey Long,
Wang Ying,
Fwa Tien Fang
Publication year - 2004
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2004.00369.x
Subject(s) - scheduling (production processes) , genetic algorithm , travel time , computer science , schedule , population , job shop scheduling , transport engineering , mathematical optimization , operations research , real time computing , engineering , mathematics , machine learning , sociology , demography , operating system
Pavement maintenance activities often involve lane closures, leading to traffic congestion and causing increases in road users' travel times. Scheduling of such activities should minimize the increases in travel times to all the travelers at network level. This article presents a hybrid methodology for scheduling of pavement maintenance activities involving lane closure in a network consisting of freeways and arterials, using genetic algorithm (GA) as an optimization technique, coupled with a traffic‐simulation model to estimate the total travel time of road users in the road network. The application of this scheduling method is demonstrated through a hypothetical problem consisting of assigning three maintenance teams to handle 10 job requests in a network in 1 day. After 10 generations of genetic evolution with a population size of four, the hybrid GA‐simulation model recommended a schedule that reduced the network total travel time by 5.1%, compared to the initial solution.