Genetic Algorithm for Optimizing Routing Design and Fleet Allocation of Freeway Service Overlapping Patrol

Xiuqiao Sun, Jian Wang, Weitiao Wu and Wenjia Liu
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Xiuqiao Sun: School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Jian Wang: School of Management, Harbin Institute of Technology, Harbin 150001, China
Weitiao Wu: School of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, China
Wenjia Liu: School of Management, Harbin Institute of Technology, Harbin 150001, China

Sustainability, 2018, vol. 10, issue 11, 1-15

Abstract: The freeway service patrol problem involves patrol routing design and fleet allocation on freeways that would help transportation agency decision-makers when developing a freeway service patrols program and/or altering existing route coverage and fleet allocation. Based on the actual patrol process, our model presents an overlapping patrol model and addresses patrol routing design and fleet allocation in a single integrated model. The objective is to minimize the overall average incident response time. Two strategies—overlapping patrol and non-overlapping patrol—are compared in our paper. Matrix encoding is applied in the genetic algorithm (GA), and to maintain population diversity and avoid premature convergence, a niche strategy is incorporated into the traditional genetic algorithm. Meanwhile, an elitist strategy is employed to speed up the convergence. Using numerical experiments conducted based on data from the Sioux Falls network, we clearly show that: overlapping patrol strategy is superior to non-overlapping patrol strategy; the GA outperforms the simulated annealing (SA) algorithm; and the computational efficiency can be improved when LINGO software is used to solve the problem of fleet allocation.

Keywords: combinatorial optimization; freeway service patrol; incident management; fleet allocation; patrol routing design; genetic algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
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