Application of the Simulated Annealing Algorithm for Transport Infrastructure Planning
Decisions in planning for transport infrastructure are the result of complex technical, political, and societal concerns. Its context of limited public funding and large costs require that decision making is soundly supported. When addressing real-world problems, however, it is extremely difficult to ascertain the system configuration yielding the most value. Different alternatives exist that trade-off interrelated factors governing the value of the configurations. Metaheuristics can be of assistance when solving such real-world problems. This chapter presents an application of the simulated annealing algorithm to solve an integrated approach to high-speed rail planning. The algorithm capabilities in addressing the intricacies imposed by large and complex problems are discussed.
KeywordsMetaheuristics Simulated annealing Optimization Parameter calibration High-speed rail modeling
The authors would like to acknowledge the financial support of Fundação para a Ciência e Tecnologia (FCT) through doctoral grant (SFRH/BD/43012/2008) and the access to preliminary studies provided by former Rede Ferróviaria de Alta Velocidade, S.A. (RAVE).
- 7.EC: Commission Decision of 20 December 2007 concerning a technical specification for interoperability relating to the infrastructure sub-system of the trans-European high-speed rail system. Off. J. Eur. Union (2008)Google Scholar
- 8.CEN: Railway application—track alignment design parameters—track gauges 1435 mm and wider—Part 1: plain line. ENV 13803-1, CEN—European Committee for Standardization (2002)Google Scholar
- 9.RTRI: Design Standards for Railway Structures—Displacement Limits (2007)Google Scholar
- 10.Levinson, D., Gillen, D., Kanafani, A., Mathieu, J.: The Full Cost of Intercity Transportation—A Comparison of High Speed Rail, Air And Highway Transportation In California (1996)Google Scholar
- 13.Jong, J.C.: Optimizing highway alignments with genetic algorithms. Ph.D. Dissertation. Department of Civil and Environmental Engineering (1998)Google Scholar
- 30.Costa, A.L., Cunha, C., Coelho, P.A.L.F., Einstein, H.H.: Solving high-speed rail planning with the simulated annealing algorithm. J. Transp. Eng. 139, 635–642 (2013). doi:10.1061/(ASCE)TE.1943-5436.0000542
- 32.Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)Google Scholar
- 34.Aarts, E., Korst, J., Van Laarhoven, P.J.M.: Simulated annealing. In: Aarts, E., Lenstra, J.K. (eds.) Local Search in Combinatorial Optimization, 1st edn., pp 91–120. Wiley, New York (1997)Google Scholar
- 36.Johnson, D.S., Aragon, C.R., MCGeoch, L.A., Schevon C.: Optimization by simulated annealing—an experimental evaluation. Part 1. Graph Partit. Oper. Res. 37, 865–892 (1989) doi:10.1287/opre.37.6.865
- 41.APA: Environmental Atlas. In: Port. Environ. Agency (2012). http://sniamb.apambiente.pt/webatlas/