A Pareto Based Multi-Objective Evolutionary Algorithm Approach to Military Installation Rail Infrastructure Investment

  • Eugene Lesinski
  • Steven Corns Missouri University of Science and Technology
Keywords: Evolutionary Algorithms, Pareto Front, Rail Infrastructure Investment, Rail Repair


Decision making for military railyard infrastructure is an inherently multi-objective problem, balancing cost versus capability. In this research, a Pareto-based Multi-Objective Evolutionary Algorithm is compared to a military rail inventory and decision support tool (RAILER). The problem is formulated as a multi-objective evolutionary algorithm in which the overall railyard condition is increased while decreasing cost to repair and maintain. A prioritization scheme for track maintenance is introduced that takes into account the volume of materials transported over the track and each rail segment’s primary purpose. Available repair options include repairing current 90 gauge rail, upgrade of rail segments to 115 gauge rail, and the swapping of rail removed during the upgrade. The proposed Multi-Objective Evolutionary Algorithm approach provides several advantages to the RAILER approach. The MOEA methodology allows decision makers to incorporate additional repair options beyond the current repair or do nothing options. It was found that many of the solutions identified by the evolutionary algorithm were both lower cost and provide a higher overall condition that those generated by DoD’s rail inventory and decision support system, RAILER. Additionally, the MOEA methodology generates lower cost, higher capability solutions when reduced sets of repair options are considered. The collection of non-dominated solutions provided by this technique gives decision makers increased flexibility and the ability to evaluate whether an additional cost repair solution is worth the increase in facility rail condition.


Agarwal, S., Pape, L. E., Dagli, C. H., Ergin, N. K., Enke, D., Gosavi, A., ... & Gottapu, R. D. (2015). Flexible and intelligent learning architectures for SoS (FILA-SoS): Architectural evolution in systems-of-systems. Procedia Computer Science, 44, 76-85.
Caetano, L. F., & Teixeira, P. F. (2013). Availability approach to optimizing railway track renewal operations. Journal of Transportation Engineering, 139(9), 941-948.
Coello, C. C. (2006). Evolutionary multi-objective optimization: a historical view of the field. IEEE computational intelligence magazine, 1(1), 28-36.
Corne, D. W., Knowles, J. D., & Oates, M. J. (2000, September). The Pareto envelope-based selection algorithm for multiobjective optimization. In International conference on parallel problem solving from nature (pp. 839-848). Springer, Berlin, Heidelberg.
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000, September). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In International Conference on Parallel Problem Solving From Nature (pp. 849-858). Springer, Berlin, Heidelberg.
Diaz-Dorado, E., Cidrás, J., & Míguez, E. (2002). Application of evolutionary algorithms for the planning of urban distribution networks of medium voltage. IEEE Transactions on Power Systems, 17(3), 879-884.
Dojutrek, M. S., Labi, S., & Dietz, J. E. (2015). A fuzzy approach for assessing transportation infrastructure security. In Complex Systems Design & Management (pp. 207-224). Springer.
Eiben, Á. E., Hinterding, R., & Michalewicz, Z. (1999). Parameter control in evolutionary algorithms. IEEE Transactions on evolutionary computation, 3(2), 124-141.
Gunduz, M., Nielsen, Y., & Ozdemir, M. (2013). Fuzzy assessment model to estimate the probability of delay in Turkish construction projects. Journal of Management in Engineering, 31(4), 04014055.
Hajela, P., & Lin, C. Y. (1992). Genetic search strategies in multicriterion optimal design. Structural optimization, 4(2), 99-107.
Ishibuchi, H., & Murata, T. (1998). A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 28(3), 392-403.
Ishibuchi, H., & Murata, T. (1996, May). Multi-objective genetic local search algorithm. In Evolutionary Computation, 1996., Proceedings of IEEE International Conference on (pp. 119-124). IEEE.
Kirstukas, S. J., Bryden, K. M., & Ashlock, D. A. (2005). A hybrid genetic programming approach for the analytical solution of differential equations. International Journal of General Systems, 34(3), 279-299.
Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety, 91(9), 992-1007.
Lévi, D. (2001, November). Optimization of track renewal policy. In World Congress on Railway Research, Cologne.
Lidén, T. (2015). Railway infrastructure maintenance-a survey of planning problems and conducted research. Transportation Research Procedia, 10, 574-583. [5] D. Lévi, "Optimization of track renewal policy," World Congress on Railway Research, Cologne. 2001.
Martland, C. D., McNeil, S., Acharya, D., Mishalani, R., & Eshelby, J. (1990). Applications of expert systems in railroad maintenance: scheduling rail relays. Transportation Research Part A: General, 24(1), 39-52.
Marzouk, M., & Osama, A. (2015). Fuzzy approach for optimum replacement time of mixed infrastructures. Civil Engineering and Environmental Systems, 32(3), 269-280.
McCorkle, D. S., Bryden, K. M., & Carmichael, C. G. (2003). A new methodology for evolutionary optimization of energy systems. Computer Methods in Applied Mechanics and Engineering, 192(44-46), 5021-5036.
McGill, W. L., & Ayyub, B. M. (2007). Multicriteria security system performance assessment using fuzzy logic. The Journal of Defense Modeling and Simulation, 4(4), 356-376.
Melching, C. S., & Liebman, J. S. (1988). Allocating railroad maintenance funds by solving binary knapsack problems with precedence constraints. Transportation Research Part B: Methodological, 22(3), 181-194.
Murakami, K., & Turnquist, M. A. (1985). Dynamic model for scheduling maintenance of transportation facilities (No. 1030).
Office of Secretary of Defense for Installation and Environment, “Real Property Inventory Requirements Document,” http://www.acq.osd.mil/ie/download/rpir/rpir_appa.shtml
Oyama, T., & Miwa, M. (2006). Mathematical modeling analyses for obtaining an optimal railway track maintenance schedule. Japan Journal of Industrial and applied mathematics, 23(2), 207.
Pape, L., Giammarco, K., Colombi, J., Dagli, C., Kilicay-Ergin, N., & Rebovich, G. (2013). A fuzzy evaluation method for system of systems meta-architectures. Procedia Computer Science, 16, 245-254.
Reina, D. G., Ruiz, P., Ciobanu, R., Toral, S. L., Dorronsoro, B., & Dobre, C. (2016). A survey on the application of evolutionary algorithms for mobile multihop ad hoc network optimization problems. International Journal of Distributed Sensor Networks, 12(2), 2082496.
Sangkawelert, N., & Chaiyaratana, N. (2003, December). Diversity control in a multi-objective genetic algorithm. In Evolutionary Computation, 2003. CEC'03. The 2003 Congress on (Vol. 4, pp. 2704-2711). IEEE.
Shukla, A., Pandey, H. M., & Mehrotra, D. (2015, February). Comparative review of selection techniques in genetic algorithm. In Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), 2015 International Conference on (pp. 515-519). IEEE.
Uzarski, D. R., & Grussing, M. N. (2013). Beyond mandated track safety inspections using a mission-focused, knowledge-based approach. International Journal of Rail Transportation, 1(4), 218-236.
Uzarski, D. R., Plotkin, D. E., & Brown, D. G. (1988). The RAILER System for Maintenance Management of US Army Railroad Networks: RAILER 1 Description and Use (No. CERL-TR-M-88/18). CONSTRUCTION ENGINEERING RESEARCH LAB (ARMY) CHAMPAIGN IL.
Wang, L., Xiong, S. W., Yang, J., & Fan, J. S. (2006, August). An improved elitist strategy multi-objective evolutionary algorithm. In Machine Learning and Cybernetics, 2006 International Conference on (pp. 2315-2319). IEEE.
Zeidler, D., Frey, S., Kompa, K. L., & Motzkus, M. (2001). Evolutionary algorithms and their application to optimal control studies. Physical Review A, 64(2), 023420.
Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-report, 103.
How to Cite
Lesinski, E., & Corns, S. (2019). A Pareto Based Multi-Objective Evolutionary Algorithm Approach to Military Installation Rail Infrastructure Investment. Industrial and Systems Engineering Review, 7(2), 64-75. https://doi.org/10.37266/ISER.2019v7i2.pp64-75