Optimal Risk Reduction in the Railway Industry by Using Dynamic Programming
The paper suggests for the first time the use of dynamic programming techniques for optimal risk reduction in the railway industry. It is shown that by using the concept ‘amount of removed risk by a risk reduction option’, the problem related to optimal allocation of a fixed budget to achieve a maximum risk reduction in the railway industry can be reduced to an optimisation problem from dynamic programming. For n risk reduction options and size of the available risk reduction budget B (expressed as integer number), the worst-case running time of the proposed algorithm is O (n x (B+1)), which makes the proposed method a very efficient tool
for solving the optimal risk reduction problem in the railway industry.
<p>[1] Elvik, R. (2001). Cost–benefit analysis of road safety measures:
applicability and controversies. Accident Analysis and Prevention. vol
33 (2001) pp. 9–17.
[2] J. Li, S. Pollard, G. Kendall, E. Soane, G. Davies. “Optimising risk
reduction: An expected utility approach for marginal risk reduction
during regulatory decision-making”. Reliability Engineering and System
Safety. Elsevier Ltd, (2009).
[3] B. Flyvbjerg, M. Holm, K. Skamris, S.L. Buhl. “How common and how
large are Cost Overruns in Transport Infrastructure Projects”, Transport
Reviews, volume 23 (1): 71-88, (2003).
[4] An M., Chen, Y., Baker, C.J.(2011). A fuzzy reasoning and fuzzyanalytical
hierarchy process based approach to the process of railway
risk information: A railway risk management system. Information
Sciences 181 (2011) 3946–3966. Elsevier Inc.
[5] Ramanathan, R., Ganesh, L.S. (1995). Using AHP for resource
allocation problems. European. Journal of Operational Research, vol. 80,
417.
[6] Saaty, T. (1988). The Analytic Hierarchy Process, McGraw-Hill, New
York,
[7] Ghazinoory, S., Aliahmadi A., Namdarzangeneh, S., Ghodsypour, S.H.
(2007). “Using AHP and L.P. for choosing the best alternatives based
the gap analysis”. Applied Mathematics and Computation vol. 184, pp.
316–321.
[8] Rashid, M., Hayes, D.F. (2011). “Needs-based sewerage prioritization:
Alternative to conventional cost-benefit analysis” Md. Journal of
Environmental Management, vol. 92, 2427-2440. Elsevier Ltd
[9] Cagno E., Di Giulio, A., Trucco, P. (2001). “An algorithm for the
implementation of safety improvement programs”. Safety Science, vol.
37, pp. 59-75. Elsevier Science Ltd.
[10] Persaud, B., Kazakov, A. (1994). “A procedure for allocating a safety
improvement budget among treatment types”. Accident Analysis and
Prevention. Vol. 26, No. 1, pp. 121-126. Pergamon Press Ltd.
[11] Khisty, C.J., Mohammadi, J., (2001). Fundamentals of System
Engineering, with Economics, Probability and Statistics. Prentice Hall,
Inc., Upper Saddle River, N.J,pp. 1-57.
[12] Lindhe, A., Rose´n, L., Norberg, T., Bergstedt, O., Pettersson, T.J.R.
(2011). “Cost-effectiveness analysis of risk-reduction measures to reach
water safety targets”. Water Research 45, pp. 241-253. Elsevier Ltd.
[13] Ozkir, V., Demirel, T. (2012). A fuzzy assessment framework to select
among transportation projects in Turkey. Expert Systems with
Applications, vol. 39 pp. 74–80. Elsevier Ltd.
[14] Sato, Y. (2012). “Optimal budget planning for investment in safety
measures of a chemical company”. International Journal of Production
Economics, vol. 140, pp. 579-585. Elsevier B.V.
[15] Caputo, A.C.,Pelagagge, P.M., Palumbo, M. (2011). Economic
optimization of industrial safety measures using genetic algorithms..
Journal of Loss Prevention in the Process Industries, vol. 24 pp. 541-551.
Elsevier Ltd.
[16] Pirlot, M. (1996). “General local search methods”. European Journal of
Operational Research, vol. 92, pp. 493-511.
[17] Olson, D.L. (1988). “Opportunities and Limitations of AHP in Multiobjective
Programming”. Math Computing Modelling, Vol. 11, pp. 206-
209
[18] Hey, J.D. (1995). “Experimental investigations of errors in decision
making under risk”. European Economic Review, vol. 39, pp. 633-640.
Elsevier Science B.V.
[19] Van Laarhoven, P.J.M., Aarts, E.H.L., Lenstra, J.K. (1992). "Jobshop
scheduling by simulated annealing". Operations Research, vol. 40, pp.
113-125.
[20] Aven, T., Kørte, J. (2003). “On the use of risk and decision analysis to
support decision-making”. Reliability Engineering and System Safety,
vol. 79 pp. 289–299. Elsevier Science Ltd.
[21] Fukuba, Y., Ito, K. (1984). “The so-called expected utility theory is
inadequate”. Mathematical Social Sciences, vol. 7, pp.1-12. Elsevier
Science Publishers B.V.
[22] Basso, A., Peccati, L.A. (2001). “Optimal resource allocation with
minimum activation levels and fixed costs – Theory and methodology”.
European Journal of Operational Research, vol. 131 pp. 536-549
[23] Horowitz, E., Sahni, S. (1974). "Computing partitions with applications
to the Knapsack Problem", Journal of the ACM, vol.21 pp. 277-292.
[24] Bjorndal, M.H. Caprara, A., Cowling, P.I., Croce, F.D., Lourenco, H.,
Malucelli, F., Orman, A.J., Pisinger, D., Rego, C., Salazar, J.J.
(1995).“Some thoughts on combinatorial optimization”. European
Journal of Operational Research, vol. 83, pp. 253-270.
[25] Dasgupta, S., Papadimitriou, C., Vazirani, U. (2008). “Algorithms”.
McGraw Hill, Boston, 2008.
[26] Bellman R. (1957). “Dynamic programming”. Princeton, N. J., Princeton
University Press.</p>
<p>[1] Elvik, R. (2001). Cost–benefit analysis of road safety measures:
applicability and controversies. Accident Analysis and Prevention. vol
33 (2001) pp. 9–17.
[2] J. Li, S. Pollard, G. Kendall, E. Soane, G. Davies. “Optimising risk
reduction: An expected utility approach for marginal risk reduction
during regulatory decision-making”. Reliability Engineering and System
Safety. Elsevier Ltd, (2009).
[3] B. Flyvbjerg, M. Holm, K. Skamris, S.L. Buhl. “How common and how
large are Cost Overruns in Transport Infrastructure Projects”, Transport
Reviews, volume 23 (1): 71-88, (2003).
[4] An M., Chen, Y., Baker, C.J.(2011). A fuzzy reasoning and fuzzyanalytical
hierarchy process based approach to the process of railway
risk information: A railway risk management system. Information
Sciences 181 (2011) 3946–3966. Elsevier Inc.
[5] Ramanathan, R., Ganesh, L.S. (1995). Using AHP for resource
allocation problems. European. Journal of Operational Research, vol. 80,
417.
[6] Saaty, T. (1988). The Analytic Hierarchy Process, McGraw-Hill, New
York,
[7] Ghazinoory, S., Aliahmadi A., Namdarzangeneh, S., Ghodsypour, S.H.
(2007). “Using AHP and L.P. for choosing the best alternatives based
the gap analysis”. Applied Mathematics and Computation vol. 184, pp.
316–321.
[8] Rashid, M., Hayes, D.F. (2011). “Needs-based sewerage prioritization:
Alternative to conventional cost-benefit analysis” Md. Journal of
Environmental Management, vol. 92, 2427-2440. Elsevier Ltd
[9] Cagno E., Di Giulio, A., Trucco, P. (2001). “An algorithm for the
implementation of safety improvement programs”. Safety Science, vol.
37, pp. 59-75. Elsevier Science Ltd.
[10] Persaud, B., Kazakov, A. (1994). “A procedure for allocating a safety
improvement budget among treatment types”. Accident Analysis and
Prevention. Vol. 26, No. 1, pp. 121-126. Pergamon Press Ltd.
[11] Khisty, C.J., Mohammadi, J., (2001). Fundamentals of System
Engineering, with Economics, Probability and Statistics. Prentice Hall,
Inc., Upper Saddle River, N.J,pp. 1-57.
[12] Lindhe, A., Rose´n, L., Norberg, T., Bergstedt, O., Pettersson, T.J.R.
(2011). “Cost-effectiveness analysis of risk-reduction measures to reach
water safety targets”. Water Research 45, pp. 241-253. Elsevier Ltd.
[13] Ozkir, V., Demirel, T. (2012). A fuzzy assessment framework to select
among transportation projects in Turkey. Expert Systems with
Applications, vol. 39 pp. 74–80. Elsevier Ltd.
[14] Sato, Y. (2012). “Optimal budget planning for investment in safety
measures of a chemical company”. International Journal of Production
Economics, vol. 140, pp. 579-585. Elsevier B.V.
[15] Caputo, A.C.,Pelagagge, P.M., Palumbo, M. (2011). Economic
optimization of industrial safety measures using genetic algorithms..
Journal of Loss Prevention in the Process Industries, vol. 24 pp. 541-551.
Elsevier Ltd.
[16] Pirlot, M. (1996). “General local search methods”. European Journal of
Operational Research, vol. 92, pp. 493-511.
[17] Olson, D.L. (1988). “Opportunities and Limitations of AHP in Multiobjective
Programming”. Math Computing Modelling, Vol. 11, pp. 206-
209
[18] Hey, J.D. (1995). “Experimental investigations of errors in decision
making under risk”. European Economic Review, vol. 39, pp. 633-640.
Elsevier Science B.V.
[19] Van Laarhoven, P.J.M., Aarts, E.H.L., Lenstra, J.K. (1992). "Jobshop
scheduling by simulated annealing". Operations Research, vol. 40, pp.
113-125.
[20] Aven, T., Kørte, J. (2003). “On the use of risk and decision analysis to
support decision-making”. Reliability Engineering and System Safety,
vol. 79 pp. 289–299. Elsevier Science Ltd.
[21] Fukuba, Y., Ito, K. (1984). “The so-called expected utility theory is
inadequate”. Mathematical Social Sciences, vol. 7, pp.1-12. Elsevier
Science Publishers B.V.
[22] Basso, A., Peccati, L.A. (2001). “Optimal resource allocation with
minimum activation levels and fixed costs – Theory and methodology”.
European Journal of Operational Research, vol. 131 pp. 536-549
[23] Horowitz, E., Sahni, S. (1974). "Computing partitions with applications
to the Knapsack Problem", Journal of the ACM, vol.21 pp. 277-292.
[24] Bjorndal, M.H. Caprara, A., Cowling, P.I., Croce, F.D., Lourenco, H.,
Malucelli, F., Orman, A.J., Pisinger, D., Rego, C., Salazar, J.J.
(1995).“Some thoughts on combinatorial optimization”. European
Journal of Operational Research, vol. 83, pp. 253-270.
[25] Dasgupta, S., Papadimitriou, C., Vazirani, U. (2008). “Algorithms”.
McGraw Hill, Boston, 2008.
[26] Bellman R. (1957). “Dynamic programming”. Princeton, N. J., Princeton
University Press.</p>
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:62243", author = "Michael Todinov and Eberechi Weli", title = "Optimal Risk Reduction in the Railway Industry by Using Dynamic Programming", abstract = "The paper suggests for the first time the use of dynamic programming techniques for optimal risk reduction in the railway industry. It is shown that by using the concept ‘amount of removed risk by a risk reduction option’, the problem related to optimal allocation of a fixed budget to achieve a maximum risk reduction in the railway industry can be reduced to an optimisation problem from dynamic programming. For n risk reduction options and size of the available risk reduction budget B (expressed as integer number), the worst-case running time of the proposed algorithm is O (n x (B+1)), which makes the proposed method a very efficient tool
for solving the optimal risk reduction problem in the railway industry.
", keywords = "Optimisation, railway risk reduction, budget
constraints, dynamic programming.", volume = "7", number = "7", pages = "1612-5", }
