Transport modeling: An artificial immune system approach
DOI:
https://doi.org/10.2298/YJOR0601003TKeywords:
Uncertainty modelling, fuzzy sets, artificial immune system, transportation, trafficAbstract
This paper describes an artificial immune system approach (AIS) to modeling time-dependent (dynamic, real time) transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions) that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies) for different antigens (different traffic "scenarios"). This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.References
Belobaba, P.P., "Airline yield management: An overview of seat inventory control", Transportation Science, 21 (1987) 66-73.
Belobaba, P.P., "Application of a probabilistic decision model to airline seat inventory control", Operations Research, 37 (1989) 183-197.
Bertsimas, D., Chervi, P., and Peterson, M., "Computational approaches to stochastic vehicle routing problems", Transportation Science, 29 (1995) 342-352.
Bingham, E., "Reinforcement learning in neurofuzzy traffic signal control", European Journal of Operational Research, 131 (2001) 232-241.
Bodily, S., and Weatherford, L., "Perishable-asset revenue management - generic and multiple-price yield management with diversion", Omega-International Journal of Management Science, 23 (1995) 173-185.
Brumelle, S.L., and McGill, J.I., "Airline seat allocation with multiple nested fare classes", Operations Research, 41 (1993) 127-137.
Brumelle, S.L., McGill, J.I., Oum, T.H., Sawaki, K., and Tretheway, M.W., "Allocation of airline seats between stochastically dependent demands", Transportation Science, 24 (1990) 183-192.
Chang, Y-H., and Shyu, T-H., "Traffic signal installation by the expert system using fuzzy set theory for inexact reasoning", Transportation Planning and Technology, 17 (1993) 191-202.
Chen, L., May, A., and Auslander, D., "Freeway ramp control using fuzzy set theory for inexact reasoning", Transportation Research, 24A (1990) 15-25.
Cybenko, G., "Approximation by superpositions of a sigmoidal function", Mathematics of Control, Signals, and System, 2 (1989) 303-314.
Dror, M., "Modeling vehicle routing with uncertain demands as a stochastic program: properties of the corresponding solution", European Journal of Operational Research, 64 (1993) 432-441.
Dror, M., Laporte, G., and Louveaux, F., "Vehicle routing with stochastic demands and restricted failures", Operations Research, 37 (1993) 273-283.
Dror, M., Laporte, G., and Trudeau, P., "Vehicle routing with stochastic demands: properties and solution frameworks", Transportation Science, 23 (1989) 166-176.
Dror, M., and Trudeau, P., "Stochastic vehicle routing with modified savings algorithm", European Journal of Operational Research, 23 (1986) 228-235.
Gendreau, M., Laporte, G., Seguin, R., "Stochastic vehicle routing", European Journal of Operational Research, 88 (1996) 3-12.
Goldberg, D., Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Reading, MA, 1989.
Gosavi, A., Bandla, N., and Das, T.K., "A reinforcement learning approach to a single leg airline revenue management problem with multiple fare classes and overbooking", IIE Transactions, 34 (2002) 729-742.
Holland, J., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, 1975.
Hornik, K., Stinchcombe, M., and White, H., "Multilayer feedforward networks are universal approximators", Neural Networks, 2 (1989) 359-366.
Kim, J.W., Kim, B.M., and Kim, J.Y., "Genetic algorithm simulation approach to determine membership functions of fuzzy traffic controller", Electronics Letters, 34 (1998) 1982-1983.
Klir, G., and Folger, T., Fuzzy Sets, Uncertainty and Information, Prentice Hall, Englewood Cliffs, NJ, 1988.
Kosko, B., Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, NJ, 1992.
Kosko, B., Fuzzy Thinking, Hyperion, New York, NY, 1993.
Laporte, G., "The vehicle routing problem - An overview of exact and approximate algorithms", European Journal of Operational Research, 59 (1992) 345-358.
Lambert, V., Laporte, G., and Louveaux, F.V., "Designing collection routes through bank branches", Computers & Operations Research, 20 (1993) 783-791.
Lautenbacher, C.J., and Stidham, S., "The underlying Markov decision process in the single leg airline yield-management problem", Transportation Science, 33 (1999) 136-146.
Lee, J.H., and Lee-Kwang, H., "Distributed and cooperative fuzzy controllers for traffic intersections group", IEEE Transactions on Systems Man and Cybernetics Part C, 29 (1999) 263-271.
Littlewood, K., "Forecasting and control of passengers bookings", in: XII AGIFORS Symposium Proceedings, 1972, 95-117.
Luk, J., "Two traffic-responsive area traffic control methods: SCAT and SCOOT", Traffic Engineering and Control, 25 (1984) 14-20.
Mendel, J., "Fuzzy logic systems for engineering: A tutorial", Proceedings of the IEEE, 83 (1995) 345-377.
Mendel, J., Uncertain Rule-Based Fuzzy Logic Systems, Prentice Hall, Upper Saddle River, NJ, 2001.
Nakatsuyama, M., Nagahashi, N., and Nishizuka, N., "Fuzzy logic phase controller for traffic functions in the one-way arterial road", Proceedings of the IFAC 9th Triennial World Congress, Pergamon Press, Oxford, 1983, 2865-2870.
Niittymaki, J., "General fuzzy rule base for isolated traffic signal control-rule formulation", Transportation Planning and Technology, 24 (2001) 227-247.
Niittymaki, J., and Pursula, M., "Signal control using fuzzy logic", Fuzzy Sets and Systems, 116 (2000) 11-22.
Pappis, C., and Mamdani, E., "A fuzzy controller for a traffic junction", IEEE Transactions on Systems, Man and Cybernetics, SMC-7 (1977) 707-717.
Popovic, J., "Vehicle routing in the case of uncertain demand: A Bayesian approach", Transportation Planning and Technology, 19 (1995) 19-29.
Popovic, J., and Teodorovic, D., "An adaptive method for generating demand inputs to airline seat inventory control models", Transportation Research B, 31 (1997) 159-175.
Potvin, J.Y., Duhamel, C., and Guertin F., "A genetic algorithm for vehicle routing with backhauling", Applied Intelligence, 6 (1996) 345-355.
Powell, W., "An operational planning model for the dynamic vehicle allocation problem with uncertain demands", Transportation Research, 21B (1987) 217-232.
Robertson, D., and Bretherton, R.D., "Optimizing networks of traffic signals in real time-the SCOOT method", IEEE Transactions on Vehicular Technology, 40 (1991) 11-15.
Sasaki, T., and Akiyama, T., "Traffic control process of expressway by fuzzy logic, fuzzy sets and systems", 26 (1988) 165-178.
Sayers, T., "Issues in the development of a multi-objective GA to optimise traffic signal controller parameters", Multiple Criteria Decision Making in the New Millennium, Lecture Notes in Economics and Mathematical Systems, 507 (2001) 437-446.
Sayers, T., and Bell, M.H.G., "Traffic responsive signal control using fuzzy logic-A practical modular approach", Proceedings of the Fourth European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, 1996, 2159-2163.
Secomandi, N., "Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demands", Computers & Operations Research, 27 (2000) 1201-1225.
Secomandi, N., Abbott, K., Atan, T., and Boyd, E.A., "From revenue management concepts to software systems", Interfaces, 32 (2002) 1-11.
Subramanian, J., Stidham, S., and Lautenbacher, C.J., "Airline yield management with overbooking, cancellations, and no-shows", Transportation Science, 33 (1999) 147-167.
Swan, W.M., "Airline demand distributions: passenger revenue management and spill", Transportation Research E, 38 (2002) 253-263.
Teodorovic, D., Airline Operations Research, Gordon and Breach Science Publishers, New York, 1988.
Teodorovic, D., "Fuzzy sets theory applications in traffic and transportation-invited review", European Journal of Operational Research, 74 (1994) 379–390.
Teodorovic, D., "Airline network seat inventory control: Fuzzy set theory approach", Transportation Planning and Technology, 22 (1998) 47-72.
Teodorovic, D., "Fuzzy logic systems for transportation engineering: The state of the art", Transportation Research, 33A (1999) 337-364.
Teodorovic, D., Krcmar-Nozic, E., and Stojkovic, G., "Airline seat inventory control by application of the simulated annealing", Transportation Planning and Technology, 17 (1993) 219–233.
Teodorovic, D., and Lucic, P., "Intelligent vehicle routing system", Proceedings of the 3rd IEEE Conference on Intelligent Transportation Systems, Dearborn, MI, 2000, 482-487.
Teodorović, D., Lučić, P., Popović, J., Kikuchi, S., and Stanić, B., "Intelligent isolated intersection", Proceedings of the 10th International IEEE Conference on Fuzzy Systems, Melbourne, Australia, 2001, 276–279.
Teodorović, D., and Pavković, G., "A simulated annealing technique approach to the vehicle routing problem in the case of stochastic demand", Transportation Planning and Technology, 16 (1992) 261-273.
Teodorović D, and Pavković G, "The fuzzy set theory approach to the vehicle routing problem when demand at nodes is uncertain", Fuzzy Sets and Systems, 82 (1996) 307-317.
Teodorovic, D., Popovic, J., Pavkovic, G., and Kikuchi, S., "Intelligent airline seat inventory control system", Transportation Planning and Technology, 25 (2002a) 155-173.
Teodorović, D., Varadarajan, V., Chinnaswamy, M.R., and Ramaraj, S., "Evolution of real time traffic adaptive signal control algorithms", in: Proceedings of The International Conference on Operations Research for Development (ICORD 2002b), Anna University, Chennai (Madras), India, 2002b.
Teodorovic, D., and Vukadinovic, K., Traffic Control and Transport Planning: A Fuzzy Sets and Neural Networks Approach, Kluwer Academic Publishers, Boston/Dordrecht/London, 1998.
Vander, A.J., Sherman, J.H., and Luciano, D.S., Human Physiology, McGraw-Hill Publishing Company, New York, 1990.
Van Ryzin, G., and McGill, J., "Revenue management without forecasting or optimization: An adaptive algorithm for determining airline seat protection levels", Management Science, 46 (2000) 760-775.
Van Breedam, "Comparing descent heuristics and metaheuristics for the vehicle routing problem", Computers & Operations Research, 28 (2001) 289-315.
Wang, L-X., and Mendel, J., "Generating fuzzy rules by learning from examples", IEEE Transactions on Systems, Man and Cybernetics, 22 (1992a) 1414-1427.
Wang, L-X., and Mendel, J., "Back-propagation of fuzzy systems as nonlinear dynamic system identifiers", Proceedings IEEE International Conference on Fuzzy Systems, San Diego, CA, 1992(b) 807-813.
Wang, L.-X, and Mendel, J., "Fuzzy basis functions, universal approximation, and orthogonal least squares learning", IEEE Transactions on Neural Networks, 3 (1992c) 807-813.
Weatherford, L., "Using prices more realistically as decision variables in perishable-asset revenue management problems", Journal of Combinatorial Optimization, 1 (1997) 277-304.
Weatherford, L., and Bodily, S., "A taxonomy and research overview of perishable-asset revenue management - yield management, overbooking, and pricing", Operations Research, 40 (1992) 831-844.
Weatherford, L., Bodily, S., and Pfeifer, P., "Modeling the customer arrival process and comparing decision rules in perishable asset revenue management situations", Transportation Science, 27 (1993) 239-251.
Yang, W.-H., Mathur, K., and Ballou, R.H., "Stochastic vehicle routing problem with restocking", Transportation Science, 34 (2000) 99-112.
Zadeh, L., "Fuzzy sets", Information and Control, 8 (1965) 338-353.
Downloads
Published
Issue
Section
License
Copyright (c) 2006 YUJOR
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
