Vehicle route selection with an adaptive neuro fuzzy inference system in uncertainty conditions
DOI:
https://doi.org/10.31181/dmame180113pKeywords:
Neuro-Fuzzy Model, Vehicle Assignment Problem, Route SelectionAbstract
A useful routing system should have the capability of supporting the driver effectively in deciding on an optimum route to his preference. This paper describes the problem of choice of road route under conditions of uncertainty which drivers are faced with as they carry out their task of transportation. The choice of road route depends on the needs stated in the transport requirements, the location of the users and the conditions under which the transport task is performed. The route guidance system developed in this paper is an Adaptive Neuro Fuzzy Inference Guidance System (ANFIGS) that provides instructions to drivers based upon "optimum" route solutions. A dynamic route guidance (DRG) system routes drivers using the current traffic conditions. ANFIGS can provide actual routing advice to the driver in light of the real-time traffic conditions. In the DRG system for the choice of road route, the experiential knowledge of drivers and dispatchers is accumulated in a neuro-fuzzy network which has the capability of generalizing a solution. The adaptive neuro-fuzzy network is trained to select an optimal road route on the basis of standard and additional criteria. As a result of the research, it is shown that the suggested adaptable fuzzy system, which has the ability to learn, has the capability of imitating the decision making process of the drivers and dispatchers and of showing a level of competence which is comparable with the level of their competence.
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Bertsimas, D. J., & Simchi-Levi, D. (1996). A New Generation of Vehicle Routing Research: Robust Algorithms, Addressing Uncertainty. Operations Research, 44 (2), 286 - 304.
Ćirović, G., & Pamučar., D. (2013). Decision Support Model for Prioritizing Railway Level Crossings for Safety Improvements: Application of the Adaptive Neuro-Fuzzy System. Expert Systems with Applications, 40 (6), 2208-2223.
Ćirović, G., Pamučar., D., & Božanić, D. (2014). Green Logistic Vehicle Routing Problem: Routing Light Delivery Vehicles in Urban Areas Using a Neuro-Fuzzy Model. Expert Systems with Applications, 41, 4245-4258.
Gendreau, M., Guertin, F., Potvin, J.Y., & Séguin, R. (2006). Neighborhood Search Heuristics for a Dynamic Vehicle Dispatching Problem with Pick-Ups and Deliveries. Transportation Research Part C: Emerging Technologies, 14 (3), 157–174.
Ghiani, G., Guerriero, F., Laporte, G., & Musmanno, R. (2003). Real-Time Vehicle Routing: Solution Concepts, Algorithms and Parallel Computing Strategies. European Journal of Operational Research, 151 (1), 1–11.
Jovanović, A., Pamučar, D., Pejčić-Tarle, S. (2014). Green vehicle routing in urban zones – A neuro-fuzzy approach. Expert systems with applications, 41, 3189–3203.
Lobel, A. (1997). Vehicle Scheduling in Public Transit and Lagrangean Pricing. Management Science, 44, 1637–49.
Maalouf, M., Cameron A.M., Sridhar R., & Court, M. (2014). A New Fuzzy Logic Approach to Capacitated Dynamic Dial-a-Ride Problem. Fuzzy Sets and Systems, 255, 30–40.
Masson, R., Lahrichi, N., & Rousseau, L.M. (2015). A Two-Stage Solution Method for the Annual Dairy Transportation Problem. European Journal of Operational Research, 251 (1), 1–8.
Milenković, M., Bojović, N., Švadlenka, L., & Melichar, V. (2015). A Stochastic Model Predictive Control to Heterogeneous Rail Freight Car Fleet Sizing Problem. Transportation Research Part E: Logistics and Transportation Review, 82, 162–98.
Pamučar, D., & Ćirović, G. (2015). The Selection of Transport and Handling Resources in Logistics Centers Using Multi-Attributive Border Approximation Area Comparison (MABAC). Expert Systems with Applications, 42 (6), 3016–28.
Pamučar, D., Božanić, D., & Ranđelović, A. (2017). Multi-criteria decision making: An example of sensitivity analysis. Serbian journal of management, 11(1), 1-27.
Pamučar, D., Gigović, Lj., Ćirović, G., & Regodić, M. (2016a). Transport Spatial Model for the Definition of Green Routes for City Logistics Centers. Environmental Impact Assessment Review, 56, 72–87.
Pamučar, D., Ljubojević, S., Kostadinović, D., & Đorović, B. (2016b). Cost and Risk aggregation in multi-objective route planning for hazardous materials transportation - A neuro-fuzzy and artificial bee colony approach. Expert Systems with Applications, 65, 1-15.
Pamučar, D., Lukovac, V., & Pejčić Tarle, S. (2013) Application of Adaptive Neuro Fuzzy Inference System in the process of transportation support. Asia-Pacific Journal of Operational Research, 30 (2), 1250053/1- 1250053/32.
Pamučar, D., Vasin, Lj., Atanasković, P., & Miličić, M. (2016c). Planning the city logistics terminal location by applying the green p-median model and type-2 neuro-fuzzy network. Computational Intelligence and Neuroscience, Article ID 6972818, http://dx.doi.org/10.1155/2016/6972818.
Pap, E., Bošnjak, Z., & Bošnjak, S. (2000). Application of Fuzzy Sets with Different T-Norms in the Interpretation of Portfolio Matrices in Strategic Management. Fuzzy Sets and Systems, 114 (1), 123–131.
Pilla, V.L., Rosenberger, J.M., Chen, V., Engsuwan, N., & Siddappa, S. (2012). A Multivariate Adaptive Regression Splines Cutting Plane Approach for Solving a Two-Stage Stochastic Programming Fleet Assignment Model. European Journal of Operational Research, 216 (1), 162–71.
Pillac, V., Gendreau, M., Guéret, C., & Medaglia, A.L. (2011). A Review of Dynamic Vehicle Routing Problems. Cirrelt, 62, 0–28.
Piu, F., Kumar, V.P., Bierlaire, M., & Speranza, M.G. (2015). Introducing a Preliminary Consists Selection in the Locomotive Assignment Problem. Transportation Research Part E: Logistics and Transportation Review, 82, 217–237.
Preetvanti, S., & Saxena, P.K. (2003). The Multiple Objective Time Transportation Problem with Additional Restrictions. European Journal of Operational Research, 146 (3), 460–476.
Rais, A., Alvelos, F., & Carvalho, M. S. (2014). New Mixed Integer-Programming Model for the Pickup-and-Delivery Problem with Transshipment. European Journal of Operational Research, 235 (3), 530–539.
Rouillon, S., Desaulniers, G., & Soumis, F. (2006). An Extended Branch-and-Bound Method for Locomotive Assignment. Transportation Research Part B: Methodological, 40 (5), 404–423.
Salari, M., & Naji-Azimi, Z. (2012). An Integer Programming-Based Local Search for the Covering Salesman Problem. Computers & Operations Research, 39 (11), 2594–2602.
Shi, A., Cui, N., Bai, Y., Xie, W., Chen, M., & Ouyang, Y. (2015). Reliable Emergency Service Facility Location under Facility Disruption, En-Route Congestion and in-Facility Queuing. Transportation Research Part E: Logistics and Transportation Review, 82, 199-216.
Sicilia, J.A., Quemada, C., Royo, B., & Escuín, D. (2015). An Optimization Algorithm for Solving the Rich Vehicle Routing Problem Based on Variable Neighbourhood Search and Tabu Search Metaheuristics. Journal of Computational and Applied Mathematics, 291, 468–477.
Teodorović, D., & Pavković, G. (1996). The Fuzzy Set Theory Approach to the Vehicle Routing Problem When Demand at Nodes Is Uncertain. Fuzzy Sets and Systems, 82, 307–317.
Veluscek, M., Kalganova, T., Broomhead, P., & Grichnik, A. (2015). Composite Goal Methods for Transportation Network Optimization. Expert Systems with Applications 42 (8), 3852–3867.
Vukadinović, K., Teodorović, D., & Pavković, G. (1999). An Application of Neurofuzzy Modeling: The Vehicle Assignment Problem. European Journal of Operational Research, 114 (3), 474–488.
Werth, T.L., Holzhauser, M., & Krumke. S.O. (2014). Atomic Routing in a Deterministic Queuing Model. Operations Research Perspectives, 1 (1), 18–41.
Ying, Z., Qi, M., Lin, W.H., & Miao, L. (2015). A Metaheuristic Approach to the Reliable Location Routing Problem under Disruptions. Transportation Research Part E: Logistics and Transportation Review, 83, 90–110.
Yongheng, J., & Grossmann, I.E. (2015). Alternative Mixed-Integer Linear Programming Models of a Maritime Inventory Routing Problem. Computers & Chemical Engineering 77, 147–61.
Yuzhen, H., Xu, B., Bard, J.F., Chi, H., & Gao, M. (2015). Optimization of Multi-Fleet Aircraft Routing Considering Passenger Transiting under Airline Disruption. Computers & Industrial Engineering, 80,132–44.
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