Vehicle route selection with an adaptive neuro fuzzy inference system in uncertainty conditions

  • Dragan Pamucar University of defence in Belgrade, Military academy, Department of logistics, Belgrade, Serbia
  • Goran Ćirović The Belgrade University College of Civil Engineering and Geodesy, Belgrade, Serbia
Keywords: Neuro-Fuzzy Model, Vehicle Assignment Problem, Route Selection

Abstract

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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Published
2018-03-15
How to Cite
Pamucar, D., & Ćirović, G. (2018). Vehicle route selection with an adaptive neuro fuzzy inference system in uncertainty conditions. Decision Making: Applications in Management and Engineering, 1(1), 13-37. Retrieved from http://www.dmame.org/index.php/dmame/article/view/1