Abstract
A useful routing system should have the capability of supporting the driver effectively in deciding on an 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.
Highlights
The vehicle routing problem (VRP) has played a very important role in the distribution and supply chain management, in addition to many other areas
The hybrid neuro-fuzzy system briefly presented in this paper was successfully applied in designing an intelligent decision support system for route selection in uncertainty conditions
The research conducted proves that fuzzy neural networks are a very effective and useful instrument for the implementation of intelligent decision support systems for route selection
Summary
The vehicle routing problem (VRP) has played a very important role in the distribution and supply chain management, in addition to many other areas. One objective of such a dynamic route guidance system is to balance the level of service on all major network links so as to increase the efficiency, speed, safety and quality of travel (e.g. to minimize travel time) This system could prove to be extremely useful when transportation needs to be carried out under conditions, when a traffic accident has taken place or when work is being carried out on damaged roads. In the DRG system for selecting a road route, which is presented in this paper, the experiential knowledge of drivers who run transport vehicles in transport units is accumulated in a neuro-fuzzy network which has the capacity to generalize solutions. The criteria by which the transport manager selects and makes a decision regarding which route the vehicle should use for the task are: Type of road surface, Travel distance, Travel time, Route capacity, Traffic capacity, Road capacity and The existence of alternative roads along the length of the route. By developing a fuzzy system it is possible to transform the deployment strategy for vehicles on specific routes into an automatic control strategy
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
