Abstract
In this paper, we propose a new model of vehicle routing based on micro services using the principle of selection and composition of paths. In this model each micro service is responsible for a road resource, a step in the driver's journey, implementing a specific objective of the road course. The micro services are deployed in a cloud architecture in multiple instances according to a system of increase in load and fault tolerance. Drivers requests are sent to a proxy micro-service, with abstract structures of road courses represented by oriented graphs. The proxy micro-service is responsible for analyzing the request to determine the driver's profile and its context in the journey in order to select the micro-service responsible for providing information on the most appropriate road resource. The method of meta-heuristic optimization of road courses, used in the proposed approach, is based on the ant colony algorithm, or we will describe an adaptation of this optimization method to propose to the driver, at each stage of the journey, the most optimal resource to exploit.
Highlights
Information Technologies have evolved in a remarkable way the Information Systems (IS) of the companies
We propose the results of a simulation corresponding to an application implemented at the level of the proxy micro-service, which consists in randomly launching HTTP clients, each representing a driver
The approach is based on the ant colony algorithm to improve road traffic management by helping to select the best possible path for a given destination
Summary
Information Technologies have evolved in a remarkable way the Information Systems (IS) of the companies. Micro-services [3], [4], [5] are an architectural style that has emerged in recent years and are best prepared for massively distributed systems. In our case we will describe an adaptation of meta-heuristic optimization method by ant colonies to propose to the driver, at each stage of the journey, the most optimal resource to exploit. The meta-heuristics of ant colony optimization (ACO) was initiated mainly by Marco Dorigo in 1992 [8], for the search of the shortest path in a graph. It was inspired by the behavior of real ants made by Deneubourg and al in 1983 [9]. Several applications of ACO were presented by researchers, including the problem of routing vehicles in 1999 by Bullnheimer and al [11]
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