# iRoute--An Adaptive Route Planning Solution for Commercial Vehicle Fleets
## 1 Introduction  
The advancement of navigation technology has created a highly competitive and complex environment for the transportation industry. Companies that adopt new technologies increase their chance to be market leaders by optimizing internal processes. The efficient management of orders, pick-up, and delivery of goods is the key to generating profit and staying competitive in the long term. While the additional cost of the distribution of goods is 70 percent for the food and beverage industry [1], the continuous growth of the population living in the urban area has increased the volume of goods delivered to customers [2]. Furthermore, the density of the population in the urban area has generated traffic problems, and urban logistics have experienced significant changes in recent years. Thus, the number of customers ordering online has increased and they are putting pressure on shipping companies because they want fast and cheap delivery. In order not to delay the delivery of goods, manufacturers adhere to the process of distributed manufacturing by moving production sites as close as possible to the location of demand [3]. Companies thattransport small packages minimize the cost by keeping up the routes of the drivers and want to build customer relationships by using the same agents to visit the same customers at about the same time every day [4], which is why it should come as no surprise that the vehicle routing problem (VRP) is a popular optimization problem when it comes to finding the best possible routes (several types of routing problems have been extended from the VRP). Although there are many route optimization software options available on the market, each has its limitations, and choosing the right one is critical to a company's long-term success and growth. The adaptive routing algorithm makes decisions dynamically based on the parameters that are constantly changing, and adaptation to a set of data and new constraints is done in real time to generate the best route. [PERSON] proposes an adaptive quantum genetic algorithm based on binary coding for assigning routes to specific vehicles [5]. The self-adaptive angular acceleration algorithm is used in order to have a small number of iterations. When the threshold is exceeded, the evolution ends and can determine the optimal solution to the problem. [PERSON] et al. present a new two-level VRP model for electric vehicles based on an adaptive local search algorithm [6]. By simulating the freight transportation process with real-time traffic conditions, the algorithm satisfies the VRP constraints. [PERSON] et al. present an adaptive genetic algorithm for minimizing the total transportation cost with a homogeneous fleet of vehicles and determining for a given route the loading arrangement of the items transported on the vehicle loading surface based on the two-dimensional orthogonal function [7]. [PERSON] et al. propose an algorithm using particle swarm optimization for route planning [8]. The algorithm increases the weight in the initial stage and gradually reduces the weight in the final stage by adaptively adjusting the weight, which is combined with the historical search information. Vehicle fleet management software is an essential tool for transportation companies looking to optimize their costs, but not all software solutions are suitable because each solution has different limitations. Before the development of the software solution, we conducted a study to determine the necessary characteristics based on the identified constraints, thus defining a set of requirements that are considered most relevant by potential users who are working in the transportation business [9]. One of the most common constraints is the limited number of stops that can be included in the optimization algorithm, which makes it difficult to manage complex delivery routes. Other solutions are limited by the volume of vehicles that a dispatcher can allocate to a route optimization plan by having difficulty in allocating single or multiple delivery windows.