a dynamic competition environment, many companies are concerned to increase logistics capabilities, especially due to the costs of allocating clients to routes and the distribution expenses included then in the total cost and adopt VRP for Product-Service-Systems (PSS) [5]. Furthermore, in the recent literature about VRP, a new trend has been gaining attention concerning the area of Internet of Things (IoT). The IoT can optimize delivery routes due to the implementation of sensors. Devices have a better connection to the internet and therefore, they can be managed at any time or place [6]. Specifically, [PERSON] [7; 8] analyze the paradigm of machine to machine driven IoT, which aims to provide contextual services and a more energy efficient resource management approach. This provides the opportunity to adopt connecting devices, which improve the flexibility of the control and decision-making processes and enable \"a mobile node's self-optimization and self-adaptation function\" [6]. Our attention is focused on the aforementioned grouping problems when applied to natural gas distribution networks (NGDNs), which deliver an energy source growing in popularity due to its low environmental impact compared to oil and coal. For these reasons, planning and optimization of NGDNs factors are critical and they are of great interest in this field of research, especially concerning metropolitan areas. With this in mind, optimization methods and operation research techniques could reduce network logistic outlays as well as design, flow, operation and expansion costs. Managing NGDNs includes making a variety of decisions ranging from human resources and material resources to facilities, infrastructures, and carriers. Common problems that affect NGDNs are:  
* Designing of new NGDN or expansion of existing ones [9];
* Routing a fleet of vehicles from a depot to service a set of customers that are geographically dispersed [10];
* Determining the pipelines in the network, the compressor stations and their capacities [11];
* Minimizing the transportation costs and maximizing the transported volume [12];
* Optimizing gas network inventories in the face of system uncertainties [13];
* Synchronizing the minimum risk loss and total cost of natural gas pipeline networks [14]. Despite the numerous papers available on VRP, only a few study and analyze the problems occurring in capillary distribution operations such as those found in a metropolitan area. However, there are some exceptions such as [15], where a set of field service operations are considered in order to manage the assets of a NGDN and maintain high levels of customer satisfaction and gas provider profitability, particularly regarding activation, deactivation and management of utility meters with respect to time, distance and capacity, and where an operational VRP characterized by the presence of both mandatory and not mandatory customers, and partially overlapping time windows were presented. The authors adopted a VRP with time windows (VRP-TW) combined with Simulated Annealing (SA), which allows for real constraints optimization in reasonable computational time and represents an alternative approach with respect to existing contributions to the considered subject. This algorithm interface operates as a decision support system tool that allows decision makers to both visualize and to modify the routes while allowing interesting optimization capabilities. The implementation of the established algorithm to a real case study resulted in a far more complex system, where both running time and usability were not effective, resulting in a pilot approach which did not allow the company to adapt the changes needed or take into consideration the additional paths and real time constraints. Accordingly, this research proposes a different algorithm which takes into account these drawbacks and suggests a new solution, that is, simplifying the problem by reducing the number of constraints and varying some variables. Hence, a true comparison of the two studies is not possible.