## 5 Conclusions  
The proposed algorithm based on the Saving Algorithm heuristic approach which aims to solve a Capacitated Vehicle Routing Problem (CVRP) with time and distance constraints was developed. This paper starts with the results of a practical application to a real scenario as reported by [13], where an operational VRP characterized by the presence of both mandatory and not mandatory customers and partially overlapping time windows was 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.  
The implementation of the previous algorithm to the real scenario resulted in an overly complex system setting to manage all constraints and both the running time and usability were not effective. Furthermore, the highly-sophisticated optimization model was difficult to modify and maintain by the company, for example, to implement new additional paths and constraints. Because of these limitations, the presented paper proposes a reasonable reduction in complexity (accepted by the service company) in order to design and implement a multi-stage algorithm to solve the reduced problem. The heuristic approach proposed in this paper, based on the CVRP with time constraints, effectively tackles the complex problem regarding the field service operations of utility networks in a metropolitan area, by exploiting the well-known \"cluster-first-route-second\" problem decomposition technique to minimize the distance within the cluster by generalized assignment problem. In each cluster, a new route is generated by solving the 2-Opt, subdividing the original problem into sub-problems that are faced in a sequence of successive phases. This algorithm minimizes the transportation costs and maximizes the workload according to customer demand within the constraints of time window.  
Advantages, documented by the results provided from the practical application of this algorithm, are:  
* The results demonstrated the efficiency of the approach in tackling a complex optimization task by providing highly pragmatic and realistic time-responsive solutions;
* Flexibility since it can be applied to different routing problems not only to NGDNs;
* Increased \"adaptability\" thanks to an implemented approach of optimizing routes (greater parametrization freedom), taking into account the different constraints, depending on the specific problem under study.  
As a future study, this new approach can be tested and compared with some additional comparative numerical results from other relevant research works in order to show the benefit of adopting this proposed framework. Additionally, future developments could be the real-time connection of operators to this heuristic reactive approach in order to know the state and location of operators and to improve their allocation with respect to service visits inserted in the time window.  
Conceptualization, M.D. and F.T.; Methodology, R.R.; Validation, L.C., R.M. and R.R.; Formal Analysis, M.D. and F.T.; Data Curation, R.R.; Writing-Original Draft Preparation, M.D.; Writing-Review & Editing, M.D.; Supervision, F.T.  
The authors declare no conflict of interest.