## 5 Conclusions and Further Research  
The results obtained from the MOLNS algorithm were compared to both the best-published solutions in [PERSON]'s dataset, as well as to those of other multiobjective algorithms, providing us significant insights about this method. Based on the conducted research, as it becomes clear, no algorithm can obtain all Pareto optimal solutions in each instance in [PERSON]'s dataset. Instead, the results have been conjunctly obtained from multiple algorithms since the VRPTW first appeared. The results of the proposed MOLNS algorithm are near-optimal, with only small deviations from the optimal, and even include three new non-dominated solutions. The computational experiments indicate that the results are efficient and are obtained in a very realistic time. As both efficiency and speed are critical factors when implementing an algorithm in software solutions, the proposed MOLNS algorithm can be examined for implementation in such software, and this will in fact be the next step of our research. In real-life scenarios, performing an initial plan of routes and deliveries does not necessarily fulfill the purpose of distribution services. Unforeseen events and traffic congestion, in many cases and especially in the urban environment, cause delays and generate the need for rescheduling, in order to ensure that all deliveries are executed and served on-time. The presented algorithm can be easily adapted and applied in dynamic routing cases to calculate new schedules of deliveries efficiently and quickly. The implementation of the proposed algorithm in a software solution for logistics operations, therefore, needs to be further researched. Additionally, the potential reinforcement of the algorithm to solve cases that require the dynamic rescheduling of deliveries should also be studied. Recent advances in technology, such as traffic management sensors, real-time traffic data and the Internet of things technology, can provide data to enhance such rescheduling procedures and deal with the dynamic nature of the real-life problems. In addition, the algorithm could be further enhanced by applying multicriteria decision making approaches such as the one presented by [PERSON] et al. (2016) to optimize its function in accordance with different use-case scenarios. Finally, provided the aforementioned technologies are successfully combined with the proposed algorithm, in the next step of our research, an advanced software solution that can support the distribution processes of logistics companies, in the urban environment, while, at the same time, focusing on the fulfillment of time-window restrictions, will be created. Conceptualization, G.D.K. and S.P.G. ; methodology, G.D.K. and E.P.K. ; software, G.D.K. and E.P.K. ; validation, G.D.K, S.P.G and E.P.K. ; formal analysis, G.D.K. ; investigation, G.D.K. and E.P.K. ; resources, S.P.G., G.A.P. and I.P.T. ; data curation, G.D.K. ; writing--original draft preparation, G.D.K and S.P.G. ; writing--review and editing, G.D.K, S.P.G, E.P.K. and I.P.T. ; visualization, G.D.K and G.A.P. ; supervision, S.P.G., I.P.T. and G.A.P. ; project administration, S.P.G. and I.P.T. ; and funding acquisition, S.P.G., I.P.T. and G.A.P. All authors have read and agreed to the published version of the manuscript. The present work was co-funded by the European Union and Greek national funds through the Operational Program \"Competitiveness, Entrepreneurship and Innovation\" (EPAnEK), under the call \"RESEARCH-CREATEINNOVATE\" (Project Code: T1 EDK-00527 and Acronym: SMARTRANS). In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments). The authors declare no conflict of interest. * [PERSON] et al. (2010) [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows. _Comput. Oper. Res._**2010**, _37_, 724-737.