In this survey, the issues of urban routing are analyzed, and critical considerations for smart and cost-effective delivery services are highlighted. Smart cities require intelligent services and solutions to address their routing issues. This article gives a brief description of current services that either apply classical methods or services that employ machine learning approaches. Furthermore, a comparison of the most promising research options in regard to VRP is provided. Finally, an initial design of a holistic scheme that would optimally combine several tools and approaches to serve the needs of different users with regard to the VRP is presented.

This work proposes an effective solution to the Vehicle Routing Problem, taking into account all phases of the delivery process. When compared to real-world data, the findings are encouraging and demonstrate the value of Machine Learning algorithms incorporated into the process. Several algorithms were combined along with a modified Hopfield network to deliver the optimal solution to a multiobjective issue on a platform capable of monitoring the various phases of the process. Additionally, a system providing viable insights and analytics in regard to the orders was developed. The results reveal a maximum distance saving of 25% and a maximum overall delivery time saving of 14%.

Safety-critical systems embedding concurrent on-line testing techniques are vulnerable to design issues causing the degradation of totally self-checking (TSC) property, which is proved to be fatal for further operations (e.g., space electronics, medical devices). In addition to the exploration of the degradation of TSC property over time, a concurrent on-line testing architecture is offered that adjusts the input activity, addressing the absence of input values or the low frequency of their appearance (e.g., during sleep mode). During concurrent on-line testing, the inputs of the circuit under test (CUT) are, at the same time, its test vectors. This architecture tolerates possible degradation of the terms that contribute to the calculation of the totally self-checking goal (TSCG$\left(t\right)$). An adaptive built-in self-test (BIST) unit is proposed that dynamically applies test vector subsets when permitted, based on the frequency of appearance of the input values. The clustering of the inputs is based on the k-means algorithm and, in combination with the ordering of the test vectors to minimize the subsets, results in partitioning the test procedure in a significantly shorter time. The comparison to other solutions used for concurrent on-line testing showed that the proposed adaptive BIST has significant advantages. It can cope with rare occurrences, or even no occurrence, of input values by enabling the BIST mechanism appropriately. The results showed that it may increase the TSCG$\left(t\right)$ up to almost 90% when applied during a low-power mode and present better concurrent test latency (CTL) when assumptions regarding the availability of all input values and the probability of occurrence are not realistic.