The way citizens interact with cities affects overall life quality. Their participation in social decisions is of paramount importance for helping on public decisions that affect governance, regulation and education. This interaction has the potential of being boosted within the scope of smart and digital cities, especially by recent advances in blockchain technology. This work introduces insights about how smart cities’ concepts and innovative technologies can help society to face daily challenges for improving citizens’ awareness. Digital technologies are able to drive social and economic development by employing Information and Communication Technology (ICT) to promote innovation. In this context, e-governance, in conjunction with disruptive concepts such as blockchain, is showing up as a fundamental tool for a decentralized democracy. This study reviews, discusses, raises open points and presents suggestions towards an efficient, transparent and sustainable use of technology, applied to future cities.

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Distribution planning is crucial for most companies since goods are rarely produced and consumed at the same place. Distribution costs, in addition, can be an important component of the final cost of the goods. In this paper, we study a VRP variant inspired on a real case of a large distribution company. In particular, we consider a VRP with a heterogeneous fleet of vehicles that are allowed to perform multiple trips. The problem also includes docking constraints in which some vehicles are unable to serve some particular customers. Given the combinatorial nature and the size of the problem, which discard the use of efficient exact methods for its resolution, a novel heuristic algorithm is proposed. The proposed algorithm, called GILS-VND, combines Iterated Local Search (ILS), Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Descent (VND) procedures. Our method obtains better solutions than other approaches found in the related literature, and improves the solutions used by the company leading to * Corresponding author * * Principal corresponding author

BackgroundBlood samples are usually collected daily from different collection points, such hospitals and health centers, and transported to a core laboratory for testing. This paper presents a project to improve the collection routes of two of the largest clinical laboratories in Spain. These routes must be designed in a cost-efficient manner while satisfying two important constraints: (i) two-hour time windows between collection and delivery, and (ii) vehicle capacity.MethodsA heuristic method based on a genetic algorithm has been designed to solve the problem of blood sample collection. The user enters the following information for each collection point: postal address, average collecting time, and average demand (in thermal containers). After implementing the algorithm using C programming, this is run and, in few seconds, it obtains optimal (or near-optimal) collection routes that specify the collection sequence for each vehicle. Different scenarios using various types of vehicles have been considered. Unless new collection points are added or problem parameters are changed substantially, routes need to be designed only once.ResultsThe two laboratories in this study previously planned routes manually for 43 and 74 collection points, respectively. These routes were covered by an external carrier company. With the implementation of this algorithm, the number of routes could be reduced from ten to seven in one laboratory and from twelve to nine in the other, which represents significant annual savings in transportation costs.ConclusionsThe algorithm presented can be easily implemented in other laboratories that face this type of problem, and it is particularly interesting and useful as the number of collection points increases. The method designs blood collection routes with reduced costs that meet the time and capacity constraints of the problem.