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.

We present new multiobjective metaheuristics for solving real-world crew scheduling problems in public bus transport companies. Since the crews of these companies are drivers, we will designate the problem as bus-driver scheduling. Crew scheduling problems are well known, and several mathematical programming-based techniques have been proposed to solve them, in particular, using the single-objective set-covering formulation. However, in practice, there exists the need to consider multiple objectives, some of them in conflict with each other; for example, the cost and service quality, implying also that alternative solution methods have to be developed. We propose multiobjective metaheuristics based on the tabu search and genetic algorithms. These metaheuristics also present some innovation features related with the structure of the crew scheduling problem that guide the search efficiently and enable them to find good solutions. Some of these new features can also be applied to the development of heuristics to other combinatorial optimization problems. A summary of computational results with real-data problems is presented. The methods have been successfully incorporated in the GIST Planning Transportation Systems and are actually used by several companies.