Key features of modern healthcare services in developed countries, in a general context characterized by strict cost control and aging population, are flexibility in access to care for different types of patient flows (e.g. inpatients, ED patients and outpatients) and capability of adapting to quantitative service demand change, commonly posed by outpatients. An expensive resource, such as a CT scanner or an MRI machine, is often shared by diverse patient flows and this single server has to assure satisfactory performances (in terms of waiting time) to both the random and the planned service demand component, implementing appropriate buffering and priority rules. For outpatients, requesting access to an appointment service in general, waiting time is represented by waiting time for the appointment day and waiting time experienced at the facility. The governing rules determining these performance results can be treated as separate problems and, in the present paper, the first kind of wait, commonly related to the existence of long waiting lists, is considered. A simple general analytical model, adopting statistical considerations, is proposed and, successively, applications in a CT scan facility in a public hospital, supported by simulation, are illustrated.

Human labor still plays a crucial role in many work contexts. However, increases in production rate require increases in human workloads. The authors propose two integer programming models aiming at finding both optimal break and job rotation schedules in work-environments characterized by low load manual tasks with high frequency of repetition. In such a work environment a major risk consists of work-related musculoskeletal disorders. Workload risk and acceptability are evaluated by the OCRA index (ISO 11228-3:2007). Models proposed are applied to an assembly line from the automotive industry. Results obtained revealed the effectiveness of the models as they proved to be adequate tools to jointly address the reduction and balancing of human workloads among employees, which are consistent with acceptable workload limits and required production levels.

The concept of ‘Sustainability’ is increasingly used to describe a paradigm upon which future policies must be based. In the view of a urban sustainable development, a strategic role in the governance of the smart City is played by waste integrated management systems (IWMS). The raising complexity of a IWMS relies on the high number of design and management variables and relationships related to collection, treatments and disposal phases. Waste management practices can affect greenhouse gas emission by influencing energy consumption, methane generation, carbon sequestration and non-energy related manufacturing emission. A decision support system can be modelled aiming to evaluate and minimize the carbon footprint of a IWMS. The model proposed goes beyond the existing technical and organizational solutions outlining the different options in a much broader view concerning both waste collection and treatments.