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In order to foster electric vehicle (EV) adoption rates, the availability of a pervasive and efficient charging network is a crucial requirement. In this paper, we provide a decision support tool for helping policymakers to locate and size EV charging stations. We consider a multi-year planning horizon, taking into account different charging technologies and different time periods (day and night). Accounting for these features, we propose an optimization model that minimizes total investment costs while ensuring a predetermined adequate level of demand coverage. In particular, the setup of charging stations is optimized every year, allowing for an increase in the number of chargers installed at charging stations set up in previous years. We have developed a tailored heuristic algorithm for the resulting problem. We validated our algorithm using case study instances based on the village of Gardone Val Trompia (Italy), the city of Barcelona (Spain), and the country of Luxembourg. Despite the variability in the sizes of the considered instances, our algorithm consistently provided high-quality results in short computational times, when compared to a commercial MILP solver. Produced solutions achieved optimality gaps within 7.5% in less than 90 s, often achieving computational times of less than 5 s.

In this paper, we introduce the Pick and Place Packaging Problem (P4), for optimally scheduling a packaging system with one input conveyor (pick) and one output conveyor (place). We give a formal definition of the underlying dynamic optimization problem. We describe two properties that hold for its solutions and present an efficient row generation approach exploiting these properties. Starting from a basic version of the problem, we introduce two variants where we account for the possibility of holding the grip of items and variable conveyor speed. We extend the proposed exact solution method to these two cases. Then, we present an efficient Iterated Local Search heuristic for the problem and its variants. Numerical results show the effectiveness of our approach.

The planning of metro lines is typically done through a strictly hierarchical approach, which is effective but somewhat inflexible. In this paper, we propose a flexible semiperiodic timetabling strategy using short-turning; thus, allowing trains to turn before reaching the terminal station of a line. Our strategy produces timetables that are periodic with respect to a group of short-turning destinations. This is denoted by the term service pattern. We introduce the service pattern timetabling problem (SPTP). Given a service pattern, the SPTP optimizes the train timetable considering capacity restrictions. The SPTP is modeled as a constraint program. We develop a framework for producing a large set of diverse and high-quality timetables for a metro line. This is achieved by repeatedly solving the SPTP with different patterns. Then we select a restricted list of non-dominated solutions with respect to three objectives: (1) the average passenger waiting time, (2) the maximum load factor achieved by the trains, and (3) the number of transfers induced by short-turning. We evaluate the proposed framework on a number of test instances. Through our computational experiments, we demonstrate the effectiveness of the developed strategy.

Drug distribution performed through hospital pharmacies facilitates public expenditure savings but incurs higher social costs for patients and caregivers. The widespread presence of community pharmacies could support patient access while also improving drug distribution. The implementation of prescriptive data analyses as constrained optimization to achieve specific objectives, could be also applied with good results in the healthcare context. Assuming the perspective of the Italian National Healthcare Service, the present study, built upon existing research in this field, proposes a decision support tool that is able to define which self-administered drugs for chronic diseases should be distributed by community pharmacies, answering to critical challenges in the case of future pandemics and healthcare emergencies, while also providing suggestions for the institutional decision-making process. Moreover, the tool aids in determining the optimal setup of the drug distribution network, comparing centralized (hospital pharmacies) and decentralized (community pharmacies) approaches, as well as their economic and social implications.