Helena Ramalhinho

The dial‐a‐ride problem (DARP) involves designing vehicle routes to fulfill the door‐to‐door transportation requests of users where the goal is to minimize costs while satisfying transportation requests. In this paper, we introduce the rich heterogeneous DARP, which extends the generalized heterogeneous DARP to consider a fleet of buses and taxis, multiple depots, time windows at pickup and delivery locations, maximum ride and waiting times, and the possibility of an accompanying person. Our approach is based on a real service in Barcelona, and we also consider the variation in trip duration based on the time of day and day of the week. A predictive model is developed using machine learning techniques to estimate trip durations accurately. We apply our proposal to the daily door‐to‐door transportation of people with reduced mobility in Barcelona and demonstrate its superiority in terms of costs and quality of service by using the Gurobi optimizer. Additionally, we provide an analysis of the consequences of varying certain features on the costs and quality of service.

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.

A central issue in modern cities is providing inclusive transportation services for people with reduced mobility. In particular, Barcelona is offering a public door-to-door pickup transportation service complementary to the adapted regular public transport. In this work, we apply descriptive analytics to provide a detailed picture of the service by introducing and analyzing a new dataset related to this transportation service. We highlight some of the main problems of the service by processing the data associated with the users and the trips. We also suggest ideas for improving the service. Finally, we propose a trip assignment system based on priorities related to the user or trip characteristics that could improve the quality of the service.

The location of primary public schools in urban areas of developing countries is the focus of this study. In such areas, new schools and modification of the current schools are required, and this process should be developed using rational and broad supporting tools for decision makers, such as optimization models. We propose a realistic coverage location model and a framework to analyze the location of schools. Our approach considers the existing schools and their resizing, the best locations of the new schools that may have different capacities, population coverage, walking distances and budget provisions for building and updating schools. As a case study, we assess the current primary school network in Ciudad Benito Juarez to provide managerial insights. Through the proposed framework, we analyze the current locations of schools and decisions to be made by considering future scenarios in different time periods. The proposed model is quite flexible and easy to adapt to new considerations, allowing it to be applied to regions in developing countries under similar conditions.

The combination of increasing life spans and low birth rates is accelerating the pace at which the share of older adults in the population worldwide is rising. As people age, their autonomy tends to decrease which leads frequently to the need to use support equipment to perform their daily living activities. Governments, at all levels, are establishing programs to enable this population to live with dignity at home, receive more proper care, and to participate in all life’s activities in a joyful and independent way. Within these programs, we can find the Assistive Technology (AT) organizations that makes available to the population assistive technology equipment as wheelchairs or hospital beds. These organizations collect and store donated products for lend them to needed users. The management of these products’ flow, the location of the access centers, and design of the transportation schemes is not straightforward, due to several complexities, such as a highly uncertain demand and offer, budget limitations, and restricted availability of human resources within the organizations, most of which are volunteers. In this paper, we analyze the AT operations, based on a Circular Economy perspective, and we develop tools that can help the managers of these programs to make better logistics decisions. These tools are based on mathematical models and efficient algorithms that have been developed to solve location, inventory, and routing operational problems in the AT organizations. We have been inspired by the social program of the Barcelona City Council, Banc del Moviment, but the tools can be used and extended to other programs around the world.

In this work, we consider the internal logistics processes in the car assembling company SEAT S.A (where SEAT is Sociedad Española de Automóviles de Turismo). We focus on the design of the supplying routes from the warehouse toward the workstations along a production line. The in‐house logistics routing problem (ILRP) can be seen as an extension of the classical vehicle routing problem considering special features such as long‐term fixed routes, unknown orders, backorders, and homogeneous fleet. To solve the ILRP, we propose an integer linear programming (ILP) model and a simulation‐based iterated local search (SimILS) algorithm. We conduct two computational experiments. The first experiment analyzes the methods' performance, and the second experiment compares the SimILS results with the current company's routes, considering real data provided by SEAT S.A. The results show that the SimILS provides the best overall results overcoming both the ILP approach and the current company's solution, leading to a significant improvement in the company's processes.