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This paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic algorithm, it involves two types of populations that are mutually impacted by the fitness calculations. In the solution population, high-quality solutions evolve, representing first-stage decisions evaluated by their performance in the face of the scenario population. The scenario population ultimately generates a diverse set of scenarios regarding their impact on the solutions. This application allows the straightforward implementation of this algorithm, where the user needs only to define the problem-dependent decoding procedure and may adjust the risk profile of the decision-maker. This paper presents the co-evolutionary algorithm and structures the interface. We also present some experiments that validate the impact of relevant features of the application. [ABSTRACT FROM AUTHOR]

This paper describes a case study concerning the teaching of logistics in the Computers and Electrical Engineering degree at FEUP. The logistics course is taken in the last semester of the degree and there are no lectures given by the teachers. All the learning strategy is based upon the autonomous learning capacity of the students, following the widespread citation of Confucius, 'I hear and I forget. I see and I remember. I do and I understand'. The students are organized in groups and their autonomous work is motivated by the presentation that each group leader has to give every other week. A discussion period follows each presentation, and can be used by the teachers to evaluate the involvement of each member of the group and to complement the presentation whenever necessary. All the students are leaders at least once. The leaders are responsible for the group management and must prepare for the 'leaders' meeting', where the presentation session is organized. Assessment is based both on the quality of the presentation and on the technical correctness and completeness in the way subjects are treated and on leadership skills. While the teachers evaluate the two first issues, peers evaluate leadership. [ABSTRACT FROM AUTHOR]

Designing a viable carsharing system in a competitive environment is challenging and often dependent on a myriad of decisions. This paper establishes and presents an integrated conceptual decision-support framework for carsharing systems, encompassing critical decisions that should be made by carsharing organizations and users. To identify the main decisions in a carsharing system, and the inputs and interactions among them, it is crucial to obtain a comprehensive understanding of the current state of the literature as well as the business practices and context. To this aim, a holistic and in-depth literature review is conducted to structure distinct streams of literature and their main findings. Then, we describe some of the key decisions and business practices that are often oversimplified in the literature. Finally, we propose a conceptual decision-support framework that systematizes the interactions between the usually isolated problems in the academic literature and business practices, integrating the perspectives of carsharing organizations and of their users. From the proposed framework, we identify relevant research gaps and ways to bridge them in the future, toward more realistic and applicable research. • We conduct a holistic literature review, structuring its distinct streams. • We propose an integrated decision-support framework for carsharing systems. • The framework is supported by real business practices and academic literature. • Relevant research and managerial insights are derived from this framework. • Main insights relate to pricing, fleet balancing, and demand modeling. [ABSTRACT FROM AUTHOR]

First-mile logistics tackles the movement of products from retailers to a warehouse or distribution centre. This first step towards the end customer has been pushed by large e-commerce platforms forming extensive networks of partners and is critical for fast deliveries. First-mile pickup requires efficient methods different from those developed for last-mile delivery, among other reasons due to the complexity of cargo features and volume — increasing the relevance of advanced packing methods. More importantly, the problem is essentially dynamic and the pickup process, in which the vehicle is initially empty, is much more flexible to react to disruptions arising when the vehicles are en route. We model the static first-mile pickup problem as a vehicle routing problem for a heterogeneous fleet, with time windows and three-dimensional packing constraints. Moreover, we propose an approach to tackle the dynamic problem, in which the routes can be modified to accommodate disruptions — new customers' demands and modified requests of known customers that are arriving while the initially established routes are being covered. We propose three reactive strategies for addressing the disruptions depending on the number of vehicles available, and study their results on a newly generated benchmark for dynamic problems. The results allow quantifying the impact of disruptions depending on the strategy used and can help the logistics companies to define their own strategy, considering the characteristics of their customers and products and the available fleet. • A tool to plan the first-mile collection of parcels by a courier is provided. • Packing and shipping the products are jointly optimized. • Uncertainty caused by disruptions and changes is tackled. • Algorithms for static and dynamic VRPTW with packing constraints are proposed. [ABSTRACT FROM AUTHOR]