Automatic assessment of object oriented programming assignments with unit testing in Python and a real case assignment

In this paper, we focus on developing automatic assessment (AA) for a topic that has some difficulties in its practical assessment: object oriented programming (OOP). For evaluating that the OOP principles have been correctly applied to a real application, we use unit testing. In this paper, we focus on prioritizing that the students understand and apply correctly complex OOP principles and that they design properly the classes (including their relationships). In addition, we focus on the Python programming language rather than the typical previous works' focus in this area. Thus, we present a real case study of a practical assignment, in which the students have to implement characters for a video game. This assignment has the particularities and advantages that it is incremental and that it applies all four OOP principles within a single assignment. We also present its solution with the UML class diagram description. Furthermore, we provide unit testing for this case study and give general advice for generalizing the unit tests to other real case scenarios. Finally, we corroborate the effectiveness of our approach with positive student evaluation

Smart and sustainable scheduling of charging events for electric buses

This paper presents a framework for the efficient management of renewable energies to charge a fleet of electric buses (eBuses). Our framework starts with the prediction of clean energy time windows, i.e., periods of time when the production of clean energy exceeds the demand of the country. Then, the optimization phase schedules charging events to reduce the use of non-clean energy to recharge eBuses while passengers are embarking or disembarking. The proposed framework is capable of overcoming the unstable and chaotic nature of wind power generation to operate the fleet without perturbing the quality of service. Our extensive empirical validation with real instances from Ireland suggests that our solutions can significantly reduce non-clean energy consumed on large data setsThis work received funding from the Sustainable Energy Authority of Ireland (SEAI) Research, Development and Demonstration (RDD) 2019 programme under the grant number 19/ RDD/51

A hybrid metaheuristic with learning for a real supply chain scheduling problem

In recent decades, research on supply chain management (SCM) has enabled companies to improve their environmental, social, and economic performance. This paper presents an industrial application of logistics that can be classified as an inventory-route problem. The problem consists of assigning orders to the available warehouses. The orders are composed of items that must be loaded within a week. The warehouses provide an inventory of the number of items available for each day of the week, so the objective is to minimize the total transportation costs and the costs of producing extra stock to satisfy the weekly demand. To solve this problem a formal mathematical model is proposed. Then a hybrid approach that involves two metaheuristics: a greedy randomized adaptive search procedure (GRASP) and a genetic algorithm (GA) is proposed. Additionally, a meta-learning tuning method is incorporated into our hybridized approach, which yields better results but with a longer computation time. Thus, the trade-off of using it is analyzed. An extensive evaluation was carried out over realistic instances provided by an industrial partner. The proposed technique was evaluated and compared with several complete and incomplete solvers from the state of the art (CP Optimizer, Yuck, OR-Tools, etc.). The results showed that our hybrid metaheuristic outperformed the behavior of these well-known solvers, mainly in large-scale instances (2000 orders per week). This hybrid algorithm provides the company with a powerful tool to solve its supply chain management problem, delivering significant economic benefits every week.The authors gratefully acknowledge the financial support of the European Social Fund (Investing In Your Future), the Spanish Ministry of Science (project PID2021-125919NB-I00), and valgrAI - Valencian Graduate School and Research Network of Artificial Intelligence and the Generalitat Valenciana, Spain, and co-funded by the European Union. The authors also thank the industrial partner Logifruit for its support in the problem specification and the permission to generate randomized data for evaluating the proposed algorithm

Cutting uncertain stock and vehicle routing in a sustainability forestry harvesting problem

Sustainable forest management is concerned with the management of forests according to the principles of sustainable development. As a contribution to the field, this paper combines the Vehicle Routing Problem (VRP) (in which the vehicles are harvesters) with the Multiple Stock Size Cutting Stock Problem under uncertainty (in which the stock is logs). We present an Integer Linear Program that dynamically combines the cutting of the uncertain stock with vehicle routing, and uses it to address real-life problems. In experiments on real data from the forestry harvesting industry, we show that it outperforms a commonly used metaheuristic algorithmThis publication has emanated from research conducted with the fnancial support of Science Foundation Ireland under Grant Number 12/RC/2289-P2 which is co-funded under the European Regional Development Fund. We would also like to acknowledge the support of the Science Foundation Ireland CONFIRM Centre for Smart Manufacturing, Research Code 16/RC/391