Abstract

Abstract
Lot-sizing and scheduling in a job shop environment is a fundamental problem that appears in many industrial settings. The problem is very complex, and solutions are often needed fast. Although many solution methods have been proposed, with increasingly better results, their computational times are not suitable for decision-makers who want solutions instantly. Therefore, we propose a novel greedy heuristic to efficiently generate production plans and schedules of good quality. The main innovation of our approach represents the incorporation of a simulation-based technique, which directly generates schedules while simultaneously determining lot sizes. By utilising priority rules, this unique feature enables us to address the complexity of job shop scheduling environments and ensures the feasibility of the resulting schedules. Using a selection of well-known rules from the literature, experiments on a variety of shop configurations and complexities showed that the proposed heuristic is able to obtain solutions with an average gap to Cplex of 4.12%. To further improve the proposed heuristic, a cooperative coevolutionary genetic programming-based hyper-heuristic has been developed. The average gap to Cplex was reduced up to 1.92%. These solutions are generated in a small fraction of a second, regardless of the size of the instance.

Abstract
Vicarious experiences in tourism possess significant marketing implications. While numerous studies have explored how various forms of vicarious experiences can impact an individual, the role of different time spans as a key factor determining the extent of said impact has been neglected in prior research. To address this gap, the present study thus bridges environmental psychology with the context of tourism and applies the theory of mental representations. An experiment (n = 359) was designed to examine differences in select mental representation dimensions (cognitive, affective, conative, and sensorial) among male and female Chinese college students who have zero/medium/maximum durations of constant vicarious experiences related to European destinations in their home environment. The results indicate that the medium duration of constant vicarious experiences leads to the most positive changes in cognitive and conative dimensions, while the longest constant vicarious experiences produce desirable affective dimension outcomes. Moreover, male college students seem to be more susceptible to the influences of such constant vicarious experiences.

Abstract
The trend for an increasingly ubiquitous and cyber-physical world has been leveraging the use and importance of microcontrollers (mu C) to unprecedented levels. Therefore, microcontroller programming (mu CP) becomes a paramount skill for electrical and computer engineering students. However, mu CP poses significant challenges for undergraduate students, given the need to master low-level programming languages and several algorithmic strategies that are not usual in generic programming. Moreover, mu CP can be time-consuming and complex even when using high-level languages. This article samples the current state of mu CP education in Portugal and unveils the potential support of natural language processing (NLP) tools (such as chatGPT). Our analysis of mu CP curricular units from seven representative Portuguese engineering schools highlights a predominant use of AVR 8-bit mu C and project-based learning. While NLP tools emerge as strong candidates as students' mu C companion, their application and impact on the learning process and outcomes deserve to be understood. This study compares the most prominent NLP tools, analyzing their benefits and drawbacks for mu CP education, building on both hands-on tests and literature reviews. By providing automatic code generation and explanation of concepts, NLP tools can assist students in their learning process, allowing them to focus on software design and real-world tasks that the mu C is designed to handle, rather than on low-level coding. We also analyzed the specific impact of chatGTP in the context of a mu CP course at ISEP, confirming most of our expectations, but with a few curiosities. Overall, this work establishes the foundations for future research on the effective integration of NLP tools in mu CP courses.

Abstract

Abstract
Wind power is becoming an important source of electrical energy production. In an onshore wind farm (WF), the electrical energy is collected at a substation from different wind turbines through electrical cables deployed over ground ditches. This work considers the WF layout design assuming that the substation location and all wind turbine locations are given, and a set of electrical cable types is available. The WF layout problem, taking into account its lifetime and technical constraints, involves selecting the cables to interconnect all wind turbines to the substation and the supporting ditches to minimize the initial investment cost plus the cost of the electrical energy that is lost on the cables over the lifetime of the WF. It is assumed that each ditch can deploy multiple cables, turning this problem into a more complex variant of previously addressed WF layout problems. This variant turns the problem best fitting to the real case and leads to substantial gains in the total cost of the solutions. The problem is defined as an integer linear programming model, which is then strengthened with different sets of valid inequalities. The models are tested with four WFs with up to 115 wind turbines. The computational experiments show that the optimal solutions can be computed with the proposed models for almost all cases. The largest WF was not solved to optimality, but the final relative gaps are small.