Abstract
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Abstract
In online three-dimensional packing problems (3D-PPs), unlike offline problems, items arrive sequentially and require immediate packing decisions without any information about the quantities and sizes of the items to come. Heuristic methods are of great importance in solving online problems to find good solutions in a reasonable amount of time. However, the literature on heuristics for online problems is sparse. As our first contribution, we developed a pool of heuristics applicable to online 3D-PPs with complementary performance on different sets of instances. Computational results showed that in terms of the number of used bins, in all problem instances, at least one of our heuristics had a better or equal performance compared to existing heuristics in the literature. The developed heuristics are also fully applicable to an intermediate class between offline and online problems, referred to in this paper as a specific type of semi-online with full look-ahead, which has several practical applications. In this class, as in offline problems, complete information about all items is known in advance (i.e., full look-ahead); however, due to time or space constraints, as in online problems, items should be packed immediately in the order of their arrival. As our second contribution, we presented an algorithm selection framework, building on developed heuristics and utilizing prior information about items in this specific class of problems. We used supervised machine learning techniques to find the relationship between the features of problem instances and the performance of heuristics and to build a prediction model. The results indicate an 88% accuracy in predicting (identifying) the most promising heuristic(s) for solving any new instance from this class of problems.

Abstract
Irregular strip packing problems are present in a wide variety of industrial sectors, such as the garment, footwear, furniture and metal industry. The goal is to find a layout in which an object will be cut into small pieces with minimum raw-material waste. Once a layout is obtained, it is necessary to determine the path that the cutting tool has to follow to cut the pieces from the layout. In the latter, the goal is to minimize the cutting distance (or time). Although industries frequently use this solution sequence, the trade-off between the packing and the cutting path problems can significantly impact the production cost and productivity. A layout with minimum raw-material waste, obtained through the packing problem resolution, can imply a longer cutting path compared to another layout with more material waste but a shorter cutting path, obtained through an integrated strategy. Layouts with shorter cutting path are worthy of consideration because they may improve the cutting process productivity. In this paper, both problems are solved together using a biobjective matheuristic based on the Biased Random-Key Genetic Algorithm. Our approach uses this algorithm to select a subset of the no-fit polygons edges to feed the mathematical model, which will compute the layout waste and cutting path length. Solving both strip packing and cutting path problems simultaneously allows the decision-maker to analyze the compromise between the material waste and the cutting path distance. As expected, the computational results showed the trade-off’s relevance between these problems and presented a set of solutions for each instance solved. © 2023, Sociedade Brasileira de Pesquisa Operacional. All rights reserved.

Abstract
When dealing with cutting problems, the generation of usable leftovers proved to be a good strategy for decreasing material waste. Focusing on practical applications, the main challenge in the implementation of this strategy is planning the cutting process to produce leftovers with a high probability of future use without complete information about the demand for any ordered items. We addressed the two-dimensional cutting stock with usable leftovers and uncertainty in demand, a complex and relevant problem recurring in companies due to the unpredictable occurrence of customer orders. To deal with this problem, a two-stage formulation that approximates the uncertain demand by a finite set of possible scenarios was proposed. Also, we proposed a matheuristic to support decision-makers by providing good-quality solutions in reduced time. The results obtained from the computational experiments using instances from the literature allowed us to verify the matheuristic performance, demonstrating that it can be an efficient tool if applied to real-life situations.

Abstract
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. © 2004, Taylor & Francis Group, LLC.

Abstract
In this paper an application of constraint logic programming (CLP) to the resolution of nesting problems is presented. Nesting problems are a special case of the cutting and packing problems, in which the pieces generally have non-convex shapes. Because of their combinatorial optimization nature, nesting problems have traditionally been tackled by heuristics and in the recent past by meta-heuristics. When trying to formulate nesting problems as linear programming models, to achieve global optimal solutions, the difficulty of dealing with the disjunction of constraints arises. On the contrary, CLP deals easily with this type of relationships among constraints. A CLP implementation for the nesting problem is described for convex and non-convex shapes. The concept of nofit polygon is used to deal with the geometric constraints inherent to all cutting and packing problems. Computational results are presented. © 2003 Wiley Periodicals, Inc.