Abstract
Tactical Sales and Operations Planning (S&OP) has emerged as an extension of the aggregate production planning, integrating mid-term decisions from procurement, production, distribution, and sales in a single plan. Despite the growing interest in the subject, past synthesizing research has focused more on the qualitative and procedural aspects of the topic rather than on modeling approaches to the problem. This paper conducts a review of the existing decision-making, i.e., optimization, models supporting S&OP. A holistic framework comprising the decisions involved in this planning activity is presented. The reviewed literature is arranged within the framework and grouped around different streams of literature which have been extending the aggregate production planning. Afterwards, the papers are classified according to the modeling approaches employed by past researchers. Finally, based on the characterization of the level of integration of different business functions provided by existing models, the review demonstrates that there are no synthesizing models characterizing the overall S&OP problem and that, even in the more comprehensive approaches, there is potential to include additional decisions that would be the basis for more sophisticated and proactive S&OP programs. We do expect this paper contributes to set the ground for more oriented and structured research in the field.

Abstract
Irregular packing problems (also known as nesting problems) belong to the more general class of cutting and packing problems and consist of allocating a set of irregular and regular pieces to larger rectangular or irregular containers, while minimizing the waste of material or space. These problems combine the combinatorial hardness of cutting and packing problems with the computational difficulty of enforcing the geometric non-overlap and containment constraints. Unsurprisingly, nesting problems have been addressed, both in the scientific literature and in real-world applications, by means of heuristic and metaheuristic techniques. However, more recently a variety of mathematical models has been proposed for nesting problems. These models can be used either to provide optimal solutions for nesting problems or as the basis of heuristic approaches based on them (e.g. matheuristics). In both cases, better solutions are sought, with the natural economic and environmental positive impact. Different modeling options are proposed in the literature. We review these mathematical models under a common notation framework, allowing differences and similarities among them to be highlighted. Some insights on weaknesses and strengths are also provided. By building this structured review of mathematical models for nesting problems, research opportunities in the field are proposed.

Abstract
The two-dimensional level strip packing problem has received little attention from the scientific community. To the best of our knowledge, the most competitive model is the one proposed in 2004 by Lodi et al., where the items are packed by levels. In 2015, an arc flow model addressing the two-dimensional level strip cutting problem was proposed by Mrad. The literature presents some mathematical models, despite not addressing specifically the two-dimensional level strip packing problem, they are efficient and can be adapted to the problem. In this paper, we adapt two mixed integer linear programming models from the literature, rewrite the Mrad's model for the strip packing problem and add well-known valid inequalities to the model proposed by Lodi et al. Computational results were performed on instances from the literature and show that the model put forward by Lodi et al. with valid inequalities outperforms the remaining models with respect to the number of optimal solutions found.

Abstract
The irregular strip packing problem arises in a wide variety of industrial sectors, from garment and footwear to the metal industry, and has a substantial impact in raw-material waste minimization. The goal of this problem is to find a layout for a large object to be cut into smaller pieces. What differentiates this problem from all the other cutting and packing problems, and is its primary source of complexity, is the irregular (non-rectangular) shape of the small pieces. However, in practical applications, after a layout has been determined, a second problem arises: finding the path that the cutting tool has to follow to actually cut the pieces, as previously planned. This second problem is known as the cutting path determination problem. Although the solution of the first problem strongly influences the resolution of the second one, only a few studies are dealing with cutting/packing and cutting path determination together, and, to the best of the authors' knowledge, none of them considers the irregular strip packing problem. In this paper, we propose the first mathematical programming model that integrates the irregular strip packing and the cutting path determination problems. Computational experiments were run to show the correctness of the proposed model and the advantage of tackling the two problems together. Two variants of the cutting path determination problem were considered, the fixed vertex and the free cut models. The strengths and drawbacks of these two variants are also established through computational experiments. Overall, the computational results show that the integration of these problems is advantageous, even if only small instances could be solved to optimality, given that solving to optimality the integrated is at least as difficult as solving each one of the other problems individually. As future research, it should be highlighted that the proposed integrated model is a solid basis for the development of matheuristics aiming at tackling real-world size problems.

Abstract
Physical properties of materials are seldom studied in the context of packing problems. In this work we study the behavior of semifluids: materials with particular characteristics that share properties both with solids and with fluids. We describe the importance of some specific semifluids in an industrial context, and propose methods for tackling the problem of packing them, taking into account several practical requirements and physical constraints. The problem dealt with here can be reduced to a variant of two-dimensional knapsack problem with guillotine cuts, where items are splittable in one of the dimensions and the number of cuts is not limited. Although the focus of this paper is on the computation of practical solutions, it also uncovers interesting mathematical properties of this problem, which differentiate it from other packing problems. A thorough computational experiment is used to assess the quality of the approaches proposed, which is analyzed and compared to relevant methods from the literature.

Abstract
While the objectives of forest management vary widely and include the protection of resources in protected forests and nature reserves, the primary objective has often been the production of wood products. However, even in this case, forests play a key role in the conservation of living resources. Constraining the areas of clearcuts contributes to this conservation, but if it is too restrictive, a dispersion of small clearcuts across the forest might occur, and forest fragmentation might be a serious ecological problem. Forest fragmentation leads to habitat loss, not only because the forest area is reduced, but also because the core area of the habitats and the connectivity between them decreases. This study presents a Monte Carlo tree search method to solve a bi-objective harvest scheduling problem with constraints on the clearcut area, total habitat area and total core area inside habitats. The two objectives are the maximization of both the net present value and the probability of connectivity index. The method is presented as an approach to assist the decision maker in estimating efficient alternative solutions and the corresponding trade-offs. This approach was tested with instances for forests ranging from some dozens to over a thousand stands and temporal horizons from three to eight periods. In general, multi-objective Monte Carlo tree search was able to find several efficient alternative solutions in a reasonable time, even for medium and large instances.