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.

Abstract
Packing rings into a minimum number of rectangles is an optimization problem which appears naturally in the logistics operations of the tube industry. It encompasses two major difficulties, namely the positioning of rings in rectangles and the recursive packing of rings into other rings. This problem is known as the Recursive Circle Packing Problem (RCPP). We present the first dedicated method for solving RCPP that provides strong dual bounds based on an exact Dantzig-Wolfe reformulation of a nonconvex mixed-integer nonlinear programming formulation. The key idea of this reformulation is to break symmetry on each recursion level by enumerating one-level packings, i.e., packings of circles into other circles, and by dynamically generating packings of circles into rectangles. We use column generation techniques to design a "price-and-verify" algorithm that solves this reformulation to global optimality. Extensive computational experiments on a large test set show that our method not only computes tight dual bounds, but often produces primal solutions better than those computed by heuristics from the literature.

Abstract
Following up the proposal of (Klimentova, Viana, Pedroso and Santos 2019), we consider the usage of a compensation scheme for multi-country kidney exchange programmes to balance out the benefits of cooperation. The novelty of our study is to base the target solution on the Shapley value of the corresponding TU-game, rather than on marginal contributions. We compare the long term performances of the above two fairness concepts by conducting simulations on realistically generated kidney exchange pools. © ECMS Mike Steglich, Christian Mueller, Gaby Neumann, Mathias Walther (Editors).

Abstract

Abstract
To cope with the fierce business competition and the increasing challenges brought with it, manufacturing companies have been demonstrating a growing interest in extending their service business. It is in this context that companies seek servitization strategies, i.e., developing the capabilities to add services to their traditional product offerings, to increase value to the customers and to differentiate themselves from the competition. However, companies pursuing a servitization strategy often lack methods and tools to design new services adapted to their context. Thus, this article seeks to cover this gap through the development of a new service design method, the (S)ervice (D)esign method for (C)ompanies undergoing a (S)ervitization process: SDCS. The development of this method followed Design Science Research (DSR) methodology. This article also presents the application of the SDCS method in a company undergoing a servitization process.

Abstract
Services are the backbone of modern economies and are increasingly supported by technology. Meanwhile, there is an accelerated growth of new technologies that are able to learn from themselves, providing more and more relevant results, i.e. Artificial Intelligence (AI). While there have been significant advances on the capabilities of AI, the impacts of this technology on service provision are still unknown. Conceptual research claims that AI offers a way to augment human capabilities or position it as a threat to human jobs. The objective of this study is to better understand the impact of AI on service, namely by understanding current trends in AI, and how they are, and will, impact service provision. To achieve this, a qualitative study, following Grounded Theory methodology was performed, with ten Artificial Intelligence experts selected from industry and academia.