Textile recycling is crucial for environmental sustainability, but accurately sorting post-consumer garments by material composition remains a challenge, especially for multifabric garments. Current methods struggle to identify and separate regions with materials distinct from the main body fabric, such as prints, collars, or waistbands and rely on manual pre-sorting. This paper proposes a system that combines near-infrared (NIR) spectroscopy and camera-based computer vision for automated material detection in textiles. The proposed method leverages a pre-trained Mask R-CNN model to perform fine-grained garment segmentation. Each segment represents a region within a garment with a potentially unique material composition. NIR spectroscopy measurements are then taken on strategically chosen points within each segment for actual material characterization. Our prototype system demonstrates the potential for this combined approach to improve the accuracy and efficiency of textile sorting for recycling applications.

A frequently studied problem in the context of digital marketing for online social networks is the influence maximization problem that seeks for an initial seed set of influencers to trigger an information propagation cascade (in terms of active message forwarders) of expected maximum impact. Previously studied problems typically neglect that the probability that individuals passively view content without forwarding it is much higher than the probability that they forward content. Considering passive viewing enables us to maximize more natural (social media) marketing metrics, including (a) the expected organic reach, (b) the expected number of total impressions, or (c) the expected patronage, all of which are investigated in this paper for the first time in the context of influence maximization. We propose mathematical models to maximize these objectives, whereby the model for variant (c) includes individual’s resistances and uses a multinomial logit model to model customer behavior. We also show that these models can be easily adapted to a competitive setting in which the seed set of a competitor is known. In a computational study based on network graphs from Twitter (now X) and from the literature, we show that one can increase the expected patronage, organic reach, and number of total impressions by 36% on average (and up to 13 times in particular cases) compared with seed sets obtained from the classical maximization of message-forwarding users. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work was supported by the Federal Ministry of Education, Science and Research of Austria and by the Austrian Agency for International Mobility and Cooperation in Education, Science and Research [Reference ICM-2019-13384]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0047 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0047 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

We study the problem of optimizing assortment decisions in the presence of product-specific costs when customers choose according to a multinomial logit model. This problem is NP-hard, and approximate solutions methods have been proposed in the literature to obtain both lower and upper bounds in a tractable manner. We propose the first exact solution method for this problem and show that provably optimal assortments of instances with up to 1,000 products can be found, on average, in about 2/10 of a second. In particular, we propose a bounding procedure to enhance an approximation method originally proposed by Feldman and Topaloglu and provide tight lower and upper bounds at a fraction of a second. We show how these bounds can be used to effectively identify an optimal assortment. We also describe how to adapt our approach to handle cardinality or space/resource capacity constraints on the assortment as well as assortment optimization under a mixed-multinomial logit model. In both cases, our solution method provides significant computational boosts compared with exact methods from the literature.

Object pose estimation enables robots to understand and interact with their environments. Training with synthetic data is necessary in order to adapt to novel situations. Unfortunately, pose estimation under domain shift, i.e., training on synthetic data and testing in the real world, is challenging. Deep learning-based approaches currently perform best when using encoder-decoder networks but typically do not generalize to new scenarios with different scene characteristics. We argue that patch-based approaches, instead of encoder-decoder networks, are more suited for synthetic-to-real transfer because local to global object information is better represented. To that end, we present a novel approach based on a specialized feature pyramid network to compute multi-scale features for creating pose hypotheses on different feature map resolutions in parallel. Our single-shot pose estimation approach is evaluated on multiple standard datasets and outperforms the state of the art by up to ∼35 %. We also perform grasping experiments in the real world to demonstrate the advantage of using synthetic data to generalize to novel environments.

Object models are highly useful for robots as they enable tasks such as detection, pose estimation and manipulation. However, models are not always easily available, especially in real-world domains of operation such as peoples’ homes. This work presents a pipeline to generate high-quality object reconstructions from human in-hand manipulation to alleviate the necessity of specialised or expensive hardware. Missing data, due to occlusion or unseen sides, is explicitly handled by incorporating shape completion. We demonstrate the usability of the reconstructions by applying a model-based as well as a CNN-based object pose estimator that is trained on synthetic images by employing state-of-the-art texture synthesis. Using our pipeline to cheaply generate object models and synthetic RGB images for training, we achieve competitive performance compared to baselines that require an elaborate set-up to construct models or large amounts of annotated data. Object grasping is also enabled by learning with the reconstructions in simulation, then executing with a real robot. These evaluations show that our reconstructions are comparable to those made under near-perfect conditions and enable 6D object pose estimation as well as real-world grasping.

Special issue on network analytics

In this article we consider the network design problem with relays (NDPR), which gives answers to some important strategic design questions in telecommunication network design. Given a family of origin-destination pairs and a set of existing links these questions are as follows: (1) What are the optimal locations for signal regeneration devices (relays) and how many of them are needed? (2) Could the available infrastructure be enhanced by installing additional links in order to reduce the travel distance and therefore reduce the number of necessary relays? In contrast to previous work on the NDPR, which mainly focused on heuristic approaches, we discuss exact methods based on different mixed-integer linear programming formulations for the problem. We develop branch-and-price and branch-price-and-cut algorithms that build upon models with an exponential number of variables (and constraints). In an extensive computational study, we analyze the performance of these approaches for instances that reflect different real-world settings. Finally, we also point out the relevance of the NDPR in the context of electric mobility.

We present a branch-and-bound (B&B) framework for the asymmetric prize-collecting Steiner tree problem (APCSTP). Several well-known network design problems can be transformed to the APCSTP, including the Steiner tree problem (STP), prize-collecting Steiner tree problem (PCSTP), maximum-weight connected subgraph problem (MWCS), and node-weighted Steiner tree problem (NWSTP). The main component of our framework is a new dual ascent algorithm for the rooted APCSTP, which generalizes Wong’s dual ascent algorithm for the Steiner arborescence problem. The lower bounds and dual information obtained from the algorithm are exploited within powerful bound-based reduction tests and for guiding primal heuristics. The framework is complemented by additional alternative-based reduction tests. Extensive computational results on benchmark instances for the PCSTP, MWCS, and NWSTP indicate the framework’s effectiveness, as most instances from literature are solved to optimality within seconds, including most of the (previously unsolved) largest instances from the recent DIMACS Challenge on Steiner trees. Moreover, results on new asymmetric instances for the APCSTP are reported. Since the addressed network design problems are frequently used for modeling various real-world applications (e.g., in bioinformatics), the implementation of the presented B&B framework has been made publicly available.

We introduce the bi-objective prize-collecting Steiner tree problem, whose goal is to find a subtree considering the conflicting objectives of minimizing the edge costs for building that tree, and maximizing the collected node revenues. We consider five iterative mixed-integer programming (MIP) frameworks that identify the complete Pareto front, i.e., one efficient solution for every point on the Pareto front. More precisely, the following methods are studied: an ε-constraint method, a two-phase method, a binary search in the objective space, a weighted Chebyshev norm method, and a method of Sylva and Crema. We also investigate how to exploit and recycle information gained during these iterative MIP procedures to accelerate the solution process. We consider (i) additional strengthening valid inequalities, (ii) procedures for initializing feasible solutions (using a solution pool), (iii) procedures for recycling violated cuts (using a cut pool), and (iv) guiding the branching process by previously detected Pareto optimal solutions. This work is a first study on exact approaches for solving the bi-objective prize-collecting Steiner tree problem. Standard benchmark instances from the literature are used to assess the efficacy of the proposed methods.

Abstract We consider a rather generic class of network design problems in which a set or subset of given terminal nodes must be connected to a dedicated root node by simple paths and a variety of resource and/or quality of service constraints must be respected. These extensions of the classical Steiner tree problem on a graph can be well modeled by a path formulation in which individual variables are used for all feasible paths. To solve this formulation in practice, branch‐and‐price is used. It turns out, however, that a naive implementation of column generation suffers strongly from certain degeneracies of the pricing subproblem, leading to excessive running times. After analyzing these computational problems, we propose two methods to accelerate and stabilize column generation by using alternative dual‐optimal solutions. The resulting branch‐and‐price approach is practically tested on the rooted delay‐constrained Steiner tree problem and a quota‐constrained version of it. Results indicate that the proposed methods in general speed‐up the solution process dramatically, far more than a piecewise linear stabilization to which we compare. Furthermore, our branch‐and‐price approach exhibits on most test instances a better performance than a state‐of‐the‐art branch‐and‐cut approach based on layered graphs. As the new stabilization technique utilizing alternative dual‐optimal solutions is generic in the sense that it easily adapts to the inclusion of a large variety of further constraints and different objective functions, the proposed method is highly promising for a large class of network design problems. © 2012 Wiley Periodicals, Inc. NETWORKS, 2013

Abstract We consider the generalized minimum edge‐biconnected network problem where the nodes of a graph are partitioned into clusters and exactly one node from each cluster is required to be connected in an edge‐biconnected way. Instances of this problem appear, for example, in the design of survivable backbone networks. We present different variants of a variable neighborhood search approach that utilize different types of neighborhood structures, each of them addressing particular properties as spanned nodes and/or the edges between them. For the more complex neighborhood structures, we apply efficient techniques—such as a graph reduction—to essentially speed up the search process. For comparison purposes, we use a mixed integer linear programming formulation based on multi‐commodity flows to solve smaller instances of this problem to proven optimality. Experiments on such instances indicate that the variable neighborhood search is also able to identify optimal solutions in the majority of test runs, but within substantially less time. Tests on larger Euclidean and random instances with up to 1,280 nodes, which could not be solved to optimality by mixed integer programming, further document the efficiency of the variable neighborhood search. In particular, all proposed neighborhood structures are shown to contribute significantly to the search process. © 2010 Wiley Periodicals, Inc. NETWORKS, 2010

We present an approach to fault management based on an architecture for distributed and collaborative network management as developed in the CELTIC project Madeira. It uses peer-to-peer communication facilities and a logical overlay network facilitating decentralized and iterative alarm processing and correlation. We argue that such an approach might help to overcome key challenges that are posed by NGN scenarios to traditional centralized network management systems. Its feasibility is demonstrated by means of a case study from the area of wireless mesh networks, where an application prototype has been developed.