1,720,971 research outputs found
Nonlinear chance-constrained problems with applications to hydro scheduling
Full text linkWe present a Branch-and-Cut algorithm for a class of nonlinear chance-constrained mathematical optimization problems with a finite number of scenarios. Unsatisfied scenarios can enter a recovery mode. This class corresponds to problems that can be reformulated as deterministic convex mixed-integer nonlinear programming problems with indicator variables and continuous scenario variables, but the size of the reformulation is large and quickly becomes impractical as the number of scenarios grows. The Branch-and-Cut algorithm is based on an implicit Benders decomposition scheme, where we generate cutting planes as outer approximation cuts from the projection of the feasible region on suitable subspaces. The size of the master problem in our scheme is much smaller than the deterministic reformulation of the chance-constrained problem. We apply the Branch-and-Cut algorithm to the mid-term hydro scheduling problem, for which we propose a chance-constrained formulation. A computational study using data from ten hydroplants in Greece shows that the proposed methodology solves instances faster than applying a general-purpose solver for convex mixed-integer nonlinear programming problems to the deterministic reformulation, and scales much better with the number of scenarios
Controlled islanding algorithm for AC/DC hybrid power systems utilising DC modulation
No full textThe controlled islanding problem is typically considered only for pure ac power systems, with the ultimate objective of either minimising power imbalance or power-flow among islands. However, as ac/dc hybrid power systems are becoming popular worldwide, current controlled islanding schemes should be redesigned to take into account the presence of high-voltage dc (HVDC) links, possibly connecting different islands. Accordingly, in this study, the authors solve the classic islanding problem combining HVDC power modulation with the conventional ac cutset search. The flexibility of the HVDC allows the authors’ strategy to jointly capture the previously mentioned objectives, and provides even lower power imbalances than the minimum imbalance strategy, at a relatively small cost of increasing power-flow impact. Then, the optimisation problem is formulated as a mixed-integer linear programming problem, which can be conveniently solved with existing commercial solvers. Finally, they validate their proposed strategy in two case studies, corresponding to a modified IEEE-118 system and to the China Southern Power Grid system. In both cases, dynamic simulations show that their proposed approach outperforms classic algorithms where the presence of HVDC power modulation is not explicitly taken into account
Optimizing allocation in a warehouse network
Full text linkWe study the allocation problem faced by an international retail company operating e-commerce: the firm wants to determine the initial allocation of goods to warehouses, so as to provide customers in local markets with high level of service. We represent the movement of goods through the warehouse network to the customers using linear and quadratic integer programming models, and provide a computational evaluation using real data
Rotor Speed Fluctuation Analysis for Rapid De-Loading of Variable Speed Wind Turbines
Full text linkDuring rapid de-loadings, the unbalanced power
of a variable speed wind turbine (VSWT) will accelerate the
rotor and may cause over speed problems. In this paper, we
specifically analyze the rapid de-loading rotor speed response of
a typical VSWT, and divide the process into four stages. Among
them, we show that the third stage, when the rotor accelerates
over the rated speed and the pitch control gets into action, is
the most critical in terms of the maximum rotor speed during
the transient phase. Then, based on the characteristics of the
response trajectory, we propose an estimation method to calculate
the maximum de-loading amplitude to ensure that the transient
rotor speed does not exceed the limit. Extensive simulations
under different wind speeds are provided to illustrate the deloading
response and the effectiveness of the proposed estimation
methodology
A Decentralized Power Sharing Strategy for Wind Farm De-Loading
No full textThis paper revises current power sharing algorithms used for de-loading operations in wind farms with variable speed wind turbines. In particular, we show that classic fair power sharing solutions can be obtained without requiring wind turbines to communicate local wind or power measurements, which is a very attractive feature for practical applications. For this purpose, algorithms borrowed from communication network theory, namely, Additive-Increase-Multiplicative-Decrease (AIMD) algorithms, are used to solve the power sharing problem in a decentralized fashion. Extensive simulations on wind farms consisting of ten wind turbines are provided to illustrate the potential and the efficacy of the proposed methodology
An Optimized Decentralized Power Sharing Strategy for Wind Farm De-Loading
Full text linkMany centralized and distributed power sharing algorithms have been proposed in the literature for de-loading operations in wind farms with variable speed wind turbines. Typically, in these strategies, two-way communications are required between the control center and the single turbines, or among the turbines. This paper solves the same problem in a truly decentralized fashion, which only requires a greatly reduced amount of one-way communications, without exchanging information among the turbines, and shows that an optimal solution can be obtained to minimize utility functions of interest of single wind turbines. In particular, we consider utility functions that take into account mechanical fluctuations and rotor over-speeds during transient, while balancing the utilization of wind turbines at
steady-state operations. This is achieved by adopting the so-called Additive Increase Multiplicative Decrease (AIMD) algorithms, which are frequently used in communication applications, for solving the power sharing problem in a decentralized fashion. Extensive simulations under different working conditions, on wind farms consisting of wind turbines of different mechanical characteristics, are provided to illustrate the potential and the efficiency of the proposed methodology
Modeling two-dimensional guillotine cutting problems via integer programming
No full textWe propose a framework to model general guillotine restrictions in two-dimensional cutting problems formulated as mixed-integer linear programs (MIPs). The modeling framework requires a pseudopolynomial number of variables and constraints, which can be effectively enumerated for medium-size instances. Our modeling of general guillotine cuts is the first one that, once it is implemented within a state-of-the-art MIP solver, can tackle instances of challenging size. We mainly concentrate our analysis on the guillotine two-dimensional knapsack problem (G2KP), for which a model, and an exact procedure able to significantly improve the computational performance, are given. We also show how the modeling of general guillotine cuts can be extended to other relevant problems such as the guillotine two-dimensional cutting stock problem and the guillotine strip packing problem (GSPP). Finally, we conclude the paper discussing an extensive set of computational experiments on G2KP and GSPP benchmark instances from the literature
Algoritmi esatti per il Job Shop Scheduling: approcci Mathematical Programming
No full textIn questa tesi ci occuperemo di fornire un modello MIP di base e di alcune sue varianti, realizzate allo scopo di comprenderne il comportamento ed eventualmente migliorarne l’efficienza. Le diverse varianti sono state costruite agendo in particolar modo sulla definizione di alcuni vincoli, oppure sui bound delle variabili, oppure ancora nell’obbligare il risolutore a focalizzarsi su determinate decisioni o specifiche variabili.
Sono stati testati alcuni dei problemi tipici presenti in letteratura e i diversi risultati sono stati opportunamente valutati e confrontati. Tra i riferimenti per tale confronto sono stati considerati anche i risultati ottenibili tramite un modello Constraint Programming, che notoriamente produce risultati apprezzabili in ambito di schedulazione. Un ulteriore scopo della tesi è, infatti, comparare i due approcci Mathematical Programming e Constraint Programming, identificandone quindi i pregi e gli svantaggi e provandone la trasferibilità al modello raffrontato
Development of an Embedded Computer Vision System for Automatic Waste Classification: The Hoooly! Project
No full textThis thesis presents the design, development, and evaluation of a smart waste bin prototype capable of classifying and sorting waste using computer vision and embedded artificial intelligence. The system integrates a convolutional neural network (CNN) for image-based waste classification with a mechanical sorting mechanism. The project was developed as part of a broader initiative in collaboration with a startup, contributing to real-world use cases and commercial demonstrations with major clients.
The prototype features a sealed input chamber where users insert waste items. These items rest on a rotating platform equipped with a camera. Upon detection, the platform rotates to a classification position, captures an image, and sends it to the onboard CNN for inference. The waste item is then directed to one of four customizable bins beneath the rotating platform based on its predicted category. The mechanical actuation and sensors are managed through a state-machine logic written in C on a dedicated microcontroller that interfaces with the Raspberry Pi and handles inputs from fill-level sensors, entrance sensors, and bin door status sensors.
The CNN was trained using transfer learning with pre-trained backbones—MobileNetV2, MobileNetV3, and EfficientNetV2-B0—finetuned on a custom waste image dataset. Two taxonomies were explored: a fine-grained version (e.g., plastic cups, glass, napkins) and a simplified 6-class taxonomy. Images were collected both in laboratory and real-world settings and augmented to improve robustness. Several configurations of dense layers and hyperparameters were tested to balance accuracy, inference latency, and model size. EfficientNetV2 consistently outperformed other backbones in classification accuracy, achieving up to 94% test accuracy in the reduced 6-class scenario.
The models were evaluated on multiple metrics, including top-1 accuracy, per-class precision and recall, inference latency, and memory footprint. Confusion matrices and classification reports revealed challenges in fine-grained class separation, especially between visually similar categories like paper, napkins, and Tetrapak. A significant insight emerged when the removal of the "paper" class led to a large boost in performance, confirming that high-quality data and clear class boundaries are critical for successful multi-class classification. This finding suggests that the underlying architecture is capable of handling more classes given a better-curated dataset.
Deployment involved not only inference testing but also full-cycle validation, including detection-to-sorting timing, mechanical reliability, and resilience under varied lighting and environmental conditions. The final system demonstrated over 92% actuation reliability and sustained performance over hundreds of test cycles. Furthermore, the bin connects to Google Cloud via Firebase, allowing real-time photo logging, remote updates, and performance monitoring—extending its potential for smart city applications.
This work exemplifies the integration of deep learning, embedded systems, and mechanical engineering to solve a practical environmental problem. It serves as a foundation for future research on adaptive waste classification, confidence-aware sorting, and scalable deployment across urban infrastructures. The thesis not only showcases a successful engineering implementation but also contributes to the sustainability and digital transformation goals in waste management
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