345 research outputs found

    Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

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    Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach

    Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

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    Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

    Acceptance Ordering Scheduling Problem: The impact of an order-portfolio on a make-to-order firm’s profitability

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    Firms’ growth, the darling measure of investors, comes from higher revenues. Thus, sales and marketing departments make extreme efforts to accept as many customer orders as possible. Unfortunately, not all orders contribute equally to profits, and some orders may even reduce net profits. Thus, saying no (i.e., not accepting an order) may be a necessary condition for net profits growth. For understanding the impact of rejecting orders on profitability, we propose an order acceptance and scheduling problem (OAS). Although the OAS has extensively been studied in the literature, there is still some gap between these papers and real-life problems in industry. In an attempt to close that gap, the OAS we propose considers orders revenues, machines costs, holding costs and tardiness costs. We develop a mixed integer linear programming (MILP) model for solving this problem. Since the complexity of the problem makes it impossible for the MILP to solver large-scale instances, we also propose a metaheuristic algorithm. Numerical experiments show that the metaheuristic finds good quality solutions in short computational times. In the last part of the paper we confirm some managerial insights: higher holding and tardiness costs imply a lower acceptance of orders, forcing production has a concave negative impact on net profits, and accurately estimating costs is essential for good planning

    Evaluation and optimisation of traction system for hybrid railway vehicles

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    Over the past decade, energy and environmental sustainability in urban rail transport have become increasingly important. Hybrid transportation systems present a multifaceted challenge, encompassing aspects such as hydrogen production, refuelling station infrastructure, propulsion system topology, power source sizing, and control. The evaluation and optimisation of these aspects are critical for the adaptation and commercialisation of hybrid railway vehicles. While there has been significant progress in the development of hybrid railway vehicles, further improvements in propulsion system design are necessary. This thesis explores strategies to achieve this ambitious goal by substituting diesel trains with hybrid trains. However, limited research has assessed the operational performance of replacing diesel trains with hybrid trains on the same tracks. This thesis develops various optimisation techniques for evaluating and refining the hybrid traction system to address this gap. In this research's first phase, the author developed a novel Hybrid Train Simulator designed to analyse driving performance and energy flow among multiple power sources, such as internal combustion engines, electrification, fuel cells, and batteries. The simulator incorporates a novel Automatic Smart Switching Control technique, which scales power among multiple power sources based on the route gradient for hybrid trains. This smart switching approach enhances battery and fuel cell life and reduces maintenance costs by employing it as needed, thereby eliminating the forced charging and discharging of excessively high currents. Simulation results demonstrate a 6% reduction in energy consumption for hybrid trains equipped with smart switching compared to those without it. In the second phase of this research, the author presents a novel technique to solve the optimisation problem of hybrid railway vehicle traction systems by utilising evolutionary and numerical optimisation techniques. The optimisation method employs a nonlinear programming solver, interpreting the problem via a non-convex function combined with an efficient "Mayfly algorithm." The developed hybrid optimisation algorithm minimises traction energy while using limited power to prevent unnecessary load on power sources, ensuring their prolonged life. The algorithm takes into account linear and non-linear variables, such as velocity, acceleration, traction forces, distance, time, power, and energy, to address the hybrid railway vehicle optimisation problem, focusing on the energy-time trade-off. The optimised trajectories exhibit an average reduction of 16.85% in total energy consumption, illustrating the algorithm's effectiveness across diverse routes and conditions, with an average increase in journey times of only 0.40% and a 15.18% reduction in traction power. The algorithm achieves a well-balanced energy-time trade-off, prioritising energy efficiency without significantly impacting journey duration, a critical aspect of sustainable transportation systems. In the third phase of this thesis, the author introduced artificial neural network models to solve the optimisation problem for hybrid railway vehicles. Based on time and power-based architecture, two ANN models are presented, capable of predicting optimal hybrid train trajectories. These models tackle the challenge of analysing large datasets of hybrid railway vehicles. Both models demonstrate the potential for efficiently predicting hybrid train target parameters. The results indicate that both ANN models effectively predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade-forward neural network topology in the time-based architecture outperforms the feed-forward neural network topology in terms of mean squared error and maximum error in the power-based architecture. Specifically, the cascade-forward neural network topology within the time-based structure exhibits a slightly lower MSE and maximum error than its power-based counterpart. Moreover, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time-based architecture exhibits lower differences (0.18% and 0.85%) compared to the power-based architecture (0.46% and 0.92%)

    Intelligent robotic disassembly optimisation for sustainability using the bees algorithm

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    Robotic disassembly plays a pivotal role in achieving efficient and sustainable product lifecycle management, with a focus on resource conservation and waste reduction. This thesis discusses robotic disassembly sequence planning (RDSP) and robotic disassembly line balancing (RDLB), with a specific emphasis on optimising sustainability models. The overarching goal was to enhance the efficiency and effectiveness of disassembly processes through intelligent robotic disassembly optimisation techniques. At the heart of this research lies the application of the Bees Algorithm (BA), a metaheuristic optimisation algorithm inspired by the foraging behaviour of honeybees. By harnessing the power of the BA, this research aims to address the challenges associated with RDSP and RDLB, ultimately facilitating sustainable disassembly practices. The thesis gives an extensive literature review of RDSP and RDLB to gain deeper insight into the current research landscape. The challenges of the RDSP problem were addressed in this work by introducing a sustainability model and various scenarios to enhance disassembly processes. The sustainability model considers three objectives: profit, energy savings, and environmental impact reduction. The four explored scenarios were recovery (REC), remanufacture (REM), reuse (REU), and an automatic recovery scenario (ARS). Two novel tools were developed for assessing algorithm performance: the statistical performance metric (SPM) and the performance evaluation index (PEI). To validate the proposed approach, a case study involving the disassembly of gear pumps was used. To optimise the RDSP, single-objective (SO), multiobjective (MO) aggregate, and multiobjective nondominated (MO-ND) approaches were adopted. Three optimisation algorithms were employed — Multiobjective Nondominated Bees Algorithm (MOBA), Nondominated Sorting Genetic Algorithm - II (NSGA-II), and Pareto Envelope-based Selection Algorithm - II (PESA-II), and their results were compared using SPM and PEI. The findings indicate that MO-ND is more suitable for this problem, highlighting the importance of considering conflicting objectives in RDSP. It was shown that recycling should be considered the last-resort recovery option, advocating for the exploration of alternative recovery strategies prior to recycling. Moreover, MOBA outperformed other algorithms, demonstrating its effectiveness in achieving a more efficient and sustainable RDSP. The problem of sequence-dependent robotic disassembly line balancing (RDLBSD) was next investigated by considering the interconnection between disassembly sequence planning and line balancing. Both aspects were optimised simultaneously, leading to a balanced and optimal disassembly process considering profitability, energy savings, environmental impact, and line balance using the MO-ND approach. The findings further support the notion that recycling should be considered the last option for recovery. Again, MOBA outperformed other algorithms, showcasing its capability to handle more complex problems. The final part of the thesis explains the mechanism of a new enhanced BA, named the Fibonacci Bees Algorithm (BAF). BAF draws inspiration from the Fibonacci sequence observed in the drone ancestry. This adoption of the Fibonacci-sequence-based pattern reduces the number of algorithm parameters to four, streamlining parameter setting and simplifying the algorithm’s steps. The study conducted on the RDSP problem demonstrates BAF’s performance over the basic BA, particularly in handling more complex problems. The thesis concludes by summarising the key contributions of the work, including the enhancements made to the BA and the introduction of novel evaluation tools, and the implications of the research, especially the importance of exploring alternative recovery strategies for end-of-life (EoL) products to align with Circular Economy principles

    Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

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    Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

    EM-driven miniaturization of high-frequency structures through constrained optimization

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    The trends afoot for miniaturization of high-frequency electronic devices require integration of active and passive high-frequency circuit elements within a single system. This high level of accomplishment not only calls for a cutting-edge integration technology but also necessitates accommodation of the corresponding circuit components within a restricted space in applications such as implantable devices, internet of things (IoT), or 5G communication systems. At the same time, size reduction does not remain the only demand. The performance requirements of the abovementioned systems form a conjugate demand to that of the size reduction, yet with a contrasting nature. A compromise can be achieved through constrained numerical optimization, in which two kinds of constrains may exist: equality and inequality ones. Still, the high cost of electromagnetic-based (EM-based) constraint evaluations remains an obstruction. This issue can be partly mitigated by implicit constraint handling using the penalty function approach. Nevertheless, securing its performance requires expensive guess-work-based identification of the optimum setup of the penalty coefficients. An additional challenge lies in allocating the design within or in the vicinity of a thin feasible region corresponding to equality constraints. Furthermore, multimodal nature of constrained miniaturization problems leads to initial design dependency of the optimization results. Regardless of the constraint type and the corresponding treatment techniques, the computational expenses of the optimization-based size reduction persist as a main challenge. This thesis attempts to address the abovementioned issues specifically pertaining to optimization-driven miniaturization of high frequency structures by developing relevant algorithms in a proper sequence. The first proposed approach with automated adjustment of the penalty functions is based on the concept of sufficient constraint violation improvement, thereby eliminating the costly initial trial-and-error stage for the identification of the optimum setup of the penalty factors. Another introduced approach, i.e., correction-based treatment of the equality constraints alleviates the difficulty of allocating the design within a thin feasible region where designs satisfying the equality constraints reside. The next developed technique allows for global size reduction of high-frequency components. This approach not only eliminates the aforementioned multimodality issues, but also accelerates the overall global optimization process by constructing a dimensionality-reduced surrogate model over a pre-identified feasible region as compared to the complete parameter search space. Further to the latter, an optimization framework employing multi-resolution EM-model management has been proposed to address the high cost issue. The said technique provides nearly 50 percent average acceleration of the optimization-based miniaturization process. The proposed technique pivots upon a newly-defined concept of model-fidelity control based on a combination of algorithmic metrics, namely convergence status and constraint violation level. Numerical validation of the abovementioned algorithms has also been provided using an extensive set of high-frequency benchmark structures. To the best of the author´s knowledge, the presented study is the first investigation of this kind in the literature and can be considered a contribution to the state of the art of automated high-frequency design and miniaturization

    A Survey of Dataset Refinement for Problems in Computer Vision Datasets

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    Large-scale datasets have played a crucial role in the advancement of computer vision. However, they often suffer from problems such as class imbalance, noisy labels, dataset bias, or high resource costs, which can inhibit model performance and reduce trustworthiness. With the advocacy of data-centric research, various data-centric solutions have been proposed to solve the dataset problems mentioned above. They improve the quality of datasets by re-organizing them, which we call dataset refinement. In this survey, we provide a comprehensive and structured overview of recent advances in dataset refinement for problematic computer vision datasets. Firstly, we summarize and analyze the various problems encountered in large-scale computer vision datasets. Then, we classify the dataset refinement algorithms into three categories based on the refinement process: data sampling, data subset selection, and active learning. In addition, we organize these dataset refinement methods according to the addressed data problems and provide a systematic comparative description. We point out that these three types of dataset refinement have distinct advantages and disadvantages for dataset problems, which informs the choice of the data-centric method appropriate to a particular research objective. Finally, we summarize the current literature and propose potential future research topics.Comment: 33 pages, 10 figures, to be published in ACM Computing Survey

    A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

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    The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area
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