Placement Problem Research Articles

Uncertainty propagation of initial conditions in thermal models

The operation of machine tools often demands a highly accurate knowledge of the tool center point’s (TCP) position. The displacement of the TCP over time can be inferred from coupled thermal models, which comprise a set of geometrically coupled heat equations. Each of these equations represents the temperature in part of the machine, and they are often formulated on complicated geometries. The accuracy of the TCP prediction depends highly on the accuracy of the model parameters, such as heat exchange parameters, and the initial temperature. Thus it is of utmost interest to determine the influence of these parameters on the TCP displacement prediction. In turn, the accuracy of the parameter estimate is essentially determined by the measurement accuracy and the sensor placement. Determining the accuracy of a given sensor configuration is a key prerequisite of optimal sensor placement. In this paper, we develop a thermal model for the discretization of realistic machine tool. On top of this model we propose two numerical algorithms to evaluate any given thermal sensor configuration with respect to its accuracy. We compute the posterior variances from the posterior covariance matrix with respect to an uncertain initial temperature field. The full matrix is dense and potentially very large, depending on the size of the discretized model. We first present a way to compute this approximation, which relies on the computation of the model sensitivities with respect to the initial values. Additionally, we present a low-rank tensor method which exploits the underlying system structure. We compare the efficiency of both algorithms with respect to runtime and memory requirements and discuss their respective advantages with regard to optimal sensor placement problems.

Energy efficient resource management in data centers using imitation-based optimization

Cloud computing is the paradigm for delivering streaming content, office applications, software functions, computing power, storage, and more as services over the Internet. It offers elasticity and scalability to the service consumer and profit to the provider. The success of such a paradigm has resulted in a constant increase in the providers’ infrastructure, most notably data centers. Data centers are energy-intensive installations that require power for the operation of the hardware and networking devices and their cooling. To serve cloud computing needs, the data center organizes work as virtual machines placed on physical servers. The policy chosen for the placement of virtual machines over servers is critical for managing the data center resources, and the variability of workloads needs to be considered. Inefficient placement leads to resource waste, excessive power consumption, and increased communication costs. In the present work, we address the virtual machine placement problem and propose an Imitation-Based Optimization (IBO) method inspired by human imitation for dynamic placement. To understand the implications of the proposed approach, we present a comparative analysis with state-of-the-art methods. The results show that, with the proposed IBO, the energy consumption decreases at an average of 7%, 10%, 11%, 28%, 17%, and 35% compared to Hybrid meta-heuristic, Extended particle swarm optimization, particle swarm optimization, Genetic Algorithm, Integer Linear Programming, and Hybrid Best-Fit, respectively. With growing workloads, the proposed approach can achieve monthly cost savings of €201.4 euro and CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CO}_2$$\\end{document} Savings of 460.92 lbs CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CO}_2$$\\end{document}/month.

Device placement using Laplacian PCA and graph attention networks

Abstract The exponential growth in data and parameters in modern neural networks has created the need to distribute these models across multiple devices for efficient training, resulting in the device placement problem. Existing graph encoding approaches for device placement suffer from low efficiency when searching for optimal parallel strategies, primarily due to suboptimal positional information retrieval. To address these challenges, we propose the Laplacian Principal Component Analysis-graph attention networks (LPCA-GAT) model. Firstly, we employ GAT to capture complex relationships between nodes and generate node encodings. Secondly, we leverage LPCA on the graph Laplacian matrix to extract crucial low-dimensional positional information. Finally, by integrating these two components, we obtain the final node encodings. This enhances the representation capability of nodes within the graph, enabling efficient device placement. The experimental results demonstrate that LPCA-GAT achieves superior device placement results, specifically accelerating execution and computation time by 13.24% and 96.38%, respectively, leading to significant improvements in both operational efficiency and performance.

Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique

Cloud computing has revolutionized the way businesses and individuals access and utilize computing resources. Efficient virtual machine placement is a critical aspect of optimizing resource utilization, reducing operational costs, energy consumption, service level agreement and minimum virtual machine migrations, execution time, and ensuring the overall performance of cloud services. This manuscript introduces a novel approach that combines the creative problem-solving capabilities of brainstorming with the computational power of Artificial Neural Networks (ANN) to address the virtual machine placement problem in cloud environments. In this study, we propose a hybrid technique that leverages the collective intelligence of human brainstorming to generate a diverse set of placement strategies. These strategies are then evaluated, refined, and optimized using an ANN model trained on historical cloud resource allocation workload logs. By integrating the human creative process with the data-driven predictive capabilities of ANN, our approach aims to overcome the limitations of traditional virtual machine placement algorithms, which often struggle to adapt to dynamic workloads and changing resource requirements. The manuscript provides a detailed description of the hybrid technique, including the process of brainstorming for generating placement strategies, data collection and preprocessing, ANN model development, and the integration of these components into an efficient placement system. We present experimental results demonstrating the effectiveness of our approach in optimizing resource allocation, improving service performance, and optimizing resource utilization, reducing energy consumption, service level agreement and minimum virtual machine migrations, reducing execution time compared to existing static, and meta-heuristic methods. The proposed Brain Storming with ANN based Hybrid Technique offers a promising solution for enhancing the efficiency of virtual machine placement in cloud computing environments. BSO-ANN outperforms the existing techniques using performance metrics (energy consumption(Kwh), execution time(ms), SLA violations, and number of migrations). It combines human ingenuity with data-driven insights to adapt to the ever-changing dynamics of cloud workloads. This manuscript contributes to the ongoing research in cloud resource management, offering a practical approach for cloud service providers and organizations to better utilize their resources and enhance the overall quality of cloud-based services. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd.

Placement Flow Study and Detailed Placement for Hybrid-Row-Height Designs

At the 3 nm node, a hybrid-row-height design paradigm has emerged for better power efficiency and performance optimization. A diverse cell library that includes multiple variants of a cell with different fin counts is available. Instead of using cells with the same fin count for the entire chip, a design may combine cells with two different fin counts. Cells with the same fin count can be laid down in the same row resulting in a chip with hybrid row heights. With this brand-new design paradigm, revisiting and revamping the conventional VLSI placement flow becomes necessary. There were attempts that addressed the placement problem associated with hybrid-row-height design at the global placement stage or the placement legalization stage. In this work, we first propose an effective detailed placement approach suitable for hybrid-row-height designs and then conduct a comprehensive study to evaluate the different options of forming a complete hybrid-row-height design placement flow. For the first time, the advantage of considering the row configuration early on in the global placement stage is confirmed. Besides, our proposed detailed placement approach can improve the final half-perimeter wirelength by over 7% on average which more than double the improvement obtainable by a basic detailed placement algorithm similar to the well-known FastDP.

Solving the Problem of Fuzzy Partition-Distribution with Determination of the Location of Subset Centers

A large number of real-world problems from various fields of human activity can be reduced to optimal partitioning-allocation problems with the purpose of minimizing the partitioning quality criterion. A typical representative of such problem is an infinite-dimensional transportation problem and more generalized problems—infinite-dimensional problems of production centers placement along with the partitioning of the area of continuously distributed consumers with the purpose of minimizing transportation and production costs. The relevant problems are characterized by some kind of uncertainty level of a not-probabilistic nature. A method is proposed to solve an optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. The method is based on the synthesis of the methods of fuzzy theory and optimal partitioning-allocation theory, which is a new science field in infinite-dimensional mathematical programming with Boolean variables. A theorem was proved that determines the form of the optimal solution of the corresponding optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. An algorithm for solving fuzzy partitioning-allocation problems is proposed, which is based on the proved theorem and on a variant of Shor’s r-algorithm—a non-differential optimization method. The application of the proposed method is demonstrated on model tasks, where the coefficient of mistrust is integrated to interpret the obtained result—the minimum value of the membership function, which allows each point of the set partition to be assigned to a specific fuzzy subset.

SplineGen: Approximating unorganized points through generative AI

This paper presents a learning-based method to solve the traditional parameterization and knot placement problems in B-spline approximation. Different from conventional heuristic methods or recent AI-based methods, the proposed method does not assume ordered or fixed-size data points as input. There is also no need for manually setting the number of knots. Parameters and knots are generated in an associative way to attain better parameter-knot alignment, and therefore a higher approximation accuracy. These features are attained by using a new generative model SplineGen, which casts the parameterization and knot placement problems as a sequence-to-sequence translation problem. It first adopts a shared autoencoder model to learn a 512-D embedding for each input point, which has the local neighborhood information implicitly captured. Then these embeddings are autoregressively decoded into parameters and knots by two associative decoders, a generative process automatically determining the number of knots, their placement, parameter values, and their ordering. The two decoders are made to work in a coordinated manner by a new network module called internal cross-attention. Once trained, SplineGen demonstrates a notable improvement over existing methods, with one to two orders of magnitude increase in approximation accuracy on test data.

Peculiarities of Adverb Placement in English Sentences (exemplified by the adverb "fairly")

The article deals with the problem of adverb placement in English sentences. The paper aims at studying the main tendencies and patterns which influence the choice of the syntactic position of adverbs in English. Special attention is paid to the correlation between the semantic and syntactic features of adverbs. The article also describes possible syntactic positions of adverbs belonging to different semantic classes and the factors which determine the change of the place of the adverb in the sentence. The paper also considers the peculiarities of functioning of adverbs in the sentence based on the analysis of the adverb "fairly". The material for study includes contexts of use of the adverb "fairly" taken from the British National Corpus (BNC). The research uses the following methods: descriptive and statistical methods, methods of contextual analysis and syntactic analysis. The relevance of the subject of the research is due to the fact that in modern English adverbs are characterized by a high level of variability in syntactic placement, and even adverbs belonging to the same semantic class may have different syntactic properties. The analysis showed that "fairly" can be used as an intensifying adverb, an emphasizer, an adverb of manner, an evaluation adverb; in a number of cases it can also acquire additional modal meanings or serve as a speech act-related adverb. Each meaning of the adverb is characterised by its specific variants of syntactic positioning: before the word it modifies, in medial position, in end position and as a parenthetic word detached from the rest of the sentence. The results of the research show the necessity to study the syntactic properties of adverbs in more detail and to clarify their existing dictionary definitions.

A Data-Driven Approach for Optimizing Early-Stage Electric Vehicle Charging Station Placement

This paper presents a novel and practical data-driven approach to sub-optimally allocate charging stations for electric vehicles (EVs) in an early-stage setting. Specifically, we investigate the following problem: For a city with a limited budget for public EV charging infrastructure construction, where should the charging stations be deployed in order to promote the transition of EVs from traditional cars? We develop a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> -nearest model and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -nearest model that can capture people's satisfaction towards a certain design and formulate the early-stage EV charging station placement problem as a monotone submodular maximization problem utilizing fine-grained population, trip, transportation network and POI data. A greedy-based algorithm is proposed to solve the problem efficiently with a provable approximation ratio. A case study of Haikou is provided to demonstrate the effectiveness of our approach.

Strategic deployment in the deep: Principled underwater sensor placement optimization with three-dimensional acoustic map.

Underwater acoustic sensors are vital for monitoring marine environments and detecting targets, but their optimal placement presents challenges, particularly in deep-sea environments. This paper addresses the question of determining the optimal sensor placement in a specific ocean region through a principled optimization approach. While previous studies mainly utilized heuristic algorithms without exploiting problem-specific structures, this work explores leveraging the complex three-dimensional acoustic environment through principled modeling and tailored optimization. Specifically, intricate three-dimensional multi-directional acoustic maps are constructed for each sensor. Based on these maps, the sensor placement problem is then cast as an integer linear programming, allowing the study to leverage established theoretical results from operations research. Additionally, an alternative algorithm with its performance indicator is presented to find near-optimal solutions efficiently and can empirically reach over 99% coverage of the optimal solution. Experimental results using real-life data from the South China Sea demonstrate the effectiveness of the proposed approach in achieving much larger detection coverage compared to random and empirical strategies. Notably, the alternative fast algorithm approaches the optimal solution in significantly less time. Furthermore, experiments show that any further simplification of this approach leads to the performance degradation.

ISAC‐oriented access point placement in cell‐free mMIMO systems

AbstractIn mobile communication networks for integrated sensing and communication, site planning of access points fundamentally determines both communication quality and sensing performance. This paper aims to tackle the AP placement problem under the setting of cell‐free massive multiple‐input multiple‐output, which represents a promising direction for network architecture evolution. Seeking a balance between the sum‐throughput of communication and the Cramér–Rao lower bound of target sensing, the access points are first pre‐placed by solving a convex optimization and further adjusted with optimal angular geometry. The final step involves employing nearest neighbour projection to refine access point positions in the cell‐free massive multiple‐input multiple‐output system, ensuring alignment with integrated sensing and communication requirements. The numerical results show that the proposed algorithm achieves 90% of the optimal communication sum‐rate and 95% of the optimal target localization accuracy.

Advanced roll porous scaffold 3D bioprinting technology.

Improvements in the roll porous scaffold (RPS) 3D bioproduction technology will increase print density of 10-15µm cells by ~ 20% up to ~ 1.5 × 108 cells/mL and purity of organoid formation by > 17%. The use of 360 and 1200 dpi inkjet printheads immediately enables biomanufacturing with 10-30µm cells in a single organoid with performance > 1.8 L/h for 15µm layer thickness. The spongy bioresorbable ribbon for RPS technology is designed to solve the problems of precise placement, leakage and increasing in the number of instantly useable cell types and superior to all currently dominant 3D bioprinting methods in speed, volume, and print density without the use of expensive equipment and components. The potential of RPS for parallel testing of new substances studied was not on animals, but using generated 3D biomodels "organ on a chip". Solid organoids are more suitable for personalized medicine with simultaneous checking of several treatment methods and drugs, targeted therapy for a specific patient in vitro using the 3D composition of his personal cells, and selection of the most effective ones with the least toxicity. Overcoming the shortage of organs for implantation and personal hormone replacement therapy for everyone was achieved using printed endocrine glands based on their DNA.

OPTIMIZATION OF LOADING AND UNLOADING WORKS AND PLACEMENT OF TARED AND PIECE CARGOES IN VEHICLES AND WAREHOUSES USING THE METHODS OF ERGONOMICS AND THEORY OF SYSTEMS

Purpose. The tasks of optimizing the placement of tared and piece cargoes (or the task of loading) in a limited volume (capacity) are typical for cases of loading with tared and piece cargoes (mainly large-sized) of transport containers, car bodies, ship holds in the merchant fleet, transport aircraft, railway wagons, areas of the warehouse complex. At the same time, the safe and efficient performance of loading and unloading operations with tared and piece cargoes, including large-sized ones, in a limited space is directly related to taking into account the requirements and using ergonomic methods. The purpose of this paper is to develop a mathematical model for the optimization of the placement of tared and piece cargoes (or about loading) in a limited volume (capacity), which is used to select a subset of cargo units with the maximum relative utility, while observing the limit on the total volume (or mass) of cargo. Methods. The analysis of literary sources made it possible to establish that for modeling and solving the problem of optimizing the placement of cargo (or loading) in a limited volume (capacity), it is rational to use one of the exact solution methods (dynamic programming method), the number of stages of which is the number of types of cargo. The work uses the method of dynamic programming, methods of theory of systems, and linear optimization. Results. The article presents a mathematical model of the problem of optimal placement of tared and piece cargoes (or about loading) in a limited volume, and for the case of placing weapons, ammunition, weapons, it is proposed to maximize not the function of the total cost (value) of the cargo, but the function that would take into account the relative usefulness of each cargo. The relative usefulness of each cargo is proposed to be determined by expert methods of systems theory, for example, the Delphi method, which is well formalized and suitable for computer processing of results. Scientific novelty. The scientific novelty consists in the development of a mathematical model that allows solving the problem of optimal placement of container loads (or loading) in a limited volume, and the target function of the optimization model is proposed to be the total relative usefulness of the cargo. Practical significance. Defined ergonomic requirements for cargo operations with tared and piece cargoes in a limited space (vehicles, warehouses). A method of numerical calculation based on the model is proposed (the case of loading a car body with tared and piece cargoes). In further research, the obtained results will be used for computer simulation according to the developed model using the Solver tool of Microsoft Excel.

Greedy selection of optimal location of sensors for uncertainty reduction in seismic moment tensor inversion

We address an optimal sensor placement problem through Bayesian experimental design for seismic full waveform inversion for the recovery of the associated moment tensor. The objective is that of optimally choosing the location of the sensors (stations) from which to collect the observed data. The Shannon expected information gain is used as the objective function to search for the optimal network of sensors. A closed form for such objective is available due to the linear structure of the forward problem, as well as the Gaussian modeling of the observational errors and prior distribution. The resulting problem being inherently combinatorial, a greedy algorithm is deployed to sequentially select the sensor locations that form the best network for learning the moment tensor. Numerical results are presented to display the optimal network of sensors and how the uncertainty in the inferred seismic moment tensor contracts. The scenario of full three-dimensional velocity models or unknown earthquake source locations is treated as nuisance uncertainty, contributing to the overall uncertainty without being the focus of the inversion. This is addressed using a consensus approach over a set of realizations of the nuisance parameter. We analyzed the resulting network of stations for the moment tensor inversion under model misspecification, which reflects realistic data-generating processes.

Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting

This paper investigates the problem of optimal base placement in collaborative robotic car painting. The objective of this problem is to find the optimal fixed base positions of a collection of given articulated robotic arms on the factory floor/ceiling such that the possibility of vehicle paint coverage is maximized while the possibility of robot collision avoidance is minimized. Leveraging the inherent two-dimensional geometric features of robotic car painting, we construct two types of cost functions that formally capture the notions of paint coverage maximization and collision avoidance minimization. Using these cost functions, we formulate a multi-objective optimization problem, which can be readily solved using any standard multi-objective optimizer. Our resulting optimal base placement algorithm decouples base placement from motion/trajectory planning. In particular, our computationally efficient algorithm does not require any information from motion/trajectory planners a priori or during base placement computations. Rather, it offers a hierarchical solution in the sense that its generated results can be utilized within already available robotic painting motion/trajectory planners. Our proposed solution’s effectiveness is demonstrated through simulation results of multiple industrial robotic arms collaboratively painting a Ford F-150 truck.

A two-step linear programming approach for repeater placement in large-scale quantum networks

Thanks to the applications such as Quantum Key Distribution and Distributed Quantum Computing, the deployment of quantum networks is gaining great momentum. A major component in quantum networks is repeaters, which are essential for reducing the error rate of qubit transmission for long-distance links. However, repeaters are expensive devices, so minimizing the number of repeaters placed in a quantum network while satisfying performance requirements becomes an important problem. Existing solutions typically solve this problem optimally by formulating an Integer Linear Program (ILP). However, the number of variables in their ILPs is O(n2), where n is the number of nodes in a network. This incurs infeasible running time when the network scale is large. To overcome this drawback, this paper proposes to solve the repeater placement problem by two steps, with each step using a linear program of a much smaller scale with O(n) variables. Although this solution is not optimal, it dramatically reduces the time complexity, making it practical for large-scale networks. Moreover, it constructs networks that have higher node connectivity than those by existing solutions, since it deploys slightly more number of repeaters into networks. Our extensive experiments on both synthetic and real-world network topologies verified our claims.

Hybrid machine learning framework for multi-well trajectory optimization in an unconventional field

The well placement problem in the petroleum industry is usually solved by integrating an optimizer with a reservoir simulation model. This is a time-consuming approach requiring thousands of simulation runs depending on the complexity of the reservoir system. This paper introduces two well placement optimization models: a Fast-Marching Model (FMM) based on fluid flow physics and a Hybrid Model integrating reservoir simulations and FMM results with a gradient boosting algorithm. These models prioritize speed and accuracy when integrated with genetic algorithm optimization, demonstrated using a synthetic unconventional field. From 290 randomly selected locations across the synthetic unconventional field, simulation runs were performed to determine cumulative gas production and used as ground truth. Using reservoir properties at these locations as inputs, Relative Opportunity Ranking (ROR) maps were generated using the FMM and Hybrid Model approaches. The ROR maps were then linked with a genetic algorithm to determine optimal well locations, iteratively updating ROR maps with penalty maps until all wells were appropriately placed. Results indicated that the standalone FMM generated ROR maps were highly correlated with the simulation results. Time complexity analysis revealed that both models were significantly faster than traditional methods, with the FMM independent of simulation and the Hybrid Model requiring only about 290 simulation runs. Ultimately, these models have shown tremendous potential for integration into current reservoir engineering workflows, reducing decision-making times in both greenfield and brownfield scenarios.

Framework for content server placement using integrated learning in content delivery network

&lt;p&gt;Content placement is a significant concern in content delivery networks (CDN), irrespective of various evolving studies. Existing methodologies showcase various significant unaddressed issues concerning content placement approaches' complexities. Therefore, the proposed study presents a novel computational framework towards dynamic content placement strategy using a novel integrated machine learning approach. Simplified mathematical modelling is used to formulate and solve the content placement problem. At the same time, reinforcement learning and the sequential attentional neural network have been utilized to optimize the decision-making towards placement of content servers. Designed and assessed over a Python environment, the proposed scheme is witnessed to exhibit 35% reduced bandwidth utilization, 20% reduced delay, 23% reduced computational resource utilization, and 28% reduced algorithm processing time in contrast to existing predictive content placement schemes.&lt;/p&gt;

A hound-inspired pre-hybridized genetic approach for router placement in wireless mesh networks

In the last years, wireless mesh networks (WMNs) have gained more and more popularity in many research and industrial applications thanks to their easy implementation, maintenance, and great reliability at a low cost. Nevertheless, for a large number of nodes, the performance of such networks is heavily influenced by the positioning of routers and gateways over the area to be covered. In this paper, we tackle the router placement problem, which is known to be NP-hard, and its approximate solution through a meta-heuristic approach. The proposed solution empowers the benefits offered by a genetic algorithm pre-hybridized with a local search approach inspired by the behavior of hound dogs. The basic idea is to exploit the dogs’ capabilities in moving throughout the solution space to effectively explore it by placing themselves in areas that are more favorable for achieving a high-quality approximate solution in a reasonable time. Experimental results on several benchmarking instances and comparisons with the most effective state-of-the-art algorithms have demonstrated the potential of the proposed approach. This is evidenced by very high connectivity and coverage, a low number of generations, and a small GA population required for convergence. This results in low computational effort and significant time savings, which are of paramount importance in IoT and edge scenarios. We remark that, although offering potential, at the current state, our proposal is not able to adapt to areas with obstacles or irregular shapes.

A hybrid-ABC approach to the multi-controller placement problem of software-defined networks

A hybrid-ABC approach to the multi-controller placement problem of software-defined networks