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NOISE CONTROL ENGINEERING JOURNAL 2024年第5期72卷 412-424页

作者： Song, Guochenhao Mo, Zhuang Shi, Tongyang Bolton, J. Stuart Purdue Univ Ray W Herrick Labs W Lafayette IN 47907 USA Chinese Acad Sci Inst Acoust Beijing Peoples R China

High surface area particles, like granular activated carbon (GAC), have shown is described: i.e., a GAC stack supported by a soft, porous layer. At first, it was absorption performance of GAC stacks can be significantly enhanced by inserting a melamine foam between the stack and a rigid backing. To enable an analytical model of this type of absorber, the interfaces between GAC stacks and other materials were first characterized. Further, both 1-D layered transfer-matrix and 2-D axi-symmetric finite-difference approaches were implemented to predict the measured absorption spectra. Because of the constraint of the stack at the impedance tube wall, the 2-D model leads to more accurate absorption spectrum predictions. However, the difference between the two model predictions becomes negligible for large-area treatments. Therefore, a more efficient 1-D theory was incorporated in the multi-objective optimization to design broadband GAC-foam absorbers. Two optimization objectives were defined to balance the overall absorption performance: i.e., one associated with absorption at low frequencies, and the other with absorption at higher frequencies. It was found that a minimum thickness broadband GAC-foam absorber favors a thin, soft foam. In conclusion, this article presented a framework for the development of general broadband sound absorbers, and by combining both granular and porous materials, it may be possible to design practical sound-absorbing metamaterials that perform well at low frequencies. (c) 2024 Institute of Noise Control Engineering.

关键词： multiobjective optimization

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Compromising allocation for optimising agri-food supply chain distribution network: a fuzzy stochastic programming approach

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第6期15卷 2019-2041页

作者： Gupta, Srikant Chaudhary, Sachin Singh, Rajesh Kr Garza-Reyes, Jose Arturo Kumar, Vikas Jaipuria Inst Management Jaipur Rajasthan India Autonomous State Med Coll Dept Community Med Shahjahanpur Uttar Pradesh India Management Dev Inst Gurgaon India Univ Derby Ctr Supply Chain Improvement Kedleston Rd Campus Derby DE22 1GB England Birmingham City Univ Birmingham England

The management of Agri-food supply chains is a complex task, given the unique product characteristics, perishability, uncertain demand, and specific storage requirements. This research introduces an innovative approach to optimizing product allocation among producers, brokers, wholesalers, and retailers, focusing on minimizing transportation costs and network delivery time through multi-objective programming. To address uncertainties, supply and demand constraints are modelled using a gamma distribution, and the maximum likelihood estimation method determines their parameters with specified probabilities. The study conducts a case analysis to showcase the model's practical effectiveness, and a numerical comparison with alternative approaches is included. The primary goal of this study is to enhance the efficiency of agri-food supply chain management practices, providing valuable insights for practitioners in the field, with a focus on cost reduction and improved delivery time.

关键词： multi-objective optimization Supply chain network Stochastic programming Gamma distribution Maximum likelihood estimation Fuzziness

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Development of an optimization model for decision-making in building retrofit projects using RETROSIM

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ADVANCES IN BUILDING ENERGY RESEARCH 2023年第3期17卷 324-344页

作者： Asadi, Ehsan Chenari, Behrang Gaspar, Adelio Rodrigues da Silva, Manuel Gameiro Univ Coimbra Dept Mech Engn ADAI LAETA Coimbra Portugal

Nearly-zero energy buildings are standard for new constructions in the European Union. The challenge for decarbonized cities is retrofitting the existing buildings. Retrofitting the existing buildings provides considerable opportunities for improving occupants' comfort and reducing global energy consumption and greenhouse gas emissions. Building retrofit is being considered one of the main approaches to achieve sustainability in the built environment at relatively low-cost and high-uptake rates. Although a broad range of retrofit technologies are readily available, methods to ascertain the most suitable set of retrofit actions for specific projects are still a practical challenge. This article first aims to develop and present a simulation-based multi-objective optimization model, named RETROSIM, to quantitatively assess technology choices in a building retrofit project. RETROSIM is a combination of a building energy simulation model and RETROSIM proprietary optimization engine. Secondly, in this study, RETROSIM is used to evaluate a building retrofit project as a case study to show the functionality of the proposed method. The study initiates with the single optimization of objective functions focusing on the building's characteristics and performance: primary energy consumption, global costs and thermal discomfort hours. Then this methodology is used to study the interaction between these conflicting objectives and evaluate their trade-offs.

关键词： RETROSIM building retrofit energy efficiency multi-objective optimization thermal comfort

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Allocation and control of multi-devices voltage regulation in distribution systems via rough set theory and grasshopper algorithm: A practical study

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RESULTS IN ENGINEERING 2025年 25卷

作者： Elsayed, Abdullah M. El-Rifaie, Ali M. Areed, Marwa F. Shaheen, Abdullah M. Atallah, Mohamed Osman Damietta Univ Fac Engn Elect Engn Dept New Damietta 34511 Damietta Egypt Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait Damietta Univ Fac Engn Elect Engn Dept Comp Engn Specializat New Damietta 34511 Damietta Egypt Suez Univ Fac Engn Elect Engn Dept POB 43221 Suez Egypt

The distribution system is the largest component of electrical power systems, playing a crucial role in delivering electricity to consumers. However, most distribution networks are uncontrolled, resulting in issues such as insufficient monitoring, poor planning, high energy losses, inadequate voltage regulation, low reliability, and frequent overloading. To address these challenges, this paper presents optimal planning, control, and operation of integrated Switched Capacitor Banks (SCBs), Distributed Generators Devices (DGDs), and Automatic Voltage Regulators (AVRs) for enhancing the performance of the distribution systems. A novel integrated approach is presented combining Statistical Rough Set Theory (SRST) with the Grasshopper optimization Algorithm (GOA) for the allocation and control of these multi-devices in distribution systems individually and simultaneously. The proposed combined SRST-GOA is designed in a multi-objective framework, focusing on minimizing power loss and cost as well as voltage regulation improvement. Also, this strategy is developed for the optimal dispatch of SCBs, DGDs, and AVRs in response to daily load variations, significantly improving system performance. A practical study of the Tala distribution system in the Menoufia governorate, Egypt, is addressed showcasing the effectiveness of the combined SRST-GOA in reducing losses, improving voltages, and maximizing annual savings. Results show reductions in energy losses, enhanced voltage profiles, and maximized cost savings, substantiating the proposed method's effectiveness compared to traditional approaches such as the standard GOA, well-known Particle Swarm Optimizer (PSO) technique, Honey Badger Algorithm (HBA) and Tunicate Swarm Algorithm (TSA). The proposed method achieved remarkable results when applied to the Tala distribution system in Egypt. The simultaneous allocation of SCBs, DGDs, and AVRs reduced power losses from 1,361.66 kW to 85.43 kW, representing a 93.7 % reduction. The v

关键词： Distribution systems Grasshopper optimization technique Rough set theory Losses minimization Distributed generators

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multi-skilled worker assignment in seru production system for the trade-off between production efficiency and workload fairness

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KYBERNETES 2023年第9期52卷 3495-3518页

作者： Zeng, Shaoyu Wu, Yinghui Yu, Yang Jiangsu Univ Sci & Technol Sch Econ & Management Zhenjiang Jiangsu Peoples R China Northeastern Univ Inst Intelligent Data & Syst Engn State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China

Purpose The paper formulates a bi-objective mixed-integer nonlinear programming model, aimed at minimizing the total labor hours and the workload unfairness for the multi-skilled worker assignment problem in Seru production system (SPS). Design/methodology/approach Three approaches, namely epsilon-constraint method, non-dominated sorting genetic algorithm 2 (NSGA-II) and improved strength Pareto evolutionary algorithm (SPEA2), are designed for solving the problem. Findings Numerous experiments are performed to assess the applicability of the proposed model and evaluate the performance of algorithms. The merged Pareto-fronts obtained from both NSGA-II and SPEA2 were proposed as final solutions to provide useful information for decision-makers. practical implications SPS has the flexibility to respond to the changing demand for small amount production of multiple varieties products. Assigning cross-trained workers to obtain flexibility has emerged as a major concern for the implementation of SPS. Most enterprises focus solely on measures of production efficiency, such as minimizing the total throughput time. Solutions based on optimizing efficiency measures alone can be unacceptable by workers who have high proficiency levels when they are achieved at the expense of the workers taking more workload. Therefore, study the tradeoff between production efficiency and fairness in the multi-skilled worker assignment problem is very important for SPS. Originality/value The study investigates a new mixed-integer programming model to optimize worker-to-seru assignment, batch-to-seru assignment and task-to-worker assignment in SPS. In order to solve the proposed problem, three problem-specific solution approaches are proposed.

关键词： multi-skilled worker assignment Bi-objective optimization Production efficiency Workload fairness

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multi-Session multi-objective Budget optimization for Auction-based Federated Learning

Multi-Session Multi-Objective Budget Optimization for Auctio...

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International Joint Conference on Neural Networks (IJCNN)

作者： Tang, Xiaoli Yu, Han Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore

ISBN: (纸本)9798350359329;9798350359312

Auction-based Federated Learning (AFL) has become a significant focus in recent years. Existing approaches for model users (MUs) in FL generally operate under the assumption that the entire set of essential data owners (DOs) must be gathered before the training process initiates. However, in practical scenarios, the MU can initiate the FL training process multiple times, gradually recruiting DOs across multiple FL training sessions. Current AFL methods are not equipped to handle such situations. The challenge of optimizing the AFL budget across multiple sessions and objectives remains unresolved. To address this gap, we propose the Efficient and Utility Return-Optimizing budget management strategy for MUs in Auction-based Federated Learning (EURO-AFL). By incorporating the hierarchical reinforcement learning framework, EURO-AFL concurrently optimizes inter-session budget pacing and intra-session budget allocation, with the dual objective of maximizing total utility while minimizing waiting time. Extensive experiments conducted on four real-world datasets illustrate the substantial advantages of EURO-AFL compared to five state-of-the-art baselines. It outperforms the best-performing baseline by 6.26%, 47.9% and 17.6% in terms of model accuracy, number of training sessions sustained, and training efficiency, respectively. To our best knowledge, EURO-AFL is the first multi-session multi-objective budget optimization approach designed for AFL MUs.

关键词： Auction-based federated learning multi-session budget pacing hierarchical reinforcement learning multi-objective optimization

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multi-objective Scheduling and Resource Allocation of Kubernetes Replicas Across the Compute Continuum 20

Multi-Objective Scheduling and Resource Allocation of Kubern...

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20th International Conference on Network and Service Management

作者： Di Cicco, Nicola Poltronieri, Filippo Santos, Jose Zaccarini, Mattia Tortonesi, Mauro Stefanelli, Cesare de Turck, Filip Politecn Milan Dept Elect Informat & Bioengn DEIB Milan Italy Univ Ferrara Distributed Syst Res Grp Ferrara Italy Univ Ghent IMEC IDLab Dept Informat Technol Ghent Belgium

ISBN: (纸本)9798331505158;9783903176669

Orchestrating microservice applications deployed on a federation of globally distributed Kubernetes clusters is a challenging and multifaceted optimization problem. It is not only computationally hard, but also requires balancing a delicate trade-off between competing performance metrics, such as latency, deployment cost, and service interruption frequency. Classical approaches in the literature merge multiple objectives into a single one via, e.g., linear combinations. However, in practice, it is complex to express a priori a quantitative preference between heterogeneous objectives, let alone with simple linear combinations. This paper adopts a more comprehensive approach leveraging proper multi-objective optimization (MOO), with the goal of producing multiple solutions from the Pareto Front. (PF). Therefore, the orchestrator can inspect a posteriori all possible "optimal" trade-offs and decide on the strategy that best tits their operating requirements. To solve the MOO problem, this paper adopts state-of-the-art multi-objective Evolutionary Algorithms and shows their effectiveness in solving the MOO problem. Illustrative results highlight the practical benefits of a MOO formulation, providing several tens of nondominated solutions and evenly covering the objectives' space.

关键词： Kubernetes Resource Allocation multi objective optimization ILP Evolutionary Algorithms Compute Continuum

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Chaos-enhanced multi-objective tunicate swarm algorithm for economic-emission load dispatch problem

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SOFT COMPUTING 2023年第9期27卷 5721-5739页

作者： Rizk-Allah, Rizk M. Hagag, Enas A. El-Fergany, Attia A. Menoufia Univ Fac Engn Basic Engn Sci Dept Shibin Al Kawm 32511 Egypt Zagazig Univ Fac Engn Elect Power & Machines Dept Zagazig 44519 Egypt

Climate change and environmental protection have a significant impact on thermal plants. So, the main principles of combined economic-emission dispatch (CEED) problem are indeed to reduce greenhouse gas emissions and fuel costs. Many approaches have demonstrated their efficacy in addressing CEED problem. However, designing a robust algorithm capable of achieving the Pareto optimal solutions under its multimodality and non-convexity natures caused by valve ripple effects is a true challenge. In this paper, chaos-enhanced multi-objective tunicate swarm algorithm (CMOTSA) for CEED problem. To promote the exploration and exploitation abilities of the basic tunicate swarm algorithm (TSA), an exponential strategy based on chaotic logistic map (ESCL) is incorporated. Based on ESCL in CMOTSA, it can improve the possibility of diversification feature to search different areas within the solution space, and then, gradually with the progress of iterative process it converts to emphasize the intensification ability. The efficacy of CMOTSA is approved by applying it to some of multi-objective benchmarking functions which have different Pareto front characteristics including convex, discrete, and non-convex. The inverted generational distance (IGD) and generational distance (GD) are employed to assess the robustness and the good quality of CMOTSA against some successful algorithms. Additionally, the computational time is evaluated, the CMOTSA consumes less time for most functions. The CMOTSA is applied to one of the practical engineering problems such as combined economic and emission dispatch (CEED) with including the valve ripples. By using three different systems (IEEE 30-bus with 6 generators system, 10 units system and IEEE 118-bus with 14 generating units), the methodology validation is made. It can be stated for the large-scale case of 118-bus systems that the results of the CMOTSA are equal to 8741.3 $/h for the minimum cost and 2747.6 ton/h for the minimum emission which

关键词： multi-objective optimization Chaos maps Chaotic multi-objective tunicate swarm algorithm Combined economic and emission dispatch Valve-point effects

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VALKYRIE: a suite of topology-aware clustering approaches for cloud-based virtual network services

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JOURNAL OF SUPERCOMPUTING 2023年第3期79卷 3298-3328页

作者： El Mansoum, Imane Lahlou, Laaziz Khasawneh, Fawaz A. Kara, Nadjia Edstrom, Claes Univ Quebec Dept Software Engn & IT Ecole Technol Super Montreal PQ Canada Ericsson Canada Mississauga ON Canada

Complex networks are practical tools for modeling, studying, and analyzing complex interactions between objects. These tools are essential in understanding applications, end-users, interactions between compute nodes, and their behaviors in computer networks. Computer networks are undergoing significant expansion due to the proliferation of network devices and compute nodes. One of the main challenges in computer networks is categorizing these compute nodes into clusters of connected compute nodes within these large-scale structures sharing similar features (e.g., Central Processing Unit, memory, disk storage). This paper proposes a set of novel, dynamic, and proactive topology-aware clustering approaches, namely, an Integer Linear Program, chemical reaction optimization, and a game theory approach that leverages the Irving algorithm, originally proposed to solve the stable roommate problem, to form clusters based on the compute nodes' features and their topological structures. Our proposed techniques are suggested to reduce the search space concerning Network Function Virtualization, Cloud-based Networks deployment, and to build on-demand clusters to meet their requirements. In this regard, the solutions aim to help decision-makers facing issues related to scalability and computational complexities of their mechanisms to deploy their cloud-based services effectively. Experimental results demonstrate the proposed approaches' effectiveness and suitability, given their polynomial-time complexities, making them easy to integrate into cloud providers' orchestration systems compared to K-Means and Density-based spatial clustering of applications with noise.

关键词： Network function virtualization Attributed network infrastructures Clustering multi-objective optimization Topological structure Attribute similarity

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Statistical learning and optimization of the helical milling of the biocompatible titanium Ti-6Al-7Nb alloy

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第3-4期125卷 1789-1813页

作者： da Costa, Tomas Barbosa Dutra Pereira, Robson Bruno Lauro, Carlos Henrique Brandao, Lincoln Cardoso Davim, J. Paulo Univ Fed Sao Joao del Rei Ctr Innovat Sustainable Mfg CIMS Dept Mech & Ind Engn 170 Frei Orlando Sq BR-36880000 Sao Joao Del Rei MG Brazil Univ Fed Sao Joao del Rei Ctr Innovat Modelling & Optimizat Syst CIMOS Dept Mech & Ind Engn 170 Frei Orlando Sq BR-36880000 Sao Joao Del Rei MG Brazil Univ Aveiro Dept Mech Engn Aveiro Portugal

Helical milling has been applied for hole-making in titanium alloys, especially in the Ti-6Al-4V alloy, considering the aims of the aeronautic, automobile, and other sectors. When considering hole-making in Ti-alloys for biomedical applications, few studies have been carried out. Besides, intelligent approaches for modeling and optimization of this process in these special alloys are demanded to achieve the best results in terms of hole surface quality, and productivity. This work presents an approach for modeling and optimization of helical milling for hole-making of Ti-6Al-7Nb biocompatible titanium alloy. The surface roughness of the holes was measured to quantify the hole quality. Principal component analysis was performed for dimensionality reduction of the roughness outputs. For modeling, a learning procedure was proposed considering polynomial response surface regression, tree-based methods, and support vector regression. Cross-validation is used for learning and model selection. The results pointed out that the support vector regression model was the best one. multi-objective evolutionary optimization was performed considering the support vector regression model and the deterministic model of the material removal rate. The Pareto set and the Pareto frontier were plotted and discussed concerning practical aspects of the helical milling process. The proposed learning and optimization approach enabled the achievement of the best results of the helical milling in the biocompatible Ti-6Al-7Nb alloy and can be applied to other intelligent manufacturing applications.

关键词： Helical milling Biocompatible titanium alloy Statistical learning Machine learning Support vector regression multi-objective evolutionary optimization

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