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Population-based and hybrid heuristic approaches for the bi-objective sustainable multi-vehicle traveling purchaser problem

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2025年

作者： Cheaitou, Ali Hamdan, Sadeque Quteineh, Heba Alsyouf, Imad Shikhli, Amir Univ Sharjah Coll Engn Sharjah U Arab Emirates Bangor Univ Bangor Business Sch Bangor LL57 2DG Gwynedd Wales Univ Sharjah Res Inst Sci & Engn Sharjah U Arab Emirates

This article examines a retailer's challenge in selecting sustainable suppliers, assigning orders, routing multiple capacitated vehicles to collect purchased products within a restricted travel distance, and choosing the vehicles' speed levels. It presents a bi-objective mixed-integer linear programming model that allows varying speed levels between arcs of the asymmetric network connecting suppliers and between vehicles on the same arc. Suppliers have different capacities and can supply various products to meet deterministic demands, with total supply capacity exceeding demand for each product. The retailer aims to select the optimal suppliers to minimize variable and fixed procurement costs, fuel and transportation costs, and CO2 social costs while maximizing the social and environmental worth of the procured goods. The study extends the pollution routing problem and the traveling purchaser problem by integrating environmental and social sustainability and accounting for the social cost of CO2 emissions. An exact approach and two suggested heuristic algorithms are used to solve the model. A comprehensive numerical analysis demonstrates that the proposed solution algorithms reliably approach the optimum within a practical amount of computation time for the instances considered.

关键词： Supplier selection sustainability social cost of CO2 speed optimization population-based heuristic hybrid heuristic

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Advanced Control Technique for Optimal Power Management of a Prosumer-Centric Residential Microgrid

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IEEE ACCESS 2024年 12卷 163819-163855页

作者： Gbadega, Peter Anuoluwapo Sun, Yanxia Balogun, Olufunke Abolaji Univ Johannesburg Dept Elect & Elect Engn Sci Auckland Pk Kingsway Campus Johannesburg 2006 South Africa

Advanced control approaches are needed for prosumer-centric residential microgrids to manage power optimally due to the shift to sustainable energy systems In residential contexts where prosumers are important, this study investigates novel approaches to improve renewable energy sources' efficiency, dependability, and integration. Prosumers who produce and consume energy introduce unique challenges and opportunities for managing power flows and maintaining grid stability. This study proposes a smart Home Energy Management System (HEMS) designed to reduce daily energy costs for prosumers while offering ancillary services to the grid. The prosumer's residence includes an electric vehicle (EV), a home battery pack, a photovoltaic (PV) system, a mix of flexible and critical electrical loads, and heating and cooling loads. The HEMS can operate independently by disconnecting from the main grid. Additionally, it supports grid frequency management by considering the operational flexibility of the controlled power sources and loads. To solve the multi-objective power management optimization problem, the Teaching-Learning-Based optimization (TLBO) algorithm was implemented. This algorithm optimizes day-ahead power scheduling by considering temperature forecasts, energy prices, solar irradiance, system constraints, and user preferences. A residential load forecasting system, which combines k-means clustering and artificial neural networks (ANNs), was developed and implemented to predict residential loads efficiently. Various simulated scenarios illustrate how the HEMS may save costs while guaranteeing that all power component operating limitations are reached, and user comfort and satisfaction are maintained. To demonstrate the capabilities and cost-effectiveness of the suggested HEMS, several scenarios are simulated and the outcomes of the day-ahead optimization carried out by the HEMS for the prosumer residence are examined. Furthermore, it is verified that the system can su

关键词： Microgrids Renewable energy sources Energy storage optimization Power system management Power system stability Photovoltaic systems Distributed power generation Reliability Vehicle-to-grid Distributed energy resources (DERs) home energy management system (HEMS) residential microgrid prosumers teaching-learning-based optimization (TLBO)

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A review of optimization techniques and algorithms used for FRP applications in civil engineering

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Journal of Engineering and Applied Science 2023年第1期70卷 61页

作者： Ramadan, Amany S. Elgendi, Elbadr O. Alexandria Egypt

optimization problems aim to minimize or maximize an objective function while fulfilling related constraints. This objective function may be a single or multi-objective optimization. Many studies have been conducted on using these optimization problems in civil and construction engineering, especially for the various machine learning techniques and algorithms that have been developed for fiber reinforced polymer (FRP) applications in the rehabilitation and design of RC structures. FRP is considered the most effective and superior technique for strengthening and retrofitting due to its significant benefits over traditional methods, which have numerous drawbacks, as well as the importance of structural strengthening as a cost-effective and practical option. In this research, an insight into how to apply algorithms and machine learning approaches to optimize FRP applications in civil and construction engineering is presented, as well as a detailed analysis of the various optimization strategies used and their findings. A total of 18 case studies from previous research were discussed and critically evaluated, and they were categorized into six groups according to the algorithm or machine learning technique utilized. Based on the case studies investigated in this study, the genetic algorithm was found to be the optimal algorithm utilized for optimizing FRP applications. The result of this research provides a useful guideline for future researchers and specialists. © 2023, The Author(s).

关键词： Machine learning

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Modeling and optimizing a broadband sound absorber consisting of a granular activated carbon stack backed by a poro-elastic layer

NOISE CONTROL ENGINEERING JOURNAL

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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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A novel combined forecasting system based on advanced optimization algorithm-A study on optimal interval prediction of wind speed

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ENERGY 2023年 264卷

作者： Li, Jingrui Wang, Jiyang Li, Zhiwu Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

Wind speed forecasting is becoming increasingly crucial for an environmentally friendly and sustainable economy because of the renewability and benefits of wind energy. Currently, many scholars have proposed various approaches to forecasting wind speed;however, due to the limitations of point prediction and drawbacks of traditional individual methods, it is challenging to acquire satisfactory results. Compared with the previous articles, this paper combines a data preprocessing strategy by fuzzification, interval estimation, and advanced optimization methods to enhance the accuracy of wind speed forecasting. Moreover, the weight coefficient allocated by a multi-objective algorithm is proved to reach Pareto optimal solution in theory. Based on the comparative experiments and discussion of the performance of the developed combined forecasting system and other control groups, it is revealed that the combined system not only outperforms for predicting wind speed with higher accuracy and stability but also enables a valid assessment of uncertainty than the former studies. It can be convinced that the developed predicting system is an appropriate and efficient tool for further practical applications in energy systems.

关键词： Wind speed forecasting Data preprocessing technique Combined system Whale optimization algorithm

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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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