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Developing a sustainable forward supply chain configuration for construction industry under uncertainty condition: a case study

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CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2024年第4期26卷 1197-1225页

作者： Abdolazimi, Omid Entezari, Sarah Shishebori, Davood Ardakani, Majid Alimohammadi Kashef, Amirarash Mississippi State Univ Dept Ind & Syst Engn Mississippi State MS 39762 USA Yazd Univ Dept Ind Engn Yazd *** Iran Ardakan Univ Fac Engn Dept Ind Engn Ardakan Iran

Recently, the benefits of employing philosophical principles to optimize supply chains in the construction sector have been delved into by many researchers. This comprises three key segments: customers or employers, design, and construction. To address challenges and enhance the viability and competitiveness of the construction sector, the adoption of supply chain management has been proposed. This study develops a multi-objective mathematical model to design a construction supply chain, which includes a primary supplier and multiple customer projects. The primary goal is to ensure that the necessary materials are delivered at different intervals, considering each product's specific technical requirements and life cycles. It is crucial to prevent late deliveries, as they can lead to product wastage and substantially impact management decisions. Additionally, it is important to highlight that this study addresses the uncertainty in the supply chain, which further complicates the planning and decision-making processes. Accordingly, some related sensitivity analyses were done to reveal the profound influence of uncertainty on the proposed model, and its important uncertain parameters were specified. Next, the study employs the robust programming approach introduced by Bertsimas and Sim to tackle the uncertainty. To validate the proposed approach, it is implemented in a real-world case study. For small-size problems, the modified weighted Chebyshev method is used for solving the model. Then, the best-worst multi-criteria decision-making technique assesses the suggested model's ability to solve various problems in larger sizes. Subsequently, the study applies meta-heuristic algorithms like MOGA-II, MORDA, and MOSA to solve large-size problems. According to the outputs analysis, the MORDA algorithm executed better than the others. Ultimately, the study derives managerial insights based on the findings from the sensitivity analysis.

关键词： Sustainable construction supply chain Robust programming Multi-objective optimization meta-heuristic algorithms

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An enhanced exponential distribution optimizer and its application for multi-level medical image thresholding problems

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ALEXANDRIA ENGINEERING JOURNAL 2024年 93卷 142-188页

作者： Hashim, Fatma A. Hussien, Abdelazim G. Bouaouda, Anas Samee, Nagwan Abdel Abu Khurma, Ruba Alamro, Hayam Al-Betar, Mohammed Azmi Helwan Univ Fac Engn Helwan Egypt Middle East Univ MEU Res Unit Amman 11831 Jordan Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Fayoum Univ Fac Sci Faiyum Egypt Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Hassan II Univ Casablanca Fac Sci & Technol Casablanca Morocco Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Al Ahliyya Univ Fac Informat Technol Comp Sci Dept Amman Jordan Ajman Univ Coll Engn & Informat Technol Dept Informat Technol POB 346 Ajman U Arab Emirates Ajman Univ Artificial Intelligence Res Ctr POB 346 Ajman U Arab Emirates

In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO is a math-inspired optimizer that has many limitations in handling complex multi -modal problems. mEDO tries to solve these drawbacks using 2 operators: phasor operator for diversity enhancement and an adaptive p -best mutation strategy for preventing it converging to local optima. To validate the effectiveness of the suggested optimizer, a comprehensive set of comparative experiments using the CEC'2020 test suite was conducted. The experimental results consistently prove that the suggested technique outperforms its counterparts in terms of both convergence speed and accuracy. Moreover, the suggested mEDO algorithm was applied for image segmentation using the multi-threshold image segmentation method with Otsu's entropy, providing further evidence of its enhanced performance. The algorithm was evaluated by comparing its results with those of existing well-known algorithms at various threshold levels. The experimental results validate that the proposed mEDO algorithm attains exceptional segmentation results for various threshold levels.

关键词： Exponential distribution optimizer Multi-level thresholding meta-heuristic algorithms Image segmentation Otsu's method Global optimization

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FNN for Diabetic Prediction Using Oppositional Whale Optimization Algorithm

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IEEE ACCESS 2024年 12卷 20396-20408页

作者： Chatterjee, Rajesh Akhtar, Mohammad Amir Khusru Pradhan, Dinesh Kumar Chakraborty, Falguni Kumar, Mohit Verma, Sahil Abu Khurma, Ruba Garcia-Arenas, Maribel Usha Martin Univ Fac Comp & IT Ranchi 835103 India Dr BC Roy Engn Coll Durgapur 713206 India MIT Art Design & Technol Univ Dept IT Pune 412201 India Chandigarh Grp Coll Dept Comp Sci & Engn Mohali 140307 Punjab India Middle East Univ Fac Informat Technol MEU Res Unit Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Granada Dept Comp Engn Automat & Robot Granada 18071 Spain Univ Granada Ctr Invest Tecnol Informac & Comunicac Granada 18071 Spain

The medical field is witnessing rapid adoption of artificial intelligence (AI) and machine learning (ML), revolutionizing disease diagnosis and treatment management. Researchers explore how AI and ML can optimize medical decision-making, promising to transform healthcare. Feed Forward Neural Networks (FNN) are widely used to create predictive disease models, cross-validated by medical experts. However, complex medical data like diabetes leads to multi-modal search spaces prone to local minima, affecting optimal solutions. In this study, we focus on optimizing a diabetes dataset from the Pima Indian community, evaluating decision-making performance in diabetes management. Employing multimodal datasets, we compare various optimization algorithms, including the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO). The test results encompass essential metrics like best-fit value, mean, median, and standard deviation to assess the impact of different optimization techniques. The findings highlight the superiority of the Oppositional Whale Optimization Algorithm (OWOA) over other methods employed in our research setup. This study demonstrates the immense potential of AI and metaheuristic algorithms to revolutionize medical diagnosis and treatment approaches, paving the way for future advancements in the healthcare landscape. Results reveal the superiority of OWOA over other methods. AI and metaheuristics show tremendous potential in transforming medical diagnosis and treatment, driving future healthcare advancements.

关键词： Feed forward neural network (FNN) oppositional learning artificial intelligence meta-heuristic algorithms whale optimization algorithm (WOA)

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Comprehensive comparisons of improved incremental conductance with the state-of-the-art MPPT Techniques for extracting global peak and regulating dc-link voltage

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ENERGY REPORTS 2024年 11卷 1590-1610页

作者： Al-Wesabi, Ibrahim Fang, Zhijian Farh, Hassan M. Hussein Al-Shamma'a, Abdullrahman A. Al-Shaalan, Abdullah M. China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Imam Mohammad Ibn Saud Islamic Univ Coll Engn Elect Engn Dept Riyadh Saudi Arabia King Saud Univ Coll Engn Elect Engn Dept Riyadh Saudi Arabia

The grid -connected converters with two stages are frequently used in renewable energy technologies and applications. Designing the dc -link voltage management scheme is challenging due to the ripples and fluctuations at the dc -link voltage in the grid -connected photovoltaic battery energy storage systems (PV/BES), which results in significant harmonic distortion, ripples, and a decreased power factor. The large electrolytic capacitors, that have a limited lifespan, are frequently utilized at the dc -link in order to decrease such ripples and oscillations. To maximize the lifespan of power converters, It is crucial to substitute the large capacitors with small capacitors, that have a very long lifespan and its small capacitance cause a significant fluctuations in the output current and overvoltage due to their large ripples on the dc -link voltage. In this paper, to extract the global peak (GP) of PV system without any ripples around the GP and Improving the dynamic performance of the entire system. First, an improved incremental conductance (IINC) technique compared to state-of-the-art MPPT techniques are proposed. Second, to stabilize the dc -link voltage, the PV array's power, and the battery's state of charge (SOC) are both controlled by the dc -link control system using a straightforward and novel d -q current regulation technique. In this instance, the low sampling frequency of the dc -link controller allows for a cost-effective solution. For this reason, the proposed IINC catches the GP with advantages of quick convergence, improved tracking effectiveness, and decreased tracking time. Moreover, the IINC is utilized in a DC -DC step-up converter to boost the output power and voltage of solar panels while lowering switching and conduction losses. In order to provide the grid system with a better AC output of higher quality, the amount of harmonics is therefore suppressed using the d -q innovative control. The simulation and experimental outcomes revealed the

关键词： Dc-link stablization PV/BES grid-connected system SSA IINC meta-heuristic algorithms Small film capacitor

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Artificial intelligence-based optimization techniques for optimal reactive power dispatch problem: a contemporary survey, experiments, and analysis

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第1期58卷 1-48页

作者： Abdel-Basset, Mohamed Mohamed, Reda Hezam, Ibrahim M. Sallam, Karam M. Alshamrani, Ahmad M. Hameed, Ibrahim A. Zagazig Univ Zagazig 44519 Sharqiyah Egypt Univ Sharjah Coll Comp & Informat Dept Comp Sci Sharjah U Arab Emirates Univ Canberra Fac Sci & Technol Sch IT & Syst Canberra ACT Australia King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia Norwegian Univ Sci & Technol NTNU Dept ICT & Nat Sci Alesund Norway

The optimization challenge known as the optimal reactive power dispatch (ORPD) problem is of utmost importance in the electric power system owing to its substantial impact on stability, cost-effectiveness, and security. Several metaheuristic algorithms have been developed to address this challenge, but they all suffer from either being stuck in local minima, having an insufficiently fast convergence rate, or having a prohibitively high computational cost. Therefore, in this study, the performance of four recently published metaheuristic algorithms, namely the mantis search algorithm (MSA), spider wasp optimizer (SWO), nutcracker optimization algorithm (NOA), and artificial gorilla optimizer (GTO), is assessed to solve this problem with the purpose of minimizing power losses and voltage deviation. These algorithms were chosen due to the robustness of their local optimality avoidance and convergence speed acceleration mechanisms. In addition, a modified variant of NOA, known as MNOA, is herein proposed to further improve its performance. This modified variant does not combine the information of the newly generated solution with the current solution to avoid falling into local minima and accelerate the convergence speed. However, MNOA still needs further improvement to strengthen its performance for large-scale problems, so it is integrated with a newly proposed improvement mechanism to promote its exploration and exploitation operators;this hybrid variant was called HNOA. These proposed algorithms are used to estimate potential solutions to the ORPD problem in small-scale, medium-scale, and large-scale systems and are being tested and validated on the IEEE 14-bus, IEEE 39-bus, IEEE 57-bus, IEEE 118-bus, and IEEE 300-bus electrical power systems. In comparison to eight rival optimizers, HNOA is superior for large-scale systems (IEEE 118-bus and 300-bus systems) at optimizing power losses and voltage deviation;MNOA performs better for medium-scale systems (IEEE 57-bus);

关键词： meta-heuristic algorithms Optimal reactive power dispatch Systems of non-linear equations Electric power systems

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Jaya clustering-based algorithm for multiobjective IoV network routing optimization

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SOFT COMPUTING 2024年第6期28卷 5639-5665页

作者： Dalbah, Lamees Mohammad Al-Betar, Mohammed Azmi Awadallah, Mohammed A. Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates

The Internet of Vehicles (IoV) is an Intelligent Transportation System, which in its turn is an application of the Internet of Things. The IoV is a network of connected vehicles that sends/receives messages. However, the moving nature of the vehicles in IoV networks raises a dynamic topology issue. Therefore, establishing effective and dependable communication pathways among vehicular nodes, contingent upon traffic density conditions, presents a growing challenge. As a result, the main contribution of this paper is to propose an efficient network optimization system involving a cluster-based network routing optimizer. The proposed method is a metaheuristic algorithm adapted as a clustering algorithm. For this purpose, the JAYA Algorithm (JAYA) was utilized and integrated with two clustering concepts extracted from K-means Clustering and Automatic Clustering, resulting in proposing JAYA Clustering Algorithm for IoV (JAYACIoV). Subsequently, the performance of the proposed algorithm was investigated utilizing 176 network testing scenarios, and extensive comparative evaluations were conducted against five algorithms from the literature. The results confirmed the superiority of the proposed system with a percentage of 64.82%, where the algorithms were ranked according to the percentage achievement of best results.

关键词： Internet of Things (IoT) Intelligent Transportation System (ITS) Internet of Vehicles (IoV) IoV network routing problem (IoV-NRP) Clustering meta-heuristic algorithms

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Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples

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ENGINEERING WITH COMPUTERS 2021年第2期37卷 1133-1145页

作者： Sun, Lei Koopialipoor, Mohammadreza Armaghani, Danial Jahed Tarinejad, Reza Tahir, M. M. Hohhot Vocat Coll Dept Civil Engn & Architecture Hohhot 010051 Peoples R China Inner Mongolia Agr Univ Water Conservancy & Civil Engn Coll Hohhot 010018 Peoples R China Amirkabir Univ Technol Fac Civil & Environm Engn Tehran 15914 Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Univ Tabriz Fac Civil Engn 29 Bahman Blvd Tabriz 51666 Iran Univ Teknol Malaysia Fac Engn UTM Construct Res Ctr Sch Civil EngnISIIC Skudai 81310 Johor Malaysia

The successful use of fly ash (FA) and silica fume (SF) materials has been reported in the design of concrete samples in the literature. Due to the benefits of using these materials, they can be utilized in many industrial applications. However, the proper use of them in the right mixes is one of the important factors with respect to the strength and weight of concrete. Therefore, this paper develops relationships based on meta-heuristic (MH) algorithms (artificial bee colony technique) to evaluate the compressive strength of concrete specimens using laboratory experiments. A database comprising silica fume replacement ratio, fly ash replacement ratio, total cementitious material, water content coarse aggregate, high-rate water-reducing agent, fine aggregate, and age of samples, as model inputs, was used to evaluate and predict the compressive strength of concrete samples. Developed models of the MH technique created relationships between the mentioned parameters. In the new models, the influence of each parameter on the compressive strength was determined. Finally, using the developed model, optimum conditions for compressive strength of concrete samples were presented. This paper demonstrated that the MH algorithms are able to develop relationships that can serve as good substitutes for empirical models.

关键词： Compressive strength of concrete meta-heuristic algorithms Artificial bee colony Optimization technique

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QSAR modelling of enzyme inhibition toxicity of ionic liquid based on chaotic spotted hyena optimization algorithm

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SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024年第9期35卷 757-770页

作者： Alharthi, A. M. Al-Thanoon, N. A. Al-Fakih, A. M. Algamal, Z. Y. Taif Univ Turabah Univ Coll Dept Math Taif Saudi Arabia Univ Mosul Dept Operat Res & Intelligent Tech Mosul Iraq Univ Teknol Malaysia Fac Sci Dept Chem Johor Baharu Malaysia Univ Mosul Dept Stat & Informat Mosul Iraq

Ionic liquids (ILs) have attracted considerable interest due to their unique properties and prospective uses in various industries. However, their potential toxicity, particularly regarding enzyme inhibition, has become a growing concern. In this study, a QSAR model was proposed to predict the enzyme inhibition toxicity of ILs. A dataset of diverse ILs with corresponding toxicity data against three enzymes was compiled. Molecular descriptors that capture the physicochemical, structural, and topological properties of the ILs were calculated. To optimize the selection of descriptors and develop a robust QSAR model, the chaotic spotted hyena optimization algorithm, a novel nature-inspired metaheuristic, was employed. The proposed algorithm efficiently searches for an optimal subset of descriptors and model parameters, enhancing the predictive performance and interpretability of the QSAR model. The developed model exhibits excellent predictive capability, with high classification accuracy and low computation time. Sensitivity analysis and molecular interpretation of the selected descriptors provide insights into the critical structural features influencing the toxicity of ILs. This study showcases the successful application of the chaotic spotted hyena optimization algorithm in QSAR modelling and contributes to a better understanding of the toxicity mechanisms of ILs, aiding in the design of safer alternatives for industrial applications.

关键词： QSAR model meta-heuristic algorithms spotted hyena optimization chaotic maps ionic liquids

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A survey study on task scheduling schemes for workflow executions in cloud computing environment: classification and challenges

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JOURNAL OF SUPERCOMPUTING 2024年第7期80卷 9384-9437页

作者： Shirvani, Mirsaeid Hosseini Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran

Several real-world scientific and industrial workflow applications adopt elastic and cost-efficient cloud services to fulfill their requirement. There are two stakeholders in the system, namely the user and the provider each of which tries to maximize its profit and at the same time minimize their possible overall costs. Since task scheduling algorithms for workflow executions determine which task should be possibly executed on what resources, they have a drastic impact on both the user's quality of experiences and on the underlying resource utilization. Indeed, an efficient task scheduling algorithm can meet service-level agreement (SLA) for both sides. For the sake of the importance of the issue, this survey presents a subjective taxonomy on the task scheduling schemes in the literature for workflow executions in cloud computing environments to be a guideline for future improvement. It classifies the literature based on the proposed algorithms in the literature, objectives, stakeholders' requirements, and evaluation metrics. This survey highlights research trends, challenges, research gaps, potential solutions, and future direction. It can also pave the way for further processing, improvement and strengthening of existing approaches or devising novel ones for interested researchers in the field of task scheduling problems.

关键词： Cloud computing Workflow application Task scheduling algorithm Directed acyclic graph (DAG) List schedulers meta-heuristic algorithms

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A Review of Sustainable Supply Chain Optimization Utilizing metaheuristic algorithms: A Comparison from Both Chinese and International Perspectives

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PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2025年 1-29页

作者： Xiao, Qiang Shi, Wei Zhang, Yuanyuan Lanzhou Jiaotong Univ Sch Econ & Management Lanzhou Peoples R China Lanzhou Jiaotong Univ Northwest Traff Econ Res Ctr Lanzhou Peoples R China

meta-heuristic algorithms are favored for their superior performance in sustainable supply chain management (SSCM) optimization. Despite extensive research in this field, there remains a lack of systematic reviews specifically addressing the application of meta-heuristic algorithms in SSCM optimization in China, as well as a comparative analysis with international research. To address this research gap, the present study employs bibliometric and content analysis methodologies, integrating 41 in the CNKI academic journal database indexed documents with 238 in the Web of Science core collection indexed documents, to systematically synthesize existing research trajectories and construct a theoretical framework. This study systematically compares the performance of major meta-heuristic algorithms and critically analyzes their limitations and challenges in the context of SSCM optimization. The results indicate the following: The number of published Chinese and international literature on this topic is increasing. Genetic algorithm (GA) and non-dominant sorting Genetic Algorithm II (NSGA-II) are the most commonly used meta-heuristic algorithms. According to key words cluster analysis, the primary research topics include sustainability management, environmentally friendly supply chain, supply chain collaboration and integration, meta-heuristic algorithms and related fields. The most commonly used hybrid strategies are NSGA-II and Variable Neighborhood Search (VNS), GA and Particle Swarm Optimization (PSO), with frequencies of 4 and 6 respectively. These algorithms are primarily employed to address practical issues such as supply chain network design, vehicle routing problems, and scheduling problems. The shortcomings of the existing research are discussed in detail, and on this basis, the specific suggestions for future research are discussed.

关键词： Sustainable Supply Chain Management Optimization meta-heuristic algorithms Bibliometrics Content analysis Sustainable Development

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