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Stochastic flexible flow shop scheduling problem with limited buffers and fixed interval preventive maintenance: a hybrid approach of simulation and metaheuristic algorithms

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2019年第6期95卷 509-528页

作者： Rooeinfar, R. Raissi, S. Ghezavati, V. R. Islamic Azad Univ South Tehran Branch Sch Ind Engn Tehran Iran

This study focused on the uncertain flexible flow shop scheduling problem with limited buffers when preventive maintenance is applied at fixed intervals. This issue has not been addressed in spite of widespread applications, due to complexity arising in solving such a stochastic decision making problem. To this aim, a novel optimization model is presented along with two types of solving methods using metaheuristic algorithms with and without a computer simulation model. The proposed hybrid method, named HSIM-META, integrates the computer simulation model along with the three most common metaheuristic algorithms, i.e., genetic algorithm (GA), simulated annealing (SA) algorithm, and particle swarm optimization (PSO), which offer better solution quality according to the literature. For this purpose, the simulation outputs are applied as an initial population for the tuned metaheuristic parameters to look for the next improved solution by investigating different approaches. Different numerical examples are discussed to examine the performance of the proposed method. The computational results of the proposed method, including hybrid simulation with GA (HSIM-GA), SA (HSIM-SA), and PSO (HSIM-PSO), are compared with the just applying GA, SA, and PSO. The results reveal that the suggested method acts more efficiently in terms of accuracy and speed in solving the problem.

关键词： Flexible flow shop stochastic scheduling limited buffers preventive maintenance metaheuristic algorithms optimization via simulation

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CHAOS THEORY, ADVANCED metaheuristic algorithms AND THEIR NEWFANGLED DEEP LEARNING ARCHITECTURE OPTIMIZATION APPLICATIONS: A REVIEW

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2024年第3期32卷 2430001-2430001页

作者： Akgul, Akif Karaca, Yell'z Pala, Muhammed Ali Cimen, Murat Erhan Boz, Ali Fuat Yildiz, Mustafa Zahid Hitit Univ Fac Engn Dept Comp Engn Corum Turkiye Univ Massachusetts Chan Med Sch Worcester MA 01655 USA Sakarya Univ Appl Sci Fac Technol Dept Elect & Elect Engn Sakarya Turkiye

metaheuristic techniques are capable of representing optimization frames with their specific theories as well as objective functions owing to their being adjustable and effective in various applications. Through the optimization of deep learning models, metaheuristic algorithms inspired by nature, imitating the behavior of living and non-living beings, have been used for about four decades to solve challenging, complex, and chaotic problems. These algorithms can be categorized as evolution-based, swarm-based, nature-based, human-based, hybrid, or chaos-based. Chaos theory, as a useful approach to understanding neural network optimization, has the basic idea of viewing the neural network optimization as a dynamical system in which the equation schemes are utilized from the space pertaining to learnable parameters, namely optimization trajectory, to itself, which enables the description of the evolution of the system by understanding the training behavior, which is to say the number of iterations over time. The examination of the recent studies reveals the importance of chaos theory, which is sensitive to initial conditions with randomness and dynamical properties that are principally emerging on the complex multimodal landscape. Chaotic optimization, in this regard, accelerates the speed of the algorithm while also enhancing the variety of movement patterns. The significance of hybrid algorithms developed through their applications in different domains concerning real-world phenomena and well-known benchmark problems in the literature is also evident. metaheuristic optimization algorithms have also been applied to deep learning or deep neural networks (DNNs), a branch of machine learning. In this respect, the basic features of deep learning and DNNs and the extensive use of metaheuristic algorithms are overviewed and explained. Accordingly, the current review aims at providing new insights into the studies that deal with metaheuristic algorithms, hybrid-based metaheu

关键词： metaheuristic algorithms Chaos-based metaheuristic algorithms Evolution-based metaheuristic algorithms Swarm-based metaheuristic algorithms Nature-based metaheuristic algorithms Human-based metaheuristic algorithms Hybrid metaheuristic algorithms Architecture Optimization Deep Learning Engineering Applications Natural Mathematics Engineering Mathematics Applicability-based optimization Chaos Theory

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A state-dependent M/M/1 queueing location-allocation model for vaccine distribution using metaheuristic algorithms

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第4期10卷 1507-1530页

作者： Hirbod, Fatemeh Eshghali, Masoud Sheikhasadi, Mohammad Jolai, Fariborz Aghsami, Amir Univ Tehran Coll Engn Sch Ind Engn Tehran 111554563 Iran Sharif Univ Technol Dept Ind Engn Tehran 111551639 Iran KN Toosi Univ Technol Sch Ind Engn Tehran 158754416 Iran

Controlling and maintaining public health in the face of diseases necessitates the effective implementation of response strategies, including the distribution of vaccines. By distributing vaccines, vulnerable populations can be targeted, individuals can be protected, and the spread of diseases can be minimized. However, managing vaccine distribution poses challenges that require careful consideration of various factors, including the location of distribution facilities. This paper proposes a novel model that combines location-allocation problems with queueing systems methodologies to optimize the efficiency of vaccine distribution. The proposed model considers factors such as uncertain demand, varying service rates, depending on the system state. Its primary objective is to minimize total costs, which encompass the establishment and adjustment of the service mechanism, travel times, and customer waiting time. To forecast customer demand rates, the model utilizes time-series techniques, specifically the seasonal Autoregressive Integrated Moving Average model. In order to tackle large-scale problems, a total of 16 newly developed metaheuristic algorithms are employed, and their performance is thoroughly evaluated. This approach facilitates the generation of solutions that are nearly optimal within a reasonable timeframe. The effectiveness of the model is evaluated through a real-life case study focused on vaccination distribution in Iran. Furthermore, a comprehensive sensitivity analysis is conducted to demonstrate the practical applicability of the proposed model. The study contributes to the advancement of robust decision-making frameworks and provides valuable insights for addressing location-related challenges in health systems.

关键词： vaccine distribution location-allocation queueing systems metaheuristic algorithms sensitivity analysis decision-making frameworks

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Connection topology optimization of steel moment frames using metaheuristic algorithms

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ENGINEERING STRUCTURES 2015年 100卷 276-292页

作者： Alberdi, Ryan Murren, Patrick Khandelwal, Kapil Univ Notre Dame Dept Civil & Env Engg & Earth Sci Notre Dame IN 46556 USA

This paper details the optimization of steel moment frames in which, in addition to the beam and column sections, the beam-to-column connection type is also variable. In these connection topology optimizations, the optimal trade-off between the lateral stiffness provided by moment connections and lower cost of pin connections is determined by identifying the most efficient arrangement of moment connections. Four metaheuristic algorithms are used to optimize two different frames and their performance is compared. Results indicate that the cost of a frame is minimized when moment connections are arranged in a manner that resembles a global structural system. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Steel frame optimization metaheuristic algorithms Connection topology optimization Optimum design Structural systems

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Comparison of robustness of metaheuristic algorithms for steel frame optimization

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ENGINEERING STRUCTURES 2015年 102卷 40-60页

作者： Alberdi, Ryan Khandelwal, Kapil Univ Notre Dame Dept Civil & Env Engn & Earth Sci Notre Dame IN 46556 USA

In this study the robustness of six metaheuristic algorithms - ant colony optimization, genetic algorithm, harmony search, particle swarm optimization, simulated annealing and tabu search - and their three improved variants - design driven harmony search, adaptive harmony search and improved simulated annealing - are compared, and the characteristics that affect algorithmic robustness are investigated. Algorithmic robustness is defined as the ability of an algorithm to consistently converge to low cost designs regardless of the variable space and irrespective of the initial starting point. To this extent, the variable spaces present in steel frame design optimization problems are studied and two unique challenges of these spaces are identified and explained. Five benchmark steel frame designs, including moment and braced frames, are presented to serve as examples on which the algorithms can be tested, and the robustness of the algorithms is investigated using these representative designs. An in-depth discussion on the characteristics of metaheuristic algorithms that make them successful is presented. It is shown that the algorithms that exhibit robustness are those that incorporate intensification and diversification in ways that can effectively navigate the large variable spaces present in steel frame design optimization problems. The most robust algorithms are found to be design driven harmony search and tabu search. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Structural optimization metaheuristic algorithms Discrete optimization Steel moment frames Steel braced frames

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Reduce the construction cost of a 7-story RC public building with metaheuristic algorithms

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ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT 2024年第2期20卷 214-229页

作者： Aslay, Semi Emrah Dede, Tayfun Erzincan Binali Yildirim Univ Dept Civil Engn Erzincan Turkiye Karadeniz Tech Univ Dept Civil Engn Trabzon Turkiye Erzincan Binali Yildirim Univ Dept Civil Engn TR-24100 Erzincan Turkiye

In recent years, a few studies have been working on the optimization of 3D reinforced concrete structures. The number of research on this subject is increasing day by day. However, in these studies, the real world-structure is generally not used;moreover, only regular structures are used, and RC shear walls do not continue on all floors. Therefore, 3D evaluation of the optimization of real-world RC structures is possible for a small number of building types. There is a need for 3D optimization studies that can be used for all building types. In this study, 3D optimization of a real-world reinforced concrete structure is carried out. The sample structure used in the paper is not only a regular reinforced concrete-framed structure, unlike other studies, but also a RC framed structure with RC shear walls continuing on each floor. In the numerical example, the effect of the RC shear walls continuing on each floor is applied in the analysis, and the design processes of the construction projects on a real-world building and the quantity-cost analysis are made together. All optimization processes are directed automatically using the ACDOS programming, which is developed by the authors. ACDOS can take into account the commercial program named Etabs to make design of construction and the metaheuristic algorithms to reduce the total cost of construction. The results obtained from this study showed that the cost (concrete-rebar-formwork) of the 7-story public dormitory building was reduced by 9.18% when compared with its real-world application.

关键词： Construction cost optimization Jaya metaheuristic algorithms

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Analysis and prediction of diaphragm wall deflection induced by deep braced excavations using finite element method and artificial neural network optimized by metaheuristic algorithms

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年第0期221卷 108335-108335页

作者： Yong, Weixun Zhang, Wengang Hoang Nguyen Xuan-Nam Bui Choi, Yosoon Trung Nguyen-Thoi Zhou, Jian Trung Tin Tran Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Kunming Prospecting Design Inst China Nonferrous Kunming 650051 Yunnan Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Hanoi Univ Min & Geol Min Fac Dept Surface Min 18 Pho Vien Hanoi 100000 Vietnam Hanoi Univ Min & Geol Innovat Sustainable & Responsible Min ISRM Grp 18 Pho Vien Hanoi 100000 Vietnam Pukyong Natl Univ Dept Energy Resources Engn Busan 48513 South Korea Ton Duc Thang Univ Inst Computat Sci Div Computat Math & Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam FPT Univ Dept Informat Technol Danang 550000 Vietnam

The construction of metropolises in smart cities is the trend of developed countries. However, it may cause damages to the surrounding structures. For this reason, the diaphragm wall has been applied to prevent the deformation or collapse of the surrounding structures. Diaphragm walls can be deflected due to the swelling pressure or other geotechnical properties during construction. Therefore, the accurate prediction of diaphragm wall deflection (DWD) is challenging in construction aiming to ensure the safety of the surrounding structures. This study is, therefore, to propose two intelligent models for predicting DWD induced by deep braced excavations based on finite element method (FEM) and metaheuristic algorithms. Accordingly, a total of 1120 finite elements were analyzed to investigate the behaviors of DWD. Finally, a neural network with multiple layer perceptron (MLP) was optimized by two metaheuristic algorithms for predicting DWD, including whale optimization (WO) and Harris hawks optimization (HHO), called MLP-HHO and MLP-WO, respectively. The results indicated that the proposed MLP-HHO and MLP-WO provided high accuracy in predicting DWD. A comparison of the proposed models in this study and previous studies was also discussed to highlight the superiority of the proposed MLP-HHO and MLP-WO models.

关键词： System safety Diaphragm wall Soft clay Braced excavation Artificial neural network metaheuristic algorithms

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Metamodel assisted optimization of glued laminated timber beams by using metaheuristic algorithms

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 79卷 129-141页

作者： Pech, Sebastian Kandler, Georg Lukacevic, Markus Fuessl, Josef Vienna Univ Technol Inst Mech Mat & Struct Karlspl 13 A-1040 Vienna Austria

An efficient use of the raw material in glued laminated timber (GLT) beams is commonly achieved by inserting lamellas of lower quality in less stressed areas, usually in the middle of a beam. But, this rather simple method leaves room for improvement. In particular, the morphology of a board and its location in the beam setup is significant, since only this information and the actual loading situation allows a proper evaluation of weaknesses. Therefore, a new optimization method was developed, able to take mechanical property distributions as well as the occurring stress states within each wooden board into account. Subsequent to an automatic knot reconstruction and determination of effective local stiffness distributions of all boards, the beams are analyzed using a finite element (FE) model. This information is further exploited to find optimal beam setups out of a sample of boards. However, as the complexity of this optimization task quickly increases with the number of boards, metaheuristic optimization algorithms were developed. Additionally, the evaluation of the computationally expensive FE model is bypassed by a metamodel, capable of approximating the desired performance parameter of any beam. Comparing the various optimization approaches to common GLT beam production methods, maximum deflection can be reduced by 15%-20%.

关键词： WOODEN beams MATHEMATICAL optimization metaheuristic algorithms MECHANICAL behavior of materials FINITE element method STIFFNESS (Mechanics)

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Predicting clay compressibility for foundation design with high reliability and safety: A geotechnical engineering perspective using artificial neural network and five metaheuristic algorithms

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 243卷

作者： Jiadong, Qiu Ohl, Joy P. Tran, Trung-Tin Univ South China Sch Resources Environm & Safety Engn Hengyang 421001 Peoples R China NanoStructuraCivis Lab Adv Smart Mat Civil Engn Tokyo Japan Swinburne Vietnam FPT Univ Dept Informat Technol Da Nang Vietnam

This study aims at predicting clay compressibility (clay compressibility) for foundation design with unprecedented reliability and safety. To achieve this goal, we set forth several scientific objectives to enhance the ac-curacy of clay compressibility predictions and ensure the robustness of our models. Firstly, we explored an updated dataset comprising diverse clay types, focusing on the intricate relationship between clay compressibility and essential soil properties. By incorporating particle size distribution, the ratio between liquid limit (wLL) and plasticity index (PI), and the ratio between void ratio (eLL) and wLL, our objective was to gain a comprehensive understanding of clay behavior, facilitating more accurate predictions. Secondly, we employed Artificial Neural Networks (ANNs) as our primary modeling tool. ANNs, with their capacity to discern complex patterns within data, formed the foundation of our predictive model. Our scientific objective was to harness the power of ANNs to capture subtle relationships among input variables, thereby ensuring a robust foundation for clay compressibility predictions. To further refine our predictions, our third objective was to optimize the ANN model. We utilized five distinct metaheuristic algorithms-Bird Swarm Algorithm (BSA), Dragonfly Optimization (DO), Elephant Herding Optimization (EHO), Jaya Algorithm (JA), and Whale Optimization Algorithm (WOA). Our aim was to fine-tune the ANN model's parameters using these algorithms, capitalizing on their strengths to achieve superior predictive performance. Additionally, our study aimed to pioneer the development and eval-uation of hybrid models for forecasting clay compressibility. By integrating the ANN model with the meta-heuristic algorithms (BSA-ANN, DO-ANN, EHO-ANN, JA-ANN, and WOA-ANN), our objective was to create hybrid frameworks that synergistically combined the strengths of both approaches. We rigorously evaluated these hybrid models, ensuring their efficacy a

关键词： Clay compressibility Artificial neural network metaheuristic algorithms Prediction accuracy Practical engineering Reliability systems

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A conceptual investigation of the effect of random numbers over the performance of metaheuristic algorithms

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JOURNAL OF SUPERCOMPUTING 2023年第13期79卷 13971-14038页

作者： Kuyu, Yigit Cagatay Vatansever, Fahri Karsan Co R&D Dept Bursa Turkiye Bursa Uludag Univ Fac Engn Dept Elect Elect Engn Bursa Turkiye

A lot of research studies focus on the development of a new algorithm or the techniques which improve the performance of the original algorithm. Very few studies conduct the research on the effect of the initial population on the solution quality of algorithms. However, in these studies, one or two algorithms have been used, and a limited number of problems have been handled. To fill in the gap in the literature, this study presents a comprehensive analysis of the five algorithms on the effect of the initial population on their final results including both the numerical and real-world problems along with a wide variety of types of distributions. The study consisted of three rounds and followed the strategy for determining the candidate algorithms to be participated in the next rounds, supported by the statistical tests. Rather than using popular random numbers, fourteen different distributions are used to imitate the random numbers in the initial population generation mechanisms of the algorithms. Two different numerical benchmark sets along with nine real-world problems are used to evaluate the performance of the algorithms. The results are compared with the original ones and other distribution-integrated algorithms. Since knowledge of the appropriate random number source is not available a priori, this study could be a good foundation for future studies not only on the matter of the effect of several distributions on the performances of the algorithms but also introducing an alternative way in generating an initial population.

关键词： metaheuristic algorithms Global optimization Mathematical distributions Random numbers

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