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Optimizing production planning and sequencing in hot strip mills: an approach using multi-objective genetic algorithms

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-48页

作者： Fardad, Hamidreza Safi-Esfahani, Faramarz Barekatain, Behrang Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran

Planning and sequencing for hot strip mills in the steel industry is a challenging, complex problem that has fascinated optimization researchers and practitioners alike. This paper applies a combinatory heuristic search and a multi-objective metaheuristic that is a novel approach called HSMO-NSGA-II and employs the HSMO heuristic search method and NSGA-II multi-objective genetic optimization as a metaheuristic algorithm to address complex hot strip mills scheduling tasks. This research aims to enhance the efficiency and effectiveness of production planning and sequencing in hot strip mill, while minimizing operational costs and maximizing rolling utilization. The output consists of slabs categorized into three parts, which converge toward a set of Pareto-optimal solutions while maintaining diversity across the entire solution space. The results demonstrate a significant improvement in comparing the base methods with the HSMO-NSGA-II method, and the proposed method shows better average performance at 23.01%. Notably, the HSMO-NSGA-II method demonstrated a remarkable improvement in performance across the evaluated scenarios, showcasing its potential to enhance productivity and operational efficiency in industrial applications significantly. These findings not only support the viability of using advanced genetic algorithms in complex industrial settings but also open avenues for future research into hybrid optimization techniques.

关键词： Production planning Sequencing Hot strip mill (HSM) multi-objective genetic algorithms Optimization

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Optimization of a hybrid photovoltaic/thermal-ground source heat pump system using artificial neural networks and multi-objective genetic algorithms

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JOURNAL OF BUILDING ENGINEERING 2025年 103卷

作者： Andalibi, Ali Safarzadeh, Mohammad Sobhani, Seyed Mohammad Jafar Pasdarshahri, Hadi Tarbiat Modares Univ Fac Mech Engn POB 14115-111 Tehran Iran

The current study focuses on determining the optimal operating conditions of a cycle using multiobjective genetic algorithms;this cycle harnesses energy from the sun and the ground to supply a multi-zone and large sports and administrative complex. The cycle is designed to incorporate various control scenarios;numerous decision variables, some of which are related to these control scenarios, are utilized to optimize the performance of the cycle. COP, ground temperature variation over a year, and reduced CO2 emissions are the objective functions of the optimization problem, which is divided into two distinct problems. In both problems, there are 36 decision variables that inherently originate from four independent variables, which are modified over the months. So, the optimization process is time-consuming, and computations are reduced by developing surrogate models via artificial neural networks. Also, a total of 1,080 different artificial neural network architectures and training data sets, each with 36 inputs (corresponding to the decision variables of the optimization problems) and one output (representing one of the objective functions) are analyzed to identify the best-performing architectures using hyperparameter tuning. Then, the Pareto fronts of the mentioned optimization problems are extracted, proposing a number of optimal conditions. The highest and the lowest amounts of the coefficient of performance on the Pareto fronts of the problems are 2.28 and 3.02, respectively. The highest possible amount is 41.15%, 7.35 %, and 7.27% more than three baseline scenarios. Among all the suggested conditions on the Pareto fronts, ten conditions (including the Knee points) are extensively studied.

关键词： Photovoltaic/thermal collectors Ground-source heat pumps Artificial neural networks multi-objective genetic algorithms Hyperparameter tuning

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multi-objective genetic algorithms for cost-effective distributions of actuators and sensors in large structures

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第9期39卷 7822-7833页

作者： Cha, Young-Jin Agrawal, Anil K. Kim, Yeesock Raich, Anne M. Worcester Polytech Inst Dept Civil & Environm Engn Worcester MA 01609 USA CUNY City Coll New York NY 10021 USA Lafayette Coll Easton PA 18042 USA

This paper proposes a multi-objective genetic algorithm (MOGA) for optimal placements of control devices and sensors in seismically excited civil structures through the integration of an implicit redundant representation genetic algorithm with a strength Pareto evolutionary algorithm 2. Not only are the total number and locations of control devices and sensors optimized, but dynamic responses of structures are also minimized as objective functions in the multi-objective formulation, i.e., both cost and seismic response control performance are simultaneously considered in structural control system design. The linear quadratic Gaussian control algorithm, hydraulic actuators and accelerometers are used for synthesis of active structural control systems on large civil structures. Three and twenty-story benchmark building structures are considered to demonstrate the performance of the proposed MOGA. It is shown that the proposed algorithm is effective in developing optimal Pareto front curves for optimal placement of actuators and sensors in seismically excited large buildings such that the performance on dynamic responses is also satisfied. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithms Optimal placement Structural control ASCE control benchmark Actuator location Sensor location

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multi-objective genetic algorithms based automated clustering for fuzzy association rules mining

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2008年第3期31卷 243-264页

作者： Alhajj, Reda Kaya, Mehmet Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada Firat Univ Dept Comp Engn TR-23169 Elazig Turkey Global Univ Dept Comp Sci Beirut Lebanon

Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets;for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective genetic algorithms (GA);the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms;2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.'s work in terms of runtime, number of large itemsets and number of association rules.

关键词： Automated clustering CURE Data mining Fuzziness Fuzzy association rules multi-objective genetic algorithms

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Coordination of set-ups between two stages of a supply chain using multi-objective genetic algorithms

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2005年第15期43卷 3163-3180页

作者： Mansouri, SA Amirkabir Univ Technol Dept Ind Engn Tehran 15914 Iran

A multi-objective genetic algorithm (MOGA) solution approach for a sequencing problem to coordinate set-ups between two successive stages of a supply chain is presented in this paper. The production batches are processed according to the same sequence in both stages. Each production batch has two distinct attributes and a set-up occurs in the upstream stage every time the first attribute of the new batch is different from the previous one. In the downstream stage, there is a set-up when the second attribute of the new batch is different from that of the previous one. Two objectives need to be considered in sequencing the production batches including minimizing total set-ups and minimizing the maximum number of set-ups between the two stages. Both problems are NP-hard so attainment of an optimal solution for large problems is prohibited. The solution approach starts with an initialization stage followed by evolution of the initial solution set over generations. The MOGA makes use of non-dominated sorting and a niche mechanism to rank individuals in the population. Selected individuals taken from a given population form the succeeding generation using four genetic operators as: reproduction, crossover, mutation and inversion. Experiments in a number of test problems show that the MOGA is capable of finding Pareto-optimal solutions for small problems and near Pareto-optimal solutions for large instances in a short CPU time.

关键词： set-up coordination sequencing supply chains multi-objective genetic algorithms

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Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on multi-objective genetic algorithms

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ENERGIES 2011年第3期4卷 532-544页

作者： Fang, Lincun Qin, Shiyin Xu, Gang Li, Tianli Zhu, Kemin Shenzhen Univ Coll Mechatron & Control Engn Shenzhen Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China

Compared to conventional vehicles Hybrid Electric Vehicles (HEVs) provide fairly high fuel economy with lower emissions. To enhance HEV performance in terms of fuel economy and emissions, and ensure user satisfaction with driving performance, the need for simultaneous optimization for the main parameters of powertrain components and control system is inevitable. However, this problem is challenging due to the large amount of coupling design parameters, conflicting design objectives and nonlinear constraints. Considering the defect of the methods which convert multi-objective optimization problems into single-objective ones, a comprehensive methodology based on the non-dominated sorting genetic algorithms II (NSGA II) to achieve parameter optimization for powertrain components and control system simultaneously and successfully find the Pareto-optimal solutions set is presented in this paper. A case simulation is carried out and simulated by ADVISOR, The simulation results show that this method can produce many Pareto-optimal solutions and a satisfactory solution can be selected by decision-makers according to their requirements. The results demonstrate the effectiveness of the algorithms proposed in this paper.

关键词： hybrid electric vehicles simultaneous optimization multi-objective genetic algorithms Pareto optimal solution

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Mining numerical association rules via multi-objective genetic algorithms

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INFORMATION SCIENCES 2013年 233卷 15-24页

作者： Minaei-Bidgoli, B. Barmaki, R. Nasiri, M. Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

Association rule discovery is an ever increasing area of interest in data mining. Finding rules for attributes with numerical values is still a challenging point in the process of association rule discovery. Most of popular methods for association rule mining cannot be applied to the numerical data without data discretization. There have been efforts to resolve the problem of dealing with numeric data. These approaches suffer from problems which are discussed in this paper. This work proposes a multi-objective genetic algorithm approach for mining association rules for numerical data. Several measures are defined in order to determine more efficient rules. Three measures, confidence, interestingness, and comprehensibility have been used as different objectives for our multi objective optimization which is amplified with genetic algorithms approach. Finally, the best rules are obtained through Pareto optimality. This method is based on the notion of rough patterns that use rough values defined with upper and lower intervals to represent a range or set of values. Mutation and crossover operators give a powerful exploration ability to the method and allow it to find out the best intervals of existing numerical values. The experimental results show that the generated rules by this method are more appropriate - based on several different characteristics - than the similar approaches' results, and our method outperforms these methods. (c) 2013 Elsevier Inc. All rights reserved.

关键词： Numerical association rule multi-objective genetic algorithms Confidence Comprehensibility Interestingness Rough value

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Tailor-made material design: An evolutionary approach using multi-objective genetic algorithms

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COMPUTATIONAL MATERIALS SCIENCE 2009年第1期45卷 1-7页

作者： Chakraborti, N. Sreevathsan, R. Jayakanth, R. Bhattacharya, B. Indian Inst Technol Dept Met & Mat Engn Kharagpur 721302 W Bengal India Indian Inst Technol Dept Civil Engn Kharagpur 721302 W Bengal India

Materials design may be defined as designing materials as dynamic multi level-structured systems with integrated and specific process/structure/performance/property relationships. The main objective of the work is to design structural materials based on inter-atomic potentials - the so-called "inverse problem" - to explore materials of high strength to weight ratio with a thermodynamically stable structure. Since the aforementioned objectives are contradicting each other it leads to a Pareto-optimal problem which is eventually solved by the multi-objective genetic algorithms solver NSGA-II. The material behavior is modeled using Lennard-Jones type interatomic potential function. The Pareto-optimal front provides a series of hypothetical materials which are then compared and contrasted with existing materials as and when possible. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Optimization Materials design Inter-atomic potential Stiffness Lightness Lennard-Jones potential multi-objective genetic algorithms NSGA-II

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Robust Optimum Design of Tuned Mass Dampers to Mitigate Pedestrian-Induced Vibrations Using multi-objective genetic algorithms

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STRUCTURAL ENGINEERING INTERNATIONAL 2017年第4期27卷 492-501页

作者： Fernando Jimenez-Alonso, Javier Saez, Andres Univ Seville Dept Bldg Engn & Geotech Engn Seville Spain Univ Seville Dept Continuum Mech & Struct Anal Seville Spain

Tuned mass dampers (TMDs) are well-known control devices used widely to mitigate the pedestrian-induced vibrations on footbridges. The main purpose of this study is the robust optimum design of TMDs using multi-objective genetic algorithms in order to control the pedestrian-induced vibrations on footbridges considering the uncertainties associated with their modal properties. The performance of the TMD is improved via the robust optimum design of its modal parameters using multi-objective genetic algorithms. As an example, a benchmark footbridge, modelled by frame and shell elements, is used to assess numerically the performance and accuracy of the proposed method. The pedestrian action is simulated by an equivalent harmonic load. The proposed method is compared with the conventional Den Hartog's proposal. The results show that this new method is more effective than the conventional proposal and the resulting damping device is more economic due to the smaller value of its design parameters.

关键词： footbridge passive structural control tuned mass dampers robust design optimization multi-objective genetic algorithms

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Precast production scheduling using multi-objective genetic algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第7期38卷 8293-8302页

作者： Ko, Chien-Ho Wang, Shu-Fan Natl Pingtung Univ Sci & Technol Dept Civil Engn Pingtung 91201 Taiwan

The goal of production scheduling is to achieve a profitable balance among on-time delivery, short customer lead time, and maximum utilization of resources. However, current practices in precast production scheduling are fairly basic, depending heavily on experience, thereby resulting in inefficient resource utilization and late delivery. Moreover, previous methods ignoring buffer size between stations typically induce unfeasible schedules. Certain computational techniques have been proven effective in scheduling. To enhance precast production scheduling, this research develops a multi-objective precast production scheduling model (MOPPSM). In the model, production resources and buffer size between stations are considered. A multi-objective genetic algorithm is then developed to search for optimum solutions with minimum makespan and tardiness penalties. The performance of the proposed model is validated by using five case studies. The experimental results show that the MOPPSM can successfully search for optimum precast production schedules. Furthermore, considering buffer sizes between stations is crucial for acquiring reasonable and feasible precast production schedules. (C) 2011 Published by Elsevier Ltd.

关键词： Precast production Scheduling multi-objective genetic algorithms Buffer

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