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multi-objective genetic algorithm for cell formation problem considering cellular layout and operations scheduling

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2012年第7期25卷 625-635页

作者： Arkat, Jamal Farahani, Mehdi Hosseinabadi Ahmadizar, Fardin Univ Kurdistan Dept Ind Engn Sanandaj 416 Iran

Integrated design of cellular manufacturing (CM) systems consist of three major decisions: cell formation (CF), cellular layout (CL) and planning issues such as cellular scheduling (CS). This article presents a mathematical model to concurrently identify the formation of cells, cellular layout and the operations sequence with the objective of minimising total transportation cost of parts as well as minimising makespan. A multi-objective genetic algorithm (MOGA) is then developed to solve the problem. The proposed MOGA exploits a novel evolutionary process which enables it to efficiently find Pareto optimal solutions. Computational results show the advantages of the proposed integrated approach and the superiority of the proposed MOGA over some well-known multi-objective evolutionary algorithms.

关键词： cell formation cellular layout operations scheduling multi-objective genetic algorithm

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Minimization of exceptional elements and voids in the cell formation problem using a multi-objective genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 9597-9602页

作者： Arkat, Jamal Hosseini, Leila Farahani, Mehdi Hosseinabadi Univ Kurdistan Dept Ind Engn Sanandaj Iran

Cell formation problem is the main issue in designing cellular manufacturing systems. The most important objective in the cell formation problem is to minimize the number of exceptional elements which helps to reduce the number of intercellular movements. Another important but rarely used objective function is to minimize the number of voids inside of the machine cells. This objective function is considered in order to increase the utilization of the machines. We present a bi-objective mathematical model to simultaneously minimize the number of exceptional elements and the number of voids in the part machine incidence matrix. An epsilon-constraint method is then applied to solve the model and to generate the efficient solutions. Because of the NP-hardness of the model, the optimal algorithms can not be used in large-scale problems and therefore, we have also developed a bi-objective genetic algorithm. Some numerical examples are considered to illustrate the performance of the model and the effectiveness of the solution algorithms. The results demonstrate that in comparison with the epsilon-constraint method, the proposed genetic algorithm can obtain efficient solution in a reasonable run time. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Cell formation problem Exceptional elements Voids epsilon-constraint method multi-objective genetic algorithm

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Selection of security system design via games of imperfect information and multi-objective genetic algorithm

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2013年 112卷 59-66页

作者： Lins, Isis Didier Rego, Leandro Chaves Moura, Marcio das Chagas Droguett, Enrique Lopez Univ Fed Pernambuco UFPE CTG CEERMA BR-50740550 Recife PE Brazil Univ Fed Pernambuco UFPE DEP CTG BR-50740530 Recife PE Brazil Univ Fed Pernambuco UFPE DE CCEN BR-50740540 Recife PE Brazil

This work analyzes the strategic interaction between a defender and an intelligent attacker by means of a game and reliability framework involving a multi-objective approach and imperfect information so as to support decision-makers in choosing efficiently designed security systems. A multi-objective genetic algorithm is used to determine the optimal security system's configurations representing the tradeoff between the probability of a successful defense and the acquisition and operational costs. Games with imperfect information are considered, in which the attacker has limited knowledge about the actual security system. The types of security alternatives are readily observable, but the number of redundancies actually implemented in each security subsystem is not known. The proposed methodology is applied to an illustrative example considering power transmission lines in the Northeast of Brazil, which are often targets for attackers who aims at selling the aluminum conductors. The empirical results show that the framework succeeds in handling this sort of strategic interaction. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Selection of compromise solutions Security system design Game of imperfect information multi-objective genetic algorithm

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Zonal control for selective catalytic reduction system using a model-based multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF ENGINE RESEARCH 2021年第3期22卷 911-920页

作者： Wang, Guoyang Qi, Jinzhu Liu, Shiyu Li, Yanfei Shuai, Shijin Wang, Zhiming Shandong Univ Sch Energy & Power Engn Jinan Shandong Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China

It is challenging for aqueous urea injection control to achieve high NOconversion efficiency while restricting tailpipe ammonia (NH3) slip. Optimizing the selective catalytic reduction systems can reduce diesel engine emissions, potentially improve fuel economy and urea utilization efficiency, and finally reduce aftertreatment costs. In this article, a model-based multi-objective genetic algorithm is adopted to optimize selective catalytic reduction systems related to trade-off between NOemission and NH3 slip. Selective catalytic reduction model is a one-state selective catalytic reduction model based on continuous stirred tank reactor theory, which significantly reduces the computational burden. The optimal NH3 coverage ratio map was obtained globally based on world harmonized transient cycle. The effect of temperature on optimal NH3 coverage ratio, Zonal control logics extracted from the optimal solution, and the control problems on different zones were analyzed. The zonal control logics were validated on multiple test cycle with different initial NH3 coverage ratios. Results show that the zonal control achieves high NOconversion while restricting the tailpipe NH3 slip. With this method, NOemission and NH3 slip of optimal solution can meet the requirements of the Euro VI emission regulation for heavy-duty diesel engines.

关键词： Heavy-duty diesel engine NO emission control selective catalytic reduction ammonia coverage ratio optimization multi-objective genetic algorithm

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Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design

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ENERGY AND BUILDINGS 2015年 88卷 135-143页

作者： Yu, Wei Li, Baizhan Jia, Hongyuan Zhang, Ming Wang, Di Chongqing Univ Fac Urban Construct & Environm Engn Chongqing 400045 Peoples R China Chongqing Univ Minist Educ Key Lab Three Gorges Reservoir Reg Ecoenvironm Chongqing 400045 Peoples R China Chongqing Univ Natl Ctr Int Res Low Carbon & Green Bldg Chongqing 400045 Peoples R China State Key Lab Bldg Safety & Built Environm Beijing 100015 Peoples R China

Several conflicting criteria exist in building design optimization, especially energy consumption and indoor environment thermal performance. This paper presents a novel multi-objective optimization model that can assist designers in green building design. The Pareto solution was used to obtain a set of optimal solutions for building design optimization, and uses an improved multi-objective genetic algorithm (NSGA-II) as a theoretical basis for building design multi-objective optimization model. Based on the simulation data on energy consumption and indoor thermal comfort, the study also used a simulation-based improved back-propagation (BP) network which is optimized by a genetic algorithm (GA) to characterize building behavior, and then establishes a GA-BP network model for rapidly predicting the energy consumption and indoor thermal comfort status of residential buildings;Third, the building design multi-objective optimization model was established by using the GA-BP network as a fitness function of the multi-objective genetic algorithm (NSGA-II);Finally, a case study is presented with the aid of the multi-objective approach in which dozens of potential designs are revealed for a typical building design in China, with a wide range of trade-offs between thermal comfort and energy consumption. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Building design Energy consumption Thermal comfort Artificial neural network multi-objective genetic algorithm

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Controlled elitist multi-objective genetic algorithm joined with neural network to study the effects of nano-clay percentage on cell size and polymer foams density of PVC/clay nanocomposites

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2020年第4期139卷 2801-2810页

作者： He, Wei Bagherzadeh, Seyed Amin Shahrajabian, Hamzeh Karimipour, Arash Jadidi, Hamid Quang-Vu Bach Minjiang Univ Fujian Univ Marine Intelligent Ship Equipment Engn Res Ctr Fuzhou 350108 Fujian Peoples R China Islamic Azad Univ Najafabad Branch Dept Mech Engn Najafabad Iran Ton Duc Thang Univ Fac Environm & Labour Safety Sustainable Management Nat Resources & Environm R Ho Chi Minh City Vietnam

The parameters of foaming and nano-clay percentage on the density of polymer foam and cell size with the PVC field is studied. Cell size and density have a significant impact on the strength of foam and its insulation (including sounds and thermal insulation). By optimizing cell size and density, foam can be produced with the best mechanical properties. In foaming process of the nanocomposite samples by mass method, the design variables (input parameters) are foaming time and temperature and MMT content. The controlled elitist multi-objective GA is applied to minimize both the foam density and the cell size. To that end, the population size and the Pareto fraction are selected as 100 and 0.5, respectively. The noninferior solution obtained by the controlled elitist multi-objective GA is illustrated. When both the MMT and the temperature are high, the resulting foam does not have ideal characteristics.

关键词： Statistical neural network multi-objective genetic algorithm Polymer foam density Clay nanocomposites

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Allocation of multi-type FACTS devices using multi-objective genetic algorithm approach for power system reinforcement

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ELECTRICAL ENGINEERING 2010年第6期92卷 227-237页

作者： Gitizadeh, M. Shiraz Univ Technol Sch Elect & Elect Engn Shiraz Iran

This paper presents a novel approach to find optimum locations and capacity of flexible alternating current transmission systems (FACTS) devices in a power system using a multi-objective optimization function. Thyristor controlled series compensator (TCSC) and static var compensator (SVC) are the utilized FACTS devices. Our objectives are: active power loss reduction, new introduced FACTS devices cost reduction, voltage deviation reduction, and increase on the robustness of the security margin against voltage collapse. The operational and controlling constraints, as well as load constraints, are considered in the optimum allocation. A multi-objective genetic algorithm (MOGA) is used to approach the Pareto-optimal front (non-dominated) solutions. In addition, the estimated annual load profile has been utilized in a sequential quadratic programming (SQP) optimization sub-problem to the optimum siting and sizing of FACTS devices. IEEE 14-bus Network is selected to validate the performance and effectiveness of the proposed method.

关键词： FACTS devices allocation multi-objective optimization multi-objective genetic algorithm

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Stable cutting zone prediction in CNC turning using adaptive signal processing technique merged with artificial neural network and multi-objective genetic algorithm

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EUROPEAN JOURNAL OF MECHANICS A-SOLIDS 2018年 70卷 238-248页

作者： Shrivastava, Y. Singh, B. Jaypee Univ Engn & Technol Mech Engn Dept Guna MP India

In metal cutting, surface quality and material removal rate are the key parameters investigated by several researchers. It has been already established that, at high-speed machining, tool chatter deteriorates the work-piece surface and effects the material removal rate too. Numerous crucial investigations have been carried out regarding the enhancement of these parameters considering tool chatter as a major thread. In the recent advancement, signal processing techniques are being used for suppression of chatter. Moreover, it has been found these advance techniques helps in predicting the actual nature of chatter. However, the chatter signal recorded during machining usually contain contaminations merged with actual signal. Hence, it becomes a task for researchers to rectify the signal and predict a suitable cutting zone that is capable of obtaining good surface finish with acceptable material removal rate. In the present work, ensemble empirical mode decomposition technique has been used to rectify the signal and optimal cutting zone has been predicted using the artificial neural network and multi-objective genetic algorithm. Machining in the obtained optimal zone will upsurge the productivity, by decreasing tool chatter and increasing material removal rate simultaneously. To validate the proposed methodology, experiments have been performed within the obtained optimal zone. The results indicate the effectiveness of the proposed methodology.

关键词： Tool chatter Data acquisition Ensemble empirical mode decomposition Intrinsic mode function Artificial neural network multi-objective genetic algorithm

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multi-objective optimization of μ-EDM parameters for μ-hole drilling on Hastelloy C 276 super alloy using Response Surface Methodology and multi-objective genetic algorithm

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CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY 2022年 39卷 115-133页

作者： Dutta, Souradeep Sarma, Deba Kumar Natl Inst Technol Meghalaya Dept Mech Engn Shillong 793003 India Natl Inst Technol Meghalaya Dept Mech Engn Shillong 793003 Meghalaya India

Hastelloy C 276 is one of the nickel-based super alloys which has an excellent resistance against corrosion at high temperature. It has a major application in nuclear and aerospace industry. Micro-electrical discharge machining (mu-EDM) is a highly accurate and precise non-conventional micro-machining process to fabricate this alloy. Thus, in this research paper an effort has been made to examine the impact of input process parameters like capacitance, gap voltage and pulse on time (t(on)) on the response variables i.e., material removal rate (MRR), tool wear rate (TWR) and diametral overcut (DOC) during mu-EDM of Hastelloy C 276. Response Surface Methodology (RSM) is a collection of statistical and mathematical tools which determine the relationship between input and output characteristics. Box-Behnken design (BBD) is a special type of RSM based design that reduces the no of experiments without affecting the variation between the input and response parameters. RSM based BBD is used in the current study to develop the mathematical models for each response variable. RSM based desirability function (RSM-DF) which convert multiple responses into a scale free desirability, is used as an optimization tool in the present study. The current work used the multi -objective genetic algorithm (MOGA), a non-conventional searching based multi-response optimization al-gorithm, to optimize the machining parameters for maximum MRR along with minimum TWR and DOC. The lowest machining time is achieved by experiment no 10 and the least entry mu-hole diameter is drilled by experiment no 15. ANOVA analysis accomplished that capacitance has the highest significant contribution towards MRR, TWR and DOC. 3D response surface plot discloses that MRR, TWR and DOC is increased with capacitance until 5600 pF, 5600 pF and 8900 pF and then there is little reduction took place for all the three responses. The optimization result from RSM-DF is capacitance of 1001.560 pF, gap voltage of 137.6670 vo

关键词： Micro-electrical discharge machining Material removal rate Tool wear rate Diametral overcut Response Surface Methodology multi-objective genetic algorithm

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A multi-objective genetic algorithm based on two-stage reinforcement learning for green flexible shop scheduling problem considering machine speed

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 258卷

作者： Zhuang, Mengzhen Zhang, Wei Tang, Hongtao Li, Xinyu Wang, Kaipu Wuhan Univ Technol Wuhan Sch Mech & Elect Engn Wuhan 430074 Peoples R China Hubei Prov Engn Res Ctr Robot & Intelligent Mfg Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

The consumption of energy and resources in the manufacturing industry has garnered significant attention due to the increasingly severe environmental issues. Green shop scheduling research is focused on optimizing economic and environmental indicators within the current manufacturing model. This paper specifically addresses the flexibility of job-shop scheduling problem considering machine speed (CMS-FJSP), as different machine speeds during the production process can impact energy and resource consumption. The objectives of this study include minimizing the makespan, total energy consumption, and tool wear. To tackle this problem, a multi-objective genetic algorithm that incorporates a two-stage reinforcement learning approach is proposed. In light of the characteristics of the problem, a three-layer encoding approach is suggested, which encompasses machine allocation, operation sequencing, and machine speed selection. Additionally, a decoding method that integrates energy-saving strategies is proposed to enhance the optimization process. To improve the quality of the population, three distinct initialization methods have been developed. Furthermore, a parameter adjustment strategy informed by two-stage reinforcement learning is introduced. This strategy incorporates a state set and action set tailored to the unique characteristics of two-stage reinforcement learning, alongside corresponding reward mechanisms. In 30 test cases, the proposed algorithm demonstrates superior uniformity and convergence compared to five classical algorithms. In a practical case within a hydraulic component production workshop conducted at a hydraulic component company, the proposed algorithm generates 26 scheduling schemes with different focuses, achieving a 14.39% reduction in makespan, a 2.13% decrease in energy consumption, and a 10.65% reduction in tool wear.

关键词： Green shop scheduling multi-objective genetic algorithm Reinforcement learning Machine speed Parameters adjusting strategy

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