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Effect of ergonomic aspects on single- and multiproduct assembly-line balancing problems

HUMAN FACTORS AND ERGONOMICS IN MANUFACTURING & SERVICE INDUSTRIES 2024年第6期34卷 491-515页

作者： Giridhar, Mena Puthiyaveedu Panicker, Vinay V. Natl Inst Technol Calicut Dept Mech Engn Ind Engn Lab Kozhikode 673601 India

Incorporating ergonomic considerations into an assembly-line balancing problem (ALBP) enhances productivity and minimizes ergonomic concerns. The assembly process, characterized by repetitive motions and handling numerous components, can lead to worker overload. Consequently, the inclusion of ergonomic aspects results in an appropriate distribution of assembly operations and relative workloads. This study investigates a multi-objective ALBP aimed at minimizing the number of workstations, overall skill level required, and variance in workers' energy expenditure across workstations. To address the ALBP while considering the ergonomic aspects, this study proposes an approach based on the non-dominated sorting genetic algorithm ii (NSGA-ii) and multi-objective simulated annealing (MOSA) using Pareto optimality. A comparative analysis of the NSGA-ii and MOSA is conducted in single- and multiproduct production scenarios, and a computational study involving various factors is performed to identify the dominant algorithm. The computational analysis indicates that the runtime performance of MOSA is 73.287% better than that of NSGA-ii;therefore, MOSA outperforms NSGA-ii. This study aims at applying scientific knowledge concerning manufacturing ergonomics to assist manufacturing industries in enhancing their productivity.

关键词： assembly line balancing cognitive abilities cognitive skills ergonomics multi-objective optimization multi-objective simulated annealing non-dominated sorting genetic algorithm ii

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Multi-objective Approach for iiR Filter Design and Bit-Width Optimization

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第8期42卷 4836-4867页

作者： Zelmat, Mohammed Lamini, El-Sedik Tagzout, Samir Belbachir, Hacene Belouchrani, Adel USTHB Fac Math RECITS Lab Algiers Algeria Res Ctr Appl Econ Dev Algiers Algeria EPE Proxylan SPA Algiers Algeria CERIST Res Ctr Sci & Tech Informat Algiers Algeria Ecole Natl Polytech LDCCP lab Algiers Algeria

Digital filter design optimization counts as one of the most challenging problems in circuit development. Furthermore, the bit-width allocation has a crucial impact on the efficiency and accuracy of fixed-point digital filters. The conventional implementation procedure involves studying the bit-width allocation problem after determining the filter coefficients. This sequential procedure leads frequently to an excessive bit-width allocation, hence an extra implementation cost. The main contribution of this paper is to propose a new implementation procedure based on the simultaneous handling of both design issues and the bit-width optimization for the infinite impulse response filter. In this study, the problem is formalized by a multi-objective programming technique using the non-dominated sorting genetic algorithm ii. The joint consideration of the two problems offers a better trade-off between design and bit-width allocation. The output of our approach is compared to the results of existing algorithms. Simulation results show that the multi-objective method significantly reduces the implementation cost while ensuring filter stability and an acceptable mean square error (MSE) compared to the existing algorithms.

关键词： Filter design System identification Bit-width optimization Precision analysis non-dominated sorting genetic algorithm ii IC design

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New flight plan optimisation method utilising a set of alternative final point arrival time targets (RTA constraints)

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AERONAUTICAL JOURNAL 2021年第1292期125卷 1710-1745页

作者： Dancila, R., I Botez, R. M. Univ Quebec Ecole Technol Super Lab Res Act Control Av & Aeroservoelastic LARCASE Montreal PQ H3C 1K3 Canada

This study investigates a new aircraft flight trajectory optimisation method, derived from the non-dominated sorting genetic algorithm ii method used for multi-objective optimisations. The new method determines, in parallel, a set of optimal flight plan solutions for a flight. Each solution is optimal (requires minimum fuel) for a Required Time of Arrival constraint from a set of candidate time constraints selected for the final waypoint of the flight section under optimisation. The set of candidate time constraints is chosen so that their bounds are contiguous, i.e. they completely cover a selected time domain. The proposed flight trajectory optimisation method may be applied in future operational paradigms, such as Trajectory-Based Operations/free flight, where aircraft do not need to follow predetermined routes. The intended application of the proposed method is to support Decision Makers in the planning phase when there is a time constraint or a preferred crossing time at the final point of the flight section under optimisation. The Decision Makers can select, from the set of optimal flight plans, the one that best fits their criteria (minimum fuel burn or observes a selected time constraint). If the Air Traffic Management system rejects the flight plan, then they can choose the next best solution from the set without having to perform another optimisation. The method applies for optimisations performed on lateral and/or vertical flight plan components. Seven proposed method variants were evaluated, and ten test runs were performed for each variant. For five variants, the worst results yielded a fuel burn less than 90kg (0.14%) over the 'global' optimum. The worst variant yielded a maximum of 321kg (0.56%) over the 'global' optimum.

关键词： Flight trajectory optimisation genetic algorithm non-dominated sorting genetic algorithm ii NSGA-ii constrained optimisation multi-objective optimisation required time of arrival aircraft performance model BADA flight plan GRIB navigation air traffic management

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Multi-objective optimization for stochastic computer networks using NSGA-ii and TOPSIS

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第3期218卷 735-746页

作者： Lin, Yi-Kuei Yeh, Cheng-Ta Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan

Network reliability is a performance indicator of computer/communication networks to measure the quality level. However, it is costly to improve or maximize network reliability. This study attempts to maximize network reliability with minimal cost by finding the optimal transmission line assignment. These two conflicting objectives frustrate decision makers. In this study, a set of transmission lines is ready to be assigned to the computer network, and the computer network associated with any transmission line assignment is regarded as a stochastic computer network (SCN) because of the multistate transmission lines. Therefore, network reliability means the probability to transmit a specified amount of data successfully through the SCN. To solve this multiple objectives programming problem, this study proposes an approach integrating non-dominated sorting genetic algorithm ii (NSGA-ii) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). NSGA-ii searches for the Pareto set where network reliability is evaluated in terms of minimal paths and Recursive Sum of Disjoint Products (RSDP). Subsequently, TOPSIS determines the best compromise solution. Several real computer networks serve to demonstrate the proposed approach. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Reliability Multiple objective programming Stochastic computer network non-dominated sorting genetic algorithm ii Technique for order preference by similarity to ideal solution

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Multi-objective approach for optimized planning of electric vehicle charging stations and distributed energy resources

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ELECTRICAL ENGINEERING 2023年第6期105卷 4105页

作者： Ferraz, Rafael S. F. Ferraz, Renato S. F. Medina, Augusto C. Rueda C. Fardin, Jussara F. F. Univ Fed Espirito Santo Postgrad Program Elect Engn Ave Fernando Ferrari BR-29075910 Vitoria ES Brazil

The increasing inclusion of electric vehicles (EVs) in distribution systems is a global trend due to their several advantages, such as increased autonomy and reduced price. However, this growth requires a high investment in electric vehicle charging stations (EVCSs) infrastructure to satisfy the demand. Thus, in this paper, an adequate planning of the EVCSs allocation and sizing is carried out to ensure better power quality indices, in addition to reducing costs related to EVCSs installation and EV users' recharging. Besides the optimal planning of EVCSs, this paper performs the optimal allocation and sizing of distributed energy resources (DERs) in order to mitigate the problem related to voltage levels and power losses. Additionally, a spatial distribution of EVs was performed for the 24 h, considering residential and commercial nodes of a distribution feeder test, from the closeness centrality of graph theory. The non-dominated sorting genetic algorithm ii (NSGA-ii) was applied to obtain the Pareto curve, which made it possible to minimize the objective functions for the IEEE 34-node test feeder. Even with the integration of EV loads, the optimal allocation and sizing of EVCSs and DERs promoted a reduction in voltage deviation and power losses of 11.576% and 56.683%, respectively, in addition to a low variation in the energy costs of 4.447%.

关键词： Distributed energy resource Electric vehicle charging station non-dominated sorting genetic algorithm ii Power quality indices

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Improving Recognition Accuracy of Partial Discharge Patterns by Image-Oriented Feature Extraction and Selection Technique

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2016年第2期23卷 1076-1087页

作者： Zhang, Shuqi Li, Chengrong Wang, Ke Li, Jinzhong Liao, Ruijin Zhou, Tianchun Zhang, Yiyi North China Elect Power Univ Beijing Key Lab High Voltage 2 Beinong Rd Beijing 102206 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl EMC 2 Beinong Rd Beijing 102206 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China Chongqing Univ Sch Elect Engn State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China Elect Power Planning & Engn Inst Beijing 100120 Peoples R China Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tec Nanning 530004 Guangxi Peoples R China

This paper aims to improve recognition accuracies of single sources of partial discharge (PD) by employing image-oriented feature extraction and selection algorithms. 419 diversified samples of PD data acquired from typically artificial defect models, where the defect size, applied voltage and insulation aging are taken into account, are employed for algorithms validation. In the proposed method, the PD gray images are decomposed into various vectors by two-dimensional principal component analysis (2DPCA) on horizontal and vertical directions, respectively, in which 9 representative parameters are extracted from each image vector. Based on the 419 PD samples, the proposed image features are confirmed to achieve better PD recognition performances than typical image compression methods relevant to 2DPCA. To further improve the recognition performance and reduce the feature dimension, the feature selection technique based on non-dominated sorting genetic algorithm ii (NSGA-ii) is adopted to search the optimal 2DPCA features. By using the proposed image-oriented feature extraction and selection algorithm, fuzzy k-nearest neighbor classifier (FkNNC), back-propagation neural network (BPNN) and support vector machine (SVM) with 16-dimensional 2DPCA features can now achieve 96.4%, 94.4% and 97.5% accuracies, respectively. The increment of average recognition accuracies of 5%-7% are obtained by different classifiers compared with phase-resolved partial discharge (PRPD) statistical features in previous studies. The investigations on the recognition results relevant to image distortion, cavity discharge change with time and PD contaminated by random noises with different level confirm the robustness of the 16-dimensional 2DPCA features, which signify a great potential in applying the proposed method to field PD pattern recognition in the future.

关键词： Partial discharges pattern recognition gray image two-dimensional principal component analysis non-dominated sorting genetic algorithm ii

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Multi-objective distributed generation planning in distribution network considering correlations among uncertainties

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APPLIED ENERGY 2018年 226卷 743-755页

作者： Zhang, Shenxi Cheng, Haozhong Li, Ke Tai, Nengling Wang, Dan Li, Furong Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China East China Power Grid Co Shanghai 200002 Peoples R China Tianjin Univ Dept Elect Engn Tianjin 300072 Peoples R China Univ Bath Dept Elect & Elect Engn Bath BA2 7AY Avon England

This paper proposes a novel multi-objective distributed generation planning methodology in distribution network considering correlations among uncertainties, i.e., wind speed, light intensity and load demand. First, under the framework of chance constrained programming, a multi-objective distributed generation planning model with the objective functions of minimizing both the annual total cost and the risk is established. The constraints of the model contain not only the restrictions of distributed generation investment and various electrical limitations, but also the restrictions of correlations among uncertainties. Second, an efficient solving strategy is employed to solve the planning model, in which the correlation-handled probabilistic power flow is used to deal with the correlated uncertainties, and non-dominated sorting genetic algorithm H is applied to achieve the Pareto optimal set of the model. Last, case studies are carried out on two test distribution networks, and the results show that a balance between the economy and the security can be achieved by non-dominated sorting genetic algorithm ii. The case studies also verify that the correlations among uncertainties can influence the multi-objective distributed generation planning results, and the stronger the correlation is, the bigger the influence will be.

关键词： Distributed generation planning Correlations Uncertainties Chance constrained programming Probabilistic power flow non-dominated sorting genetic algorithm ii

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Optimisation of engineering system using a novel search algorithm: the Spacing Multi-Objective genetic algorithm

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CONNECTION SCIENCE 2018年第3期30卷 326-342页

作者： Falahiazar, L. Shah-Hosseini, H. Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran

A large number of real-world issues are among difficult and multi-objective problems. Recently, it has been recognised that the evolutionary algorithms optimise well these types of problems. This paper proposes a novel multi-objective search algorithm that is called the Spacing Multi-Objective genetic algorithm (Spacing-MOGA). The innovation of the proposed Spacing-MOGA lies in a new survival selection algorithm called Spacing Distance. This research eliminates some of the disadvantages of other algorithms such as the non-dominated sorting genetic algorithm ii (NSGAii). The proposed Spacing-MOGA is applied to five test benchmark functions and also to the design of I-Beam. Then, the results are compared with other algorithms such as NSGAii, Adaptive Weighted Particle Swarm Optimisation (AWPSO), and non-dominated sorting Particle Swarm Optimiser (NSPSO) based on the test metrics: Hypervolume, Spacing, Spread, and Generational Distance. Furthermore, for further demonstration of the ability of the proposed Spacing-MOGA, the experimental results are evaluated by the t-test.

关键词： Multi-objective problems non-dominated sorting genetic algorithm ii non-dominated sorting Particle Swarm Optimiser Adaptive Weighted Particle Swarm Optimisation I-Beam

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Numerical Simulation and Multi-objective Optimization for Curing Process of Thermosetting Prepreg

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APPLIED COMPOSITE MATERIALS 2022年第4期29卷 1409-1429页

作者： Hou, Jiatong You, Bo Xu, Jiazhong Fu, Tianyu Wang, Tao Harbin Univ Sci & Technol Sch Mech & Power Engn Harbin 150080 Peoples R China Harbin Univ Sci & Technol Sch Automat Harbin 150080 Peoples R China Harbin Univ Sci & Technol Heilongjiang Prov Key Lab Complex Intelligent Sys Harbin 150080 Peoples R China

In order to improve the curing quality of thermosetting prepreg, reduce the unevenness of the temperature field and the curing degree field during curing process, and improve the curing efficiency, a multi-objective optimization method is used to optimize the cure cycle. In this paper, the coupling of heat conduction, cure kinetics, are used to analyze curing process of thermosetting prepreg. Firstly, a quarter finite element analysis model of the 4-layer unidirectional laminate is established in ABAQUS, the change of the cure cycle is considered, the temperature field and the curing degree field are analyzed. After comparison, the results of numerical simulation are basically consistent with the data in the reference paper. Secondly, surrogate model was established by genetic algorithm- Back Propagation (GA-BP) neural network, and the target value is predicted accurately under the given process parameters. The GA-BP surrogate model is used as the fitness function, and the non-dominated sorting genetic algorithm ii (NSGA-ii) algorithm is used to select the maximum value of temperature overshoot and the curing time as the objectives to perform multi-objective optimization of process parameters. Finally, the research results show that the optimization method can reduce the maximum value of temperature overshoot, improve the uniformity of curing, and reduce curing time. The optimization strategy of "finite element numerical simulation-GA-BP neural network-NSGA-ii optimization algorithm" is proposed, which has positive significance for the optimization of composites molding process.

关键词： Thermosetting prepreg Curing process genetic algorithm- Back Propagation neural network Multi-objective optimization non-dominated sorting genetic algorithm ii

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Location-inventory-reliability optimisation problem in a multi-objective multi-period three-level supply chain network with stochastic demand

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EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING 2023年第4期17卷 479-528页

作者： Abdi, Farid Farughi, Hiwa Sadeghi, Heibatolah Arkat, Jamal Univ Kurdistan Pasdaran St Sanandaj *** Iran Univ Kurdistan Fac Ind Engn Pasdaran St Sanandaj *** Iran

One of the efficient methods of improving the reliability of factories is allocating appropriate redundant components that play an important role in responding to customers' demands, timely delivery and cost reduction. In this study, the issue of simultaneous optimisation of facility location-inventory-redundancy allocation has been investigated. In this regard, a multiple-period three-level problem has been taken into account. It has been assumed that demand for each retailer is stochastic and follows the normal distribution. In order to deal with the fluctuations of demand, the risk pooling effect has been applied. For this purpose, an integer nonlinear programming model has been proposed to optimise the cost of the supply chain as well as its reliability. Since facility location-inventory and redundancy allocation are categorised as NP-hard problems, non-dominated sorting genetic algorithm (NSGA-ii) and archived multi-objective simulated annealing (AMOSA) algorithms have been developed for solving the aforementioned problem. Finally, their results have been evaluated by using comparison metrics of multi-objective algorithms. [Submitted: 9 September 2021;Accepted: 9 March 2022]

关键词： supply chain management location-inventory model redundancy allocation stochastic demand risk pooling non-dominated sorting genetic algorithm ii NSGA-ii archive multi-objective simulated annealing Taguchi methods

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