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Balancing and scheduling human-robot collaborative assembly lines with heterogeneous robots and limited resources: Constraint programming approach and fruit fly optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 203卷

作者： Zheng, Chenyu Li, Zixiang Janardhanan, Mukund Zhang, Zikai Zhang, Liping Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Hubei Peoples R China Univ Warwick Int Digital Lab Coventry England

Collaborative robots are increasingly utilized to assist the human workers to assemble tasks or complete the assembly tasks solely in assembly lines. This study considers the human-robot collaborative assembly lines with heterogeneous collaborative robots and limited resources to optimize cycle time where human workers and collaborative robots can operate the different tasks in parallel. A constraint programming model is formulated that was able to achieve the optimal solutions for small-sized instances. An improved fruit fly optimization algorithm is developed to tackle the large-sized instances. The proposed algorithm proposes two vectors for encoding, where task assignment vector tackles task allocation sub-problem and process alternative vector tackles process alternative allocation sub-problem. This algorithm utilizes a decoding procedure with a constraint programming approach to achieve an optimal scheduling scheme of the station with human worker and collaborative robot. The lower bound and upper bound of completion time of single station and the earliest and latest processing time of tasks are added to the constraint programming model to speed up the search process. Meanwhile, improved fruit fly optimization algorithm utilizes the improved olfactory phase, improved visual phase and restart phase to accelerate the evolution of the whole swarm and avoid being trapped in local optimum. Computational study demonstrates that constraint programming approach outperforms the current mixed integer programming approach in objective value and solution time. The decoding procedure with constraint programming outperforms the current decoding procedure with mixed integer programming. Comparative study demonstrates that the proposed method outperforms the original fruit fly optimization algorithm and achieves promising performance in comparison with other methods. Finally, the proposed method is applied on scheduling of a gear box assembly line.

关键词： Assembly line balancing Human-robot collaboration Constraint programming fruit fly optimization algorithm

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fruit fly optimization algorithm based on a novel fluctuation model and its application in band selection for hyperspectral image

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第1期12卷 1517-1539页

作者： Ding, Guoshen Qiao, Yanli Yi, Weining Fang, Wei Du, Lili Chinese Acad Sci Anhui Inst Opt & Fine Mech Key Lab Opt Calibrat & Characterizat Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

Spectral band selection is an important operation in the field of hyperspectral remote sensing. However, most of the techniques cannot satisfy the needs of efficiency and accuracy at the same time. In this paper, we present a novel spectral band selection method, fruit fly optimization algorithm (FOA). As yet, FOA has not been used to solve the problem of band selection in hyperspectral image. Through the study of the algorithm, we know that the advantages of FOA are its simple structure and fewer parameters to be adjusted, but the algorithm itself also has some drawbacks. Thus, we first analyze the shortcomings of the traditional FOA, and the corresponding proofs are given by mathematical method. Then, we separate the whole optimization process into two sub-processes, each of which plays a different role. According to the change of the current iteration information and historical optimum value, a fluctuation model is designed in sub-pro1, and its validity is analyzed and validated theoretically and experimentally. In sub-pro2, a control factor is defined to guide the change rate of the step size. These two sub-processes have their own emphasis, and they cooperate with each other, taking into account the global and local optimization capabilities of the algorithm. The test results on 26 benchmark functions also prove that the proposed algorithm is superior to various state-of-art comparison algorithms. Finally, we introduce the proposed algorithm into the band selection of hyperspectral remote sensing, the gratifying results indicate that the proposed algorithm has great potential in hyperspectral remote sensing field.

关键词： fruit fly optimization algorithm Subsection strategy Fluctuation model Band selection

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fruit fly optimization algorithm based on differential evolution and its application on gasification process operation optimization

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KNOWLEDGE-BASED SYSTEMS 2015年第Nov.期88卷 253-263页

作者： Niu, Jinwei Zhong, Weimin Liang, Yi Luo, Na Qian, Feng E China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

The expression of the smell concentration judgment value is significantly important in the application of the fruit fly optimization algorithm (FOA). The original FOA can only solve problems that have optimal solutions in zero vicinity. To make FOA more universal for the continuous optimization problems, especially for those problems with optimal solutions that are not zero. This paper proposes an improved fruit fly optimization algorithm based on differential evolution (DFOA) by modifying the expression of the smell concentration judgment value and by introducing a differential vector to replace the stochastic search. Through numerical experiments based on 12 benchmark instances, experimental results show that the improved DFOA has a stronger global search ability, faster convergence, and convergence stability in high-dimensional functions than the original FOA and evolutionary algorithms from literature. The DFOA is also applied to optimize the operation of the Texaco gasification process by maximizing the syngas yield using two decision variables, i.e., oxygen-coal ratio and coal concentration. The results show that DFOA can quickly get the optimal output, demonstrating the effectiveness of DFOA. (C) 2015 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Smell concentration judgment Differential evolution Coal gasification

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fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle

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NONLINEAR DYNAMICS 2013年第1-2期73卷 611-619页

作者： Sheng, Wang Bao, Yan Changsha Univ Dept Elect & Commun Changsha 410003 Hunan Peoples R China

By adding an electronic throttle and a torque sensor to an engine, it is potentially possible to improve emissions and fuel economy while preserving the torque response of a conventional engine. To do so effectively, however, requires the use of proper control strategy for electronic throttle. In this paper, a kind of fractional order fuzzy PID controller is proposed and it is applied to an electronic throttle. An efficient way to tune fractional order fuzzy PID controller parameters is proposed using a fruit fly optimization algorithm (FOA), which treats the controller parameters tuning as an optimization problem with a proper fitness function. The obtained simulation results show the effective performance of the proposed method.

关键词： Intelligent control Electronic throttle control fruit fly optimization algorithm Fractional order fuzzy PID controller

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fruit fly optimization algorithm multi-objective control method for MMC traction power supply system with unbalanced distribution network

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2022年第6期10卷 1922-1933页

作者： Pan, Hongbin Qin, Kai Zhang, Jiale Yuan, Changmin Xiangta Univ Coll Automat & Elect Informat Xiangtan Peoples R China

When distribution voltage is unbalanced, the voltage and current on the AC side of the traction power supply system based on a modular multilevel converter (MMC) will have negative sequence components, causing reactive and active power fluctuations. At this time, the circulation current will also occur in the MMC, threatening the safety and operation of the system. Based on the analysis of the power fluctuation mechanism, this paper derives the expression of the multi-objective control function combined with the universal current calculation formula. The fruit fly optimization algorithm (FOA) minimizes the multi-objective function and achieves the purpose of suppressing the coordinated control of the three objectives of negative sequence current and active and reactive power fluctuations. The resonant vector controller (RVC) was used to suppress the circulation current in MMC system. A simulation model was built to verify the effectiveness of the proposed strategy and algorithm.

关键词： Modular multilevel converter fruit fly optimization algorithm Circulation suppression Coordinated control of power and current

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An improved fruit fly optimization algorithm for solving traveling salesman problem

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Frontiers of Information Technology & Electronic Engineering 2017年第10期18卷 1525-1533页

作者： Lan HUANG Gui-chao WANG Tian BAI Zhe WANG College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University) Ministry of Education Changchun 130012 China

The traveling salesman problem(TSP), a typical non-deterministic polynomial(NP) hard problem, has been used in many engineering applications. As a new swarm-intelligence optimization algorithm, the fruit fly optimization algorithm(FOA) is used to solve TSP, since it has the advantages of being easy to understand and having a simple implementation. However, it has problems, including a slow convergence rate for the algorithm, easily falling into the local optimum, and an insufficient optimization precision. To address TSP effectively, three improvements are proposed in this paper to improve FOA. First, the vision search process is reinforced in the foraging behavior of fruit flies to improve the convergence rate of FOA. Second, an elimination mechanism is added to FOA to increase the diversity. Third, a reverse operator and a multiplication operator are proposed. They are performed on the solution sequence in the fruit fly's smell search and vision search processes, respectively. In the experiment, 10 benchmarks selected from TSPLIB are tested. The results show that the improved FOA outperforms other alternatives in terms of the convergence rate and precision.

关键词： Traveling salesman problem fruit fly optimization algorithm Elimination mechanism Vision search Operator

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A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem

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KNOWLEDGE-BASED SYSTEMS 2014年第0期57卷 95-103页

作者： Zheng, Xiao-long Wang, Ling Wang, Sheng-yao Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

In this paper, a novel fruit fly optimization algorithm (nFOA) is proposed to solve the semiconductor final testing scheduling problem (SFTSP). First, a new encoding scheme is presented to represent solutions reasonably, and a new decoding scheme is presented to map solutions to feasible schedules. Second, it uses multiple fruit fly groups during the evolution process to enhance the parallel search ability of the FOA. According to the characteristics of the SFTSP, a smell-based search operator and a vision-based search operator are well designed for the groups to stress exploitation. Third, to simulate the information communication behavior among fruit flies, a cooperative search process is developed to stress exploration. The cooperative search process includes a modified improved precedence operation crossover (IPDX) and a modified multipoint preservative crossover (MPX) based on two popular structures of the flexible job shop scheduling. Moreover, the influence of the parameter setting is investigated by using Taguchi method of design-of-experiment (DOE), and suitable values are determined for key parameters. Finally, computational tests results with some benchmark instances and the comparisons to some existing algorithms are provided, which demonstrate the effectiveness and the efficiency of the nFOA in solving the SFTSP. (C) 2013 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Smell-based search Vision-based search Cooperative search Semiconductor final testing scheduling problem

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Single-objective optimization for stack unit of standing wave thermoacoustic refrigerator through fruit fly optimization algorithm

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INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID 2019年 98卷 35-41页

作者： Rahman, Anas A. Zhang, Xiaoqing Huazhong Univ Sci & Technol Sch Energy & Power Engn Dept Refrigerat & Cryogen Wuhan 430074 Hubei Peoples R China

Thermoacoustic refrigeration is considered as a potential alternative refrigeration technology with environmentally-friendly working substance, no or few moving parts, high reliability as well as utilization of low grade energy. optimization for thermoacoustic systems or their key components has become a crucial task to make their performances competitive over conventional vapor-compression ones. In this study, fruit fly optimization algorithm (FOA) as an intelligent method is introduced to separately maximize coefficient of performance of stack unit and cooling power produced for one standing wave thermoacoustic refrigerator, as single objective functions under optimized stack length, stack center position, blockage ratio and drive ratio. FOA had shown better optimal results, compared to published literature results. Compared to results of genetic algorithm, COP had been enhanced by 15.8%. Moreover, cooling power was first discussed as another important objective to optimize. Studied results demonstrated that FOA is an effective approach for design optimization of thermoacoustic refrigerators. (C) 2018 Elsevier Ltd and IIR. All rights reserved.

关键词： Standing wave thermoacoustic refrigerator fruit fly optimization algorithm Stack unit Coefficient of performance Cooling power

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Short-term load forecasting of urban gas using a hybrid model based on improved fruit fly optimization algorithm and support vector machine

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ENERGY REPORTS 2019年 5卷 666-677页

作者： Lu, Hongfang Azimi, Mohammadamin Iseley, Tom Louisiana Tech Univ Trenchless Technol Ctr 599 Dan Reneau Dr Ruston LA 71270 USA

The accurate forecasting of short-term load for urban gas is the premise of gas supply sales, pipe network planning, and energy optimization scheduling. This paper adopts a hybrid model that integrates the fruit fly optimization algorithm (FFOA), simulated annealing algorithm (SA), cross factor (CF) and support vector machine (SVM) to forecast the short-term gas load of urban gas. In the model, SA and CF are used to optimize the FFOA algorithm. This paper takes the urban gas system in Kunming, China as a case study and uses the CF-SA-FFOA-SVM algorithm to predict the gas consumption and compares the results with the other four forecasting methods such as back-propagation neural network (BPNN) and autoregressive integrated moving average model (ARIMA). Besides, this paper analyzes the influence of temperature types (daily maximum temperature and daily mean temperature) in models on forecasting results, and the applicability of the algorithm for forecasting weekly and monthly gas load is analyzed. Moreover, the impact of grouping raw data by the feature on forecasting result is discussed. The following conclusions are drawn: (1) compared with other forecasting models, CF-SA-FFOA-SVM model has higher gas load forecasting accuracy. (2) for Kunming city, if the daily maximum temperature is used as the input variable in the gas load forecasting model, the forecasting accuracy is higher. (3) grouping raw data according to holiday attributes or gas types can effectively improve the accuracy of load forecasting. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： Short-term Gas load Forecasting Urban gas fruit fly optimization algorithm Support vector machine

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A bilevel improved fruit fly optimization algorithm for the nonlinear bilevel programming problem

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KNOWLEDGE-BASED SYSTEMS 2017年第Dec.15期138卷 113-123页

作者： Wang, Guangmin Ma, Linmao Chen, Jiawei China Univ Geosci Sch Econ & Management Wuhan 430074 Peoples R China Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China

This paper proposes a bilevel improved fruit fly optimization algorithm (BIFOA) to address the nonlinear bilevel programming problem (NBLPP). Considering the hierarchical nature of the problem, this algorithm is constructed by combining two sole improved fruit fly optimization algorithms. In the proposed algorithm, the lower level problem is treated as a common nonlinear programming problem rather than being transformed into the constraints of the upper level problem. Eventually, 10 test problems are selected involving low-dimensional and high-dimensional problems to evaluate the performance of BIFOA from the aspects of the accuracy and stability of the solutions. The results of extensive numerical experiments and comparisons reveal that the proposed algorithm outperforms the compared algorithms and is significantly better than the methods presented in the literature;the proposed algorithm is an effective and comparable algorithm for NBLPP. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Bilevel programming problem Nonlinear bilevel programming problem fruit fly optimization algorithm Improved fruit fly optimization algorithm

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