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Inventory model with fuzzy lead-time and dynamic demand over finite time horizon using a multi-objective genetic algorithm

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2012年第6期64卷 1822-1838页

作者： Bera, U. K. Maiti, M. K. Maiti, M. Natl Inst Technol Dept Math Jirania 799055 West Tripura India Mahishadal Raj Coll Dept Math Purba Medinipur 721628 WB India Vidyasagar Univ Dept Math Paschim Medinipur 700114 WB India

The real-world inventory control problems are normally imprecisely defined and human interventions are often required in solving these decision-making problems. In this paper, a realistic inventory problem with an infinite rate of replenishment over a prescribed finite but imprecise time horizon is formulated considering time dependent ramp type demand, which increases with time. Lead time is also assumed as fuzzy in nature. Shortages are allowed and backlogged partially. Two models are considered depending upon the ordering policies of the decision maker (DM). The imprecise parameters are first transformed to corresponding nearest interval numbers depending upon some distance metric on fuzzy numbers and then following the interval mathematics, the objective function for total profit from the planning horizon is obtained (which is an interval function). Then interval objective decision making problem is reduced to multi-objective problems using different approaches. Finally a fast and elitist multi-objective genetic algorithm (FEMOGA) is used for solving these multi-objective models to find pareto-optimal decisions for the DM. The models are illustrated numerically. As a particular case, the results due to linear trended and constant demands have been presented. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Fuzzy lead-time Interval number multi-objective genetic algorithm

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Optimization on high-pressure side of pump-turbine runner based on high efficiency and stability criterion via multi-objective genetic algorithm method

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2023年第8期237卷 1743-1761页

作者： Qin, Yonglin Li, Deyou Wang, Hongjie Liu, Zhansheng Wei, Xianzhu Wang, Xiaohang Song, Yongjian Harbin Inst Technol Sch Energy Sci & Engn 92 West Dazhi St Harbin 150001 Peoples R China Harbin Inst Large Elect Machinery State Key Lab Hydropower Equipment Harbin 150040 Peoples R China

Pumped storage power plants are doomed to play a more important role in peak-valley shifting hence the demand for operation stability is gradually regarded as an equal important criterion as high efficiency. In our paper, new concepts, namely "swept," "bowed (lean)," and "twisted" are introduced to systematic innovative design the geometry of high-pressure side (HPS) of a pump-turbine runner. Thereafter, a multi-objective optimization process, which consists of design of experiment (DoE), meta model generation, multi-objective genetic algorithm (MOGA), self-organization map (SOM) and takes both high efficiency and stability discipline into account is produced. The final optimization plan is selected and testified numerically. Compared to the original runner, the efficiency is improved from 91.7% to 91.9% at pump mode and improved from 92.3% to 92.8% at turbine mode. Moreover, the S margin is increased from 89.34 degrees to 90.23 degrees at small GVO and increased from 79.56 degrees to 80.29 degrees at large GVO. Besides, hump unsteady region is completely eliminated. Moreover, the flow characteristic comparation is conducted based on local hydraulic loss rate (LHLR) method. For design points, the optimized runner obviously decreases the hydraulic loss in hub and middle part of runner domain for turbine mode and slightly decrease the hydraulic loss in stay vane region for pump mode. For unsteady characteristics, HPS can better adjust the hydraulic loss distribution in corresponding discharge operating points, largely decreasing S unsteady characteristic in turbine mode and eliminating hump unsteady characteristic in pump mode. We believe that the methods proposed in our paper can bring the design of hydraulic machinery to a new level.

关键词： Pump turbine high pressure side multi-objective genetic algorithm hump characteristic S characteristic

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Aiming strategy optimization for uniform flux distribution in the receiver of a linear Fresnel solar reflector using a multi-objective genetic algorithm

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APPLIED ENERGY 2017年 205卷 1394-1407页

作者： Qiu, Yu Li, Ming-Jia Wang, Kun Liu, Zhan-Bin Xue, Xiao-Dai Xi An Jiao Tong Univ Key Lab Thermofluid Sci & Engn Minist Educ Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China Tsinghua Univ State Key Lab Control & Simulat Power Syst & Gene Dept Elect Engn Beijing 100084 Peoples R China Qinghai Univ New Energy Photovolta Ind Res Ctr Xining 810016 Qinghai Peoples R China

Non-uniform solar flux may lead to negative effects in the receiver of the linear Fresnel reflector (LFR), including the failure of the receiver and the fluctuating operation. For reducing these effects, an aiming strategy optimization approach is presented by combining a multi-objective genetic algorithm (GA) and Monte Carlo ray tracing to homogenize the flux distribution in current work. Both the flux non-uniformity index and the optical loss 07100 are used as the objective functions. Based on the approach, first, the flux distributions in the multi Tube Cavity Receiver (MTCR) and the Single-Tube Receiver with a Secondary Collector (STRSC) are optimized at a typical condition. Optimal results indicate that the GA optimization strategy (S2) can reach a compromise between the flux non-uniformity and the optical loss in both MTCR and STRSC systems. Furthermore, the optimal strategy obtained at a specific transversal incidence angle can be applied in a relatively large range around it. Moreover, parameter study indicates that the aiming line number (n(aim)) has little impact on the efficiencies of the two systems. naim has almost no effect on the flux non-uniformity in the MTCR, but the effect is visible in the STRSC. Finally, the application of S2 under a real-time condition indicates that fluxes in the two receivers can be homogenized efficaciously in the whole time range with a small drop of 0.2-3.8 percentage points in efficiency compared with those of traditional one-line aiming strategy (S1). It is also found that the flux non-uniformity indexes of the MTCR are greatly reduced from 0.77-1.09 to 0.02-0.06 when S1 is replaced by S2, and those of the STRSC are steeply reduced from 0.59-0.70 to 0.9-0.37. It is concluded that the present approach is effective and suitable for homogenizing the fluxes in the receivers of LFRs.

关键词： Linear Fresnel solar reflector Aiming strategy optimization Flux distribution non-uniformity Optical loss Monte Carlo ray tracing multi-objective genetic algorithm

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An effective multi-objective genetic algorithm based on immune principle and external archive for multi-objective integrated process planning and scheduling

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第9-12期91卷 3145-3158页

作者： Luo, Guofu Wen, Xiaoyu Li, Hao Ming, Wuyi Xie, Guizhong Zhengzhou Univ Light Ind Mech & Elect Engn Inst 5 Dongfeng Rd Zhengzhou 450002 Henan Peoples R China

Process planning and scheduling are two major sub-systems in a modern manufacturing system. In traditional manufacturing system, they were regarded as the separate tasks to perform sequentially. However, considering their complementarity, integrating process planning and scheduling can further improve the performance of a manufacturing system. Meanwhile, the multiple objectives are needed to be considered during the realistic decision-making process in a manufacturing system. Based on the above requirements from the real manufacturing system, developing effective methods to deal with the multi-objective integrated process planning and scheduling (MOIPPS) problem becomes more and more important. Therefore, this research proposes a multi-objective genetic algorithm based on immune principle and external archive (MOGA-IE) to solve the MOIPPS problem. In MOGA-IE, the fast non-dominated sorting approach used in NSGA-II is utilized as the fitness assignment scheme and the immune principle is exploited to maintain the diversity of the population and prevent the premature condition. Moreover, the external archive is employed to store and maintain the Pareto solutions during the evolutionary process. Effective genetic operators are also designed for MOIPPS. To test the performance of the proposed algorithm, three different scale instances have been employed. And the proposed method is also compared with other previous algorithms in literature. The results show that the proposed algorithm has achieved good improvement and outperforms the other algorithms.

关键词： Integrated process planning and scheduling multi-objective genetic algorithm Immune principle External archive

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Using a heuristic multi-objective genetic algorithm to solve the storage assignment problem for CPS-based pick-and-pass system

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ENTERPRISE INFORMATION SYSTEMS 2021年第9期15卷 1238-1259页

作者： Tu, Mengru Yang, Ming-Feng Kao, Sheng-Long Lin, Feng-Cheng Wu, Ming-Hung Lin, Cheng-Kuan Natl Taiwan Ocean Univ Dept Transportat Sci Keelung Taiwan Natl Taiwan Ocean Univ Ctr Excellence Ocean Engn Keelung Taiwan Feng Chia Univ Dept Informat Engn & Comp Sci Taichung Taiwan Natl Audit Off Taipei Taiwan

With the advancement of AI and the widespread application of IoT and cloud computing, the pick-and-pass system (PKPS) can potentially be transformed into a cyber-physical system (CPS) based intelligent warehouse picking system. This paper proposes a CPS-based PKPS with a heuristic multi-objective genetic algorithm to solve the NP-hard storage assignment problem (SAP) for order picking operations in an e-commerce-based warehouse. The proposed algorithm considers both the workload balance between picking lines and emergency replenishment during picking operation. Finally, the study shows that the proposed algorithm is effective in improving the efficiency of picking operations based on software simulation.

关键词： Cyber-physical system storage assignment problem pick-and-pass system multi-objective genetic algorithm computer-aided picking systems

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Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF CONTROL 2017年第4期90卷 661-683页

作者： Montazeri, A. West, C. Monk, S. D. Taylor, C. J. Univ Lancaster Dept Engn Gillow Ave Lancaster England

This paper concerns the problem of dynamic modelling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model-orientated research using the same machine, the paper develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimise the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivisation of an output error single-performance index. The developed algorithm utilises a multi-objective genetic algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of 'true' parameters) and experimental data. Both simulation and experimental results show that multi-objectivisation has improved convergence of the estimated parameters compared to the single-objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.

关键词： Parameter estimation system identification nonlinear model multi-objective genetic algorithm hydraulic manipulator

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Feature selection with multi-objective genetic algorithm based on a hybrid filter and the symmetrical complementary coefficient

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APPLIED INTELLIGENCE 2021年第6期51卷 3899-3916页

作者： Zhang, Rui Zhang, Zuoquan Wang, Di Du, Marui Beijing Jiaotong Univ Sch Sci Beijing Peoples R China

With the expansion of data size and data dimension, feature selection attracts more and more attention. In this paper, we propose a novel feature selection algorithm, namely, Hybrid filter and Symmetrical Complementary Coefficient based multi-objective genetic algorithm feature selection (HSMOGA). HSMOGA contains a new hybrid filter, Symmetrical Complementary Coefficient which is a well-performed metric of feature interactions proposed recently, and a novel way to limit feature subset's size. A new Pareto-based ranking function is proposed when solving multi-objective problems. Besides, HSMOGA starts with a novel step called knowledge reserve, which precalculate the knowledge required for fitness function calculation and initial population generation. In this way, HSMOGA is classifier-independent in each generation, and its initial population generation makes full use of the knowledge of data set which makes solutions converge faster. Compared with other GA-based feature selection methods, HSMOGA has a much lower time complexity. According to experimental results, HSMOGA outperforms other nine state-of-art feature selection algorithms including five classic and four more recent algorithms in terms of kappa coefficient, accuracy, and G-mean for the data sets tested.

关键词： Feature selection Feature interaction Hybrid filter Symmetrical complementary coefficient multi-objective genetic algorithm

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Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2016年第3期27卷 549-559页

作者： Pashazadeh, Hamed Gheisari, Yousof Hamedi, Mohsen Islamic Azad Univ East Azarbaijan Sci & Res Branch Young Researchers & Elites Club Tabriz Iran Islamic Azad Univ Bushehr Branch Dept Math Bushehr Iran Univ Tehran Sch Mech Engn Coll Engn Tehran Iran

Resistance spot welding (RSW) is a highly used joining procedure in automotive industry. In RSW, after a number of welds the welding electrode starts to wear and its diameter changes. This causes the weld nugget diameter abnormal variations and consequently reduces the weld strength. Therefore the tip of the electrode should be dressed in RSW. Selecting the optimum time for the welding electrode tip dressing operations is very important. In this research three welding parameters including the welding time, the welding current, and the welding pressure were identified as the main effective parameters on the weld nugget dimensions including the weld nugget diameter and height using full factorial design of experiments. Then using hybrid combination of the artificial neural networks and multi-objective genetic algorithm, the optimized values of the aforementioned parameters were specified. Finally experiments were fulfilled to estimate the admissible number of the weld spots which should be done before the electrode tip dressing operation.

关键词： Resistance spot welding Electrode tip dressing Design of experiments Artificial neural networks multi-objective genetic algorithm

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Finding Robust Adaptation Gene Regulatory Networks Using multi-objective genetic algorithm

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016年第3期13卷 571-577页

作者： Ren, Hai-Peng Huang, Xiao-Na Hao, Jia-Xuan Xian Univ Technol Dept Informat & Control Engn Xian 710048 Peoples R China Shaanxi Key Lab Complex Syst Control & Intelligen Xian Peoples R China HeXi Univ Sch Phys & Electromech Engn Zhangye 734000 Gansu Peoples R China

Robust adaptation plays a key role in gene regulatory networks, and it is thought to be an important attribute for the organic or cells to survive in fluctuating conditions. In this paper, a simplified three-node enzyme network is modeled by the Michaelis-Menten rate equations for all possible topologies, and a family of topologies and the corresponding parameter sets of the network with satisfactory adaptation are obtained using the multi-objective genetic algorithm. The proposed approach improves the computation efficiency significantly as compared to the time consuming exhaustive searching method. This approach provides a systemic way for searching the feasible topologies and the corresponding parameter sets to make the gene regulatory networks have robust adaptation. The proposed methodology, owing to its universality and simplicity, can be used to address more complex issues in biological networks.

关键词： Gene regulatory networks Michaelis-Menten rate equations robust adaptation multi-objective genetic algorithm Pareto ranking

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Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2009年第11-12期57卷 1756-1766页

作者： Jia, Jie Chen, Jian Chang, Guiran Tan, Zhenhua Northeastern Univ Coll Informat Sci & Engn 402 Sch Informat Sci & Engn Shenyang 110004 Liaoning Prov Peoples R China Northeastern Univ Ctr Comp Shenyang 110004 Peoples R China Northeastern Univ Sch Software Shenyang 110004 Peoples R China

Due to the constrained energy and computational resources available to sensor nodes, the number of nodes deployed to cover the whole monitored area completely is often higher than if a deterministic procedure were used. Activating only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy of the system. A novel coverage control scheme based on multi-objective genetic algorithm is proposed in this paper. The minimum number of sensors is selected in a densely deployed environment while preserving full coverage. As opposed to the binary detection sensor model in the previous work, a more precise detection model is applied in combination with the coverage control scheme. Simulation results show that our algorithm can achieve balanced performance on different types of detection sensor models while maintaining high coverage rate. With the same number of deployed sensors, our scheme compares favorably with the existing schemes. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Coverage control Energy multi-objective genetic algorithm Wireless sensor network

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