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A novel particle swarm optimization-based grey model for the prediction of warehouse performance

JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2021年第2期8卷 705-727页

作者： Islam, Md Rakibul Ali, Syed Mithun Fathollahi-Fard, Amir Mohammad Kabir, Golam Bangladesh Univ Engn & Technol Dhaka 1000 Bangladesh Ecole Technol Super Dept Elect Engn 1100 Notre Dame St West Montreal PQ H3C 1K3 Canada Univ Regina Fac Engn & Appl Sci Ind Syst Engn Regina SK S4S 0A2 Canada

Warehouses constitute a key component of supply chain networks. An improvement to the operational efficiency and the productivity of warehouses is crucial for supply chain practitioners and industrial managers. Overall warehouse efficiency largely depends on synergic performance. The managers preemptively estimate the overall warehouse performance (OWP), which requires an accurate prediction of a warehouse's key performance indicators (KPIs). This research aims to predict the KPIs of a ready-made garment (RMG) warehouse in Bangladesh with a low forecasting error in order to precisely measure OWP. Incorporating advice from experts, conducting a literature review, and accepting the limitations of data availability, this study identifies 13 KPIs. The traditional grey method (GM)-the GM (1, 1) model-is established to estimate the grey data with limited historical information but not absolute. To reduce the limitations of GM (1, 1), this paper introduces a novel particle swarm optimization (pso)-based grey model-psoGM (1, 1)-to predict the warehouse's KPIs with less forecasting error. This study also uses the genetic algorithm (GA)-based grey model-GAGM (1, 1)-the discrete grey model-DGM (1, 1)-to assess the performance of the proposed model in terms of the mean absolute percentage error and other assessment metrics. The proposed model outperforms the existing grey models in projecting OWP through the forecasting of KPIs over a 5-month period. To find out the optimal parameters of the pso and GA algorithms before combining them with the grey model, this study adopts the Taguchi design method. Finally, this study aims to help warehouse professionals make quick OWP estimations in advance to take control measures regarding warehouse productivity and efficiency.

关键词： Grey systems theory pso algorithm psoGM (1, 1) model warehouse KPIs OWP Taguchi method

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Multisensor information-based adaptive control method for cutting head speed of roadheader

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第11期235卷 1941-1955页

作者： Wang, Pengjiang Shen, Yang Li, Rui Zong, Kai Fu, Shichen Wu, Miao China Univ Min & Technol Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China

An adaptive control method to improve the cutting head speed of roadheaders using multisensor information is proposed, so as to solve the problems of low cutting efficiency and low intelligence of roadheaders during underground tunnelling. The operation of a roadheader is analysed, and a control strategy for its cutting head speed is proposed. In addition, the cutting head speed is categorised into five gears according to the multisensor information of different cutting states. The controller for speed estimation is designed using a back propagation neural network optimised using an improved particle swarm optimisation algorithm. A control system is established in MATLAB to analyse the effectiveness of the method. The simulation results show that an Ipso-BP controller has the best control effect and can attain the target speed. The response time was lower than those of fuzzy logic controllers and traditional PI controllers by 46% and 68%, respectively, and the overshoot decreased by 4.69% and 12.19%, respectively. Furthermore, experimental research verified the effectiveness of this method. This method can adaptively adjust the cutting head speed of a roadheader using multisensor information and is important (both theoretical and practically) for extending the service life of roadheaders and improving tunnelling efficiency.

关键词： roadheader BP neural network pso algorithm cutting head speed adaptive control

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Optimization of Construction Material Cost through Logistics Planning Model of Dragonfly algorithm - Particle Swarm Optimization

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KSCE JOURNAL OF CIVIL ENGINEERING 2021年第7期25卷 2350-2359页

作者： Pham Vu Hong Son Nguyen Huynh Chi Duy Pham Ton Dat Ho Chi Minh City Univ Technol Vietnam Natl Univ Bach Khoa Univ Construct Engn & Management Dep Ho Chi Minh City 700000 Vietnam

Managing a construction project is challenging because of cost, time, safety, and quality considerations. In the most projects, the cost of construction is one of the most critical aspect because material cost alone accounts for significant ratio of the total project. Therefore, the cost of construction materials should be controlled. In this study, we proposed the use of material requirements planning (MRP) to control the cost of construction materials. After determining the demand for the materials required for construction, we estimated both the quantity of materials required and time taken to deliver the materials to the construction site. Although economic order quantity models have been applied to analyze construction material costs, they do not accurately reflect concerns related to material cost. Therefore, we used the material supply chain model (construction logistics planning) to analyze material costs. To optimize MRP according to the current progress of a project, a novel approach combining the dragonfly algorithm (DA) and particle swarm optimization algorithm (pso) was proposed. To verify the advanced searchability of the DA-pso algorithm, the algorithm was compared with the gray wolf and the genetic algorithms.

关键词： Construction logistics planning Optimizing material Inventory control Dragonfly algorithm pso algorithm Optimize delivery schedules Time to import construction materials

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Exploring the disparities in urban park accessibility from multiple demand perspectives: A case study in Beijing, China

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ECOLOGICAL INDICATORS 2024年 163卷

作者： Qin, Jiahui Tong, Shuai Wang, Qianxin China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Peoples R China China Univ Min & Technol NASG Key Lab Land Environm & Disaster Monitoring Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Mech & Civil Engn Xuzhou 221116 Peoples R China

Urban Park Green Space (UPGS) is an essential component of cities, with a wide range of important services. There is a keener demand for green space in the process of urbanization. Previous studies have extensively evaluated the residents' accessibility to green space, which belonging to the demand of UPGS's social service function, but rarely combined the ecological service demand to conduct a refined calculation. This may lead to biased results in the evaluation of the satisfaction of green space. Considering the scarcity of urban land resources, it is difficult to support the efficient use of space based on these results. To fill this gap, this study evaluated the accessibility of various service functions that UPGS can provide from a variety of demand perspectives in central Beijing using multi-source geospatial big data. The results show that: (1) The accessibility results may be overestimated when ignoring eco-service demand. (2) Regions with worse or poor accessibility have the largest proportion, and accessibility presents an obvious low-high-low cyclic structure. (3) Urban peripheral areas are always in a significant disadvantaged position in terms of accessibility. Based on the accessibility results, the solution is further provided by using pso algorithm to simulate the location of new parks to solve the problem of insufficient UPGS supply in the low accessibility area. Our study can provide better insights for city managers to formulate effective green space management strategies and references for optimization strategy.

关键词： Demand for urban parks Spatial accessibility G2SFCA pso algorithm Sustainable development

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Digital Twin based Real-Time Analysis of DC-DC Boost Converters

Digital Twin based Real-Time Analysis of DC-DC Boost Convert...

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14th Annual IEEE Energy Conversion Congress and Exposition (IEEE ECCE) / IEEE-Industry-Applications-Society (IEEE-IAS) Annual Meeting

作者： Di Nezio, Giulia di Benedetto, Marco Lidozzi, Alessandro Solero, Luca Roma Tre Univ Dept Engn Rome Italy

ISBN: (数字)9781728193878

ISBN: (纸本)9781728193878

This paper proposes the real-time analysis of the DC-DC boost converter by utilizing the Digital Twin (DT) method. Based on the measurement data from sensors, the DT is a real-time digital model that runs in parallel to the physical DC-DC boost converter providing information for its monitoring and control. The digital model is obtained by a numerical methodology for discrete-time representation of the physical system. The estimation of uncertainty parameters of the physical system is based on the data analysis on the measurement data. The data analysis is done by applying the heuristic algorithm like Particle Swarm Optimization (pso) method. The proposed analysis is validated through the digital-switching model realized by means of the FPGA and it runs on a suitable control board. Experimental tests on the converter prototype are illustrated.

关键词： Digital Twin DC-DC boost converter parameters monitoring pso algorithm

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Parameter Identification of Train basic resistance Based on Particle Swarm Optimization 17

Parameter Identification of Train basic resistance Based on ...

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17th Conference on Industrial Electronics and Applications (ICIEA)

作者： Li Tianxiang Yang Hang Wang Chuanru Wang Qingyuan Sun Pengfei Feng Xiaoyun Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China

ISBN: (纸本)9781665409841

Train basic resistance parameters (BRP) are important foundation for train traction calculations. These parameters are time-varying with wheel rail wear and weather conditions, which resulting in the calculation model inconsistent with the real model. Therefore, this paper proposes a train basic resistance parameters identification method (BRPI) based on particle swarm optimization (pso) algorithm. By sampling train operation data, the fitting speed curve of each set of parameters is obtained under same control sequence. Taking speed fitting as objective function, pso algorithm is used to identify a set of basic resistance parameters with highest fitness. In order to eliminate the defect of easily falling into local solution, a modified speed update strategy of pso algorithm is proposed. Case studies are used to verify the effectiveness of the proposed method, results show this method has fast convergence rate and excellent solution speed, and it effectively improve the accuracy of train controller.

关键词： train basic resistance pso algorithm parameter identification heavy haul train

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A new effective robust nonlinear controller based on pso for interleaved DC-DC boost converters for fuel cell voltage regulation

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SOFT COMPUTING 2020年第22期24卷 17051-17064页

作者： Abdelmalek, Samir Dali, Ali Bettayeb, Maamar Bakdi, Azzeddine Univ Medea Fac Technol Dept Elect Engn Medea Algeria Renewable Energy Dev Ctr CDER Algiers Algeria Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Jeddah Saudi Arabia Univ Oslo Dept Math N-0851 Oslo Norway

Output voltage regulation of DC-DC converters has recently gained an increasing attention to face the many system nonidealities. The fast switching behavior is nonlinear time varying, the presence of model and measurement uncertainties, and large variations, are all inherited challenges. The aim of the present work is to design a robust nonlinear controller that ensures satisfactory and robust output voltage regulation for a proton-exchange membrane fuel cell (PEMFC) based on a DC-DC Interleaved Boost Converter (IBC). A state-space model of the DC-DC IBC is first derived using the state-space averaging technique, and a mathematical model is constructed for the PEFMC. In this regard, a robust nonlinear controller and a proportional integral controller are proposed. The controllers are tuned though particle swarm optimization algorithm to estimate their good parameters assuring the desired performance is met. The integral of absolute error criterion is used to improve the dynamic performance of the overall controlled system. Furthermore, the closed-loop stability is analyzed using the Lyapunov stability theorem, and the effectiveness of the closed-loop system is validated under various operating conditions of the PEMFC and load perturbations. Compared to other methods, the obtained results demonstrate a superior performance of the proposed control strategy in terms of its robustness to variations and uncertainties, smooth tracking of a varying set-point and faster transients.

关键词： Interleaved boost converter Proton-exchange membrane fuel cell pso algorithm Nonlinear control pso-PI controller Current ripples

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A novel multivariate time-lag discrete grey model based on action time and intensities for predicting the productions in food industry

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期238卷

作者： Ye, Jing Li, Yan Meng, Fanlin Geng, Shuaishuai Nanjing Univ Finance & Econ Sch Management Sci & Engn Nanjing 210023 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China Henan Agr Univ Coll Informat & Management Sci Zhengzhou 450046 Peoples R China

The food industry is a pillar industry and plays an important role in the development of China's national economy. In view of the complex environments and the characteristics of fluctuating development in which the Chinese food industry are located, this paper delves into the mechanism of action of relevant factors on the system behavior and proposes a grey prediction model based on action time and intensities considering the widespread existence of time-lag effects that is applicable to the prediction of the food industry. Firstly, considering the action time and intensity of the factors, this paper introduces the action duration, the parameters of time lag and intensity coefficient to expand the multivariate discrete grey prediction model. Secondly, the bidirectional grey A-S (Area-Slope) comprehensive relational model is used to calculate the degree of correlation between relevant factors acting on time-effect sub-sequence and the system behavior sequence under different action durations and time lag, to identify the driving factors and estimate their action durations and time lag. Then, the driving factors are incorporated into the grey prediction model based on action time and intensities, and the structural parameters and intensity coefficients of the model are estimated by using the least square and particle swarm algorithm. Finally, by fitting and predicting two examples in the food industry, the results of the proposed novel model are compared with those of six other prediction models to verify effectiveness. The results demonstrates that the prediction accuracy of the proposed model is better than those of other grey models in time-lag data sequences.

关键词： Grey prediction GM(1,N) Time-lag effect Parameter optimization pso algorithm

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Optimization approach for axisymmetric electric field cloaking and shielding

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2021年第1期29卷 40-55页

作者： Alekseev, G., V Tereshko, D. A. Shestopalov, Yu, V Inst Appl Math FEB RAS Vladivostok Russia Far Eastern Fed Univ Sch Nat Sci Vladivostok Russia Univ Gavle Dept Elect Math & Nat Sci Gavle Sweden

In this paper we study inverse problems of electric conductivity that arise in the design of spherical shielding or cloaking shells and other functional devices used to control static electric fields. The shells are considered consisting of a finite number of layers filled with homogeneous isotropic or anisotropic medium. The inverse problems under study are reduced to control problems with the layers electric conductivities taken as controls. A different choice of minimized functionals allows us to solve a wide range of design problems using one approach. A numerical algorithm to solve these problems is based on particle swarm optimization. Various results of numerical experiments are discussed in order to find the most effective designs. The findings obtained in this study describe a broad set of specific easy-to-manufacture structures that have the highest cloaking or shielding performance in the class of layered shells.

关键词： Conductivity problem shielding problem cloaking problem inverse problems numerical optimization pso algorithm

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Optimization of power system parameters for multi-mode hybrid electric vehicles based on regional demand

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年

作者： Wang, Hongxia Chang, Cheng Liu, Hanwu Sun, Wencai Fan, Zikai Jiang, Wei Liu, Yuwei Henan Inst Technol Sch Vehicle & Traff Engn Xinxiang Henan Peoples R China Jilin Univ Transportat Coll 988 Renmin St Changchun 130022 Jilin Peoples R China Jilin Univ Automot Engn Coll Changchun Jilin Peoples R China

The performance of the vehicle power system for multi-mode hybrid electric vehicle (M-MHEV) can be improved through meticulous parameter matching and optimization. This paper developed the powertrain parameter design and dynamic performance calculation program based on the structure design, parameter matching calculation, and powertrain system selection. A HOQM of M-MHEV is formulated to ascertain the weight coefficient of vehicle performance indicators according to different regional requirements and a parameter matching and optimization method for power system, employing the Particle Swarm Optimization (pso) algorithm, is suggested to assess and harmony the vehicle's performance. Firstly, a house of quality model of M-MHEV is constructed to ascertain the weight coefficient of vehicle performance indicators derived from different regional demands. Additionally, a method is introduced for optimization of power system parameters of M-MHEV based on regional demand, the pso algorithm is employed to optimize the characteristic parameters. And sensitivity analysis of characteristic parameters is conducted relying on user needs in different regions. The simulation results show that users in the northern and southern regions have different final weight coefficients for vehicle performance indicators and the parameters not only exert a substantial individual influence but also exhibit an interaction effect on the vehicle performance. Finally, a series of comparative simulations, are carried out with two optimization parameters respectively, the simulation outcomes demonstrate that the solution obtained through optimized design parameters solution could markedly enhance the technical parameters of the vehicle. The efficacy and viability of the proposed method based on user needs in different regions are verified. The conclusion provides a useful reference for differentiated design.

关键词： M-MHEV parameter matching and optimization house of quality model user requirement analysis pso algorithm

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