In this paper, we investigate a coordinated optimization problem of production and maintenance where the machine reliability decreases with the use of the machine. Lower reliability means the machine is more likely to...
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In this paper, we investigate a coordinated optimization problem of production and maintenance where the machine reliability decreases with the use of the machine. Lower reliability means the machine is more likely to fail during the production stage. In the event of a machine failure, corrective maintenance (CM) of the machine is required, and the CM of the machine will cause a certain cost. Preventive maintenance (PM) can improve machine reliability and reduce machine failures during the production stage, but it will also cause a certain cost. To minimize the total maintenance cost, we must de-termine an appropriate PM plan to balance these two types of maintenance. In addition, the tardiness cost of jobs is also considered, which is affected not only by the processing sequence of jobs but also by the PM decision. The objective is to find the optimal job processing sequence and the optimal PM plan to minimize the total expected cost. To solve the proposed problem, an improved grey wolf optimizer (igwo) algorithm is proposed. Experimental results show that the igwo algorithm outperforms GA, VNS, TS, and standard GWO in optimization and computational stability.
The focus of power producers has shifted from conventional energy sources to sustainable energy sources because of the depletion of fossil fuels and carbon emission causing global warming and climate change. Solar cel...
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The focus of power producers has shifted from conventional energy sources to sustainable energy sources because of the depletion of fossil fuels and carbon emission causing global warming and climate change. Solar cells are the most prominent option to deal with these problems. The precise estimation of solar cell parameters is very much required before their installation to achieve high efficiency. In recent years applications of several optimization algorithms for parameter estimation of the solar cell have been addressed. Recently, intelligent grey wolf optimizer (igwo), which is an advanced version of grey wolf optimizer (GWO) incorporating a sinusoidal truncated function as a bridging mechanism and opposition based learning has been introduced. The wide applicability of this variant has been examined over different conventional benchmark functions and on some real problems. This fact motivated authors to employ this variant on parameter extraction process. The main motivation behind the implementation of igwo on solar cell parameter estimation process is the efficiency of this version to deal with complex optimization problems. To estimate the PV cell parameter values, measurement of voltage and current are considered at three important points. These are open circuit point, short circuit point and maximum power point, for two solar cell representative models i.e. single diode model and double diode model. Results of igwo are compared with the results of other variants of GWO on these two models and for three films (Mono crystalline, poly crystalline and thin film). Results reveal that igwo produces better results.
This study targets the low accuracy and efficiency of the support vector machine (SVM) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (igwo) algorithm was proposed based on deep learning...
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This study targets the low accuracy and efficiency of the support vector machine (SVM) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (igwo) algorithm was proposed based on deep learning and a swarm intelligence optimization algorithm to optimize the structural parameters of SVM and improve the rolling bearing fault diagnosis. A nonlinear contraction factor update strategy was also proposed. The variable coefficient changes with the shrinkage factor & alpha;. Thus, the search ability was balanced at different early and late stages by controlling the dynamic changes of the variable coefficient. In the early stages of optimization, its speed is low to avoid falling into local optimization. In the later stages of optimization, the speed is higher, and finding the optimal solution is easier, balancing the two different global and local optimization capabilities to complete efficient convergence. The dynamic weight update strategy was adopted to perform position updates based on adaptive dynamic weights. First, the dataset of Case Western Reserve University was used for simulation, and the results showed that the diagnosis accuracy of igwo-SVM was 98.75%. Then, the igwo-SVM model was trained and tested using data obtained from the full-life-cycle test platform of mechanical transmission bearings independently researched and developed by Nanjing Agricultural University. The fault diagnosis accuracy and convergence value of the adaptation curve were compared with those of PSO-SVM (particle swarm optimization) and GWO-SVM diagnosis models. Results showed that the igwo-SVM model had the highest rolling bearing fault diagnosis accuracy and the best diagnosis convergence.
New energy vehicles have become a global transportation development trend in order to achieve considerable fuel consumption and carbon emission reductions. However, as the number of new energy cars grows, new energy v...
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New energy vehicles have become a global transportation development trend in order to achieve considerable fuel consumption and carbon emission reductions. However, as the number of new energy cars grows, new energy vehicle safety concerns are becoming more evident, posing a major threat to drivers' lives and property and limiting the industry's growth. This paper develops a charging safety early warning model for electric ve-hicles (EV) based on the Improved Grey Wolf Optimization (igwo) algorithm in order to improve the timeliness and accuracy of charging safety early warning. The greatest voltage of a single battery was chosen as the study goal based on the polarization characteristics of lithium-ion batteries and the equalization features of a vehicle lithium-ion battery pack. The igwo-BP algorithm is then used to fit the entire EV charging process and anticipate the vehicle's charging condition. At the same time, set the warning threshold and the warning error code. In real time, comparing the EV charging data with the fitted data, computing the residual, and building the EV charging safety warning model based on the residual change. Finally, case analysis is performed using daily charging data from both rapid and slow charging. The findings reveal that the proposed early warning model based on the igwo-BP algorithm can reliably recognize the abnormal state of EV charging voltage and issue timely warnings.
Advances in technology and population growth are two factors responsible for increasing electricity consumption, which directly increases the production of electrical energy. Also, due to environmental, technical and ...
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Advances in technology and population growth are two factors responsible for increasing electricity consumption, which directly increases the production of electrical energy. Also, due to environmental, technical and economic constraints, it is challenging to meet the demand at certain hours, such as peak hours. Therefore, it is necessary to manage the network consumption to modify the peak load and complete the power system constraints. One way to achieve this goal is to use a demand response program. The best infrastructure for running these types of programs is the smart grid (SG). This paper first considers many shiftable loads of different kinds in the SG, including residential, commercial, and industrial microgrids. The load shift method is, then, modeled as a multi-objective optimization problem to manage the shiftable load consumption of the SG. The main goals of this problem include reducing the cost of customer bills, the peak load, losses and improving network voltage. Simulations are performed by two methods of Simplex and Improved Grey Wolf Optimization (igwo). The Simplex method is implemented with the CPLEX solver in General Algebraic Modelling System (GAMS) software and the igwo algorithm is implemented in MATLAB software. Then, the effect of implementing the proposed program with both methods is examined and compared. For example, in terms of reducing the peak load, the CPLEX method could reduce the peak in the residential, commercial, and industrial microgrids by 6.7%, 1%, and 16% more than igwo, respectively. CPLEX also can reduce the production costs of residential, commercial, and industrial microgrids by more than 3.5, 2.2, and 3.9% compared to igwo. In general, the CPLEX solver provides better results than the igwo in many cases.
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