Production Management of Multi-Objective Flexible Job-Shop Based on Improved PSO

Mao, C. L.

doi:10.2507/ijsimm20-2-co11

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Let xl and x2 be the certain and uncertain types of material demand, respectively; y1 and y2 be the certain and uncertain quantities of material demand, respectively; f1 and f2 be the certain and uncertain equipment of material demand, respectively; gl and g2 be the certain and uncertain time of material demand, respectively. Then, the specific scenario of production material demand with multiple uncertainties in the actual production process includes: (1) x1→y1→f1→g1; (2) x1→y1→f1→g2; (3) x1→y1→f2→g1; (4) x1→y1→f2→g2; (5) x1→y2→f1→g1; (6) x1→y2→f1→g2; (7) x1→y2→f2→g1; (8) x1→y2→f2→g2; (9) x2→y2→f2→g2…”

Section:  mentioning

confidence: 99%

“…With the progress of the manufacturing industry and the improvement of consumer demand for consumption quality and individualization, enterprises must pursue the diversification and intellectualization of product demand and production process, and production management must quickly respond to the fluctuations in the demand for production materials by production equipment oriented to different orders [1][2][3][4][5][6][7][8][9]. Facing different orders, the production material guarantees for production equipment involves the purchase, deployment, transportation and storage of materials.…”

Section: Introductionmentioning

confidence: 99%

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Demand Prediction of Production Materials and Simulation of Production Management

Fan¹

2022

Int. j. simul. model.

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In modern times, production management must quickly respond to the fluctuations in the demand for production materials by production equipment oriented to different orders. This calls for a reasonable plan for production management. Based on Markov model, this paper explores the demand prediction of production materials, and investigates the simulation of production management. Firstly, the grey system model and the Markov chain model were combined into a hybrid prediction model for the dynamic and time-varying demand for production materials in the production process. Through dynamic scenario analysis, the authors explored the formulation of production management and control strategies under multiple uncertain demands for production materials, and gave the specific analysis process. Multiple performance indices were considered synthetically, the optimization objectives of production control simulation were given under the multiple uncertain demands for production materials, and a solution was put forward for the production control problem. The simulation results verify the effectiveness of the proposed model.

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Section:  mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Demand Prediction of Production Materials and Simulation of Production Management

Fan¹

2022

Int. j. simul. model.

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“…However, the PSO algorithm is prone to fall into a local optimal solution; therefore, improving the PSO algorithm has become the focus of research. Mao et al [18] improved the inertia weight and learning factor of the algorithm using a dynamic response mechanism. Huang et al [19] optimized particle topology using the dual inheritance framework of the cultural algorithm.…”

Section: Of 21mentioning

confidence: 99%

“…To comprehensively reflect the advantages and disadvantages of various control optimization algorithms, the physical parameters of the four common crane working conditions are shown in Table 8. Figures [15][16][17][18][19][20][21][22] shows that when the crane operates under the four common parameters, the maximum overshoot of the swing angle of the PID control system optimized by the PSO-SA algorithm is 2.45 • when affected by a crosswind and environmental noise, while the maximum overshoots of the other control systems optimized by the CPM, PSO, and SA algorithms are 3.78 • , 3.65 • , and 3.68 • , respectively. It can be seen that in the presence of external interference, the PID control system optimized by the PSO-SA algorithm can achieve a good anti-swing control effect for lifting weights.…”

Section: Experimental Simulation For Bridge Crane With Interferencementioning

confidence: 99%

Design of Anti-Swing PID Controller for Bridge Crane Based on PSO and SA Algorithm

Hui

Qiao

et al. 2022

Electronics

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Since the swing of the lifting load and the positioning of the trolley during the operation of a bridge crane seriously affect the safety and reliability of its work, we have not only designed Proportional Integral Derivative (PID) controllers for the anti-swing and positioning control but also proposed a hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm to optimize the gains of the controllers. In updating the PSO algorithm, a nonlinear adaptive method is utilized to update the inertia weight and learning coefficients, and the SA algorithm is also integrated when the PSO algorithm is searching for a global optimal solution, to reduce the probability of falling into the local optimal solution. The simulation results demonstrate that the PSO–SA algorithm proposed in this paper is prone to be a more effective method in searching for the optimal parameters for the controllers, compared with three other algorithms. As shown by the experimental results, the swing angle stabilization time of the novel algorithm is 6.9 s, while the values of the other algorithms range from 10.3 to 13.1 s under a common working condition. Simultaneously, the maximum swing angle of the novel algorithm is 7.8°, which is also better than the other algorithms.

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“…Many scholars have explored flexible production logistics. But few have integrated lowcarbon factors into flexible production logistics and multi-objective optimization system [26]. Concerning the optimization of carbon efficiency of production process, the existing studies all try to optimize the logistics distribution.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Flexible Production Logistics under Low Carbon Constraint

Wang¹,

Suo²

2022

Int. j. simul. model.

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Flexible production centring on machines or job-shops is necessary to meet the current trend of production and sales. By studying the energy consumption of production logistics, it is possible to minimize energy consumption and carbon emissions of the production process, without sacrificing production efficiency. The existing studies all try to optimize the logistics distribution. None of them attempt to optimize both production and logistics distribution parameters, and low carbon emission indices. Therefore, this paper explores the multi-objective optimization and carbon efficiency evaluation of flexible production logistics under low carbon constraint. Specifically, the constraint order between production logistics time, logistics quality, logistics cost, and low-carbon degree was determined, and the expressions of four linear programming models were given. Moreover, the carbon efficiency optimization of flexible production logistics was redefined, and a mathematical model was developed for the carbon efficiency optimization problem, which simultaneously optimizes carbon emissions and production indices. The proposed model was proved scientific through experiments.

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Production Management of Multi-Objective Flexible Job-Shop Based on Improved PSO

Cited by 7 publications

References 22 publications

Demand Prediction of Production Materials and Simulation of Production Management

Demand Prediction of Production Materials and Simulation of Production Management

Design of Anti-Swing PID Controller for Bridge Crane Based on PSO and SA Algorithm

Optimization of Flexible Production Logistics under Low Carbon Constraint

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