An improved genetic algorithm based on NSGA-Ⅱnon-dominated sorting is proposed to solve flexible job-shop scheduling problem. Makespan and machine maximum load are chosen as objective function to establish multi-objective optimization model. Real encoding and plug-in decoding are used to transform chromosomes and scheduling schemes. NSGA-Ⅱnon-dominated sorting with elite reserved strategy is designed to improve search efficiency, and different strategies for selection, cross and mutation are adopted. The feasibility and effectiveness of the algorithm are verified by simulation and results from comparison with other algorithms.
A Two-level method is proposed to solve the one-dimensional cutting-stock problem during the production process in this paper. First, nested layer cycle is designed to enumerate all the feasible cutting patterns, then a integer linear programming model is established with quantity demands as the constraint. The best cutting scheme is obtained finally according to the branch and bound method. The effectiveness of the proposed method is proved through a real world example, the calculations demonstrate that scheme obviously improves the utilization rate of stock
Current scheduling approach for multiple objective flexible job shop problem (FJSP) cannot construct a precise scheduling model and obtain a satisfactory scheduling result at the same time. To deal with this problem, a simulation optimization scheduling approach was presented which was composed of two basic modules: the Fast non-dominated Sorting Genetic Algorithm (NSGA-II) module and Witness simulation module. Firstly, a multi-objective mathematical model was found for FJSP and NSGA-II was applied to solve. Then, a set of Pareto optimal solution was obtained by NSGA-II module. In order to select the final solution from the Pareto optimal solution for FJSP, the simulation model was set up by Witness, every Pareto solution was as input for simulation model. Finally, the final solution can be selected according other performance indicators.
This paper proposes an effective genetic algorithm for the job-shop scheduling problem (JSP) to minimize makespan time. An effective chromosome representation based on real coding is used to conveniently represent a solution of the JSP, and different strategies for selection, crossover and mutation are adopted. Simulation experimental results have shown that the scheduling model using the algorithm can allocate jobs efficiently and effectively.
This paper conducts numerical simulation to a 15-stage civil axial flow compressor and obtains its main parameters distribution and performance curve by a full three-dimensional viscid flow computation software. The computation result indicates that, the developed axial flow compressor meets the anticipated design requirements, and satisfies the customers’ indicators. Under the designed compression ratio, the difference between the maximum air supply quantity in summer and the minimum air supply quantity in winter is 22%. By comparing the operating conditions and data analysis, obtained the change trend of axial velocity, static pressure and temperature, and Ma, and discovered that, under opening of 48° and outlet back pressure of 550KPa, flow separation occurred on the section of machine set close to hud, which indicated that operating condition was close to surging condition.
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