基于混合粒子群算法具有交货期瓶颈的作业车间调度问题

为了解决一类具有交货期瓶颈的作业车间调度问题，给出了基于订单优势的交货期满意度和交货期瓶颈资源确定方法，以工件拖期加权和最小为优化目标，建立了基于交货期满意度和瓶颈资源约束的作业车间调度模型;为了求解该调度模型，设计了一种基于模拟退火的混合粒子群算法，该算法采用随机工序表达方式进行编码，并在模拟退火算法中引入变温度参数来提高算法效率。通过随机仿真，分别采用PSO-SA、SA和PSO对所建立的调度模型进行求解，结果显示PSO-SA算法的广泛性好、求解效率高且算法的稳定性好，验证了模型和算法的有效性。

Job Shop Scheduling for Due-time Bottleneck Based on HPSO Algorithm

To solve a kind of job shop scheduling problem for due-time bottleneck, taking the tardiness weighted sum as the optimization objective, a model based on due-time degree of satisfaction and bottleneck resources was presented. According to the characteristics of job shop, a hybrid algorithm with particle swarm optimization and simulated annealing algorithm was proposed to solve the model. In the hybrid algorithm, a random process expression method for encoding was taken. And a dynamic temperature parameter was introduced to the simulated annealing algorithm to increase the algorithm's efficiency. The simulation was given to test the model and algorithms presented herein. And the testing results prove the method is effective.