不确定条件下的作业车间多目标调度模型及其遗传优化

针对不确定条件下的作业车间特点，区别于传统调度模型采用决策系数策略，提出了一种新的不确定条件下的多目标车间调度模型，新模型为两维调度模型，不仅处理了生产过程中的应急调度问题，还在调度目标为作业时间最短的基础上，从库存、机器一人资源约束、生产效率三个方面归纳了企业相关成本调度指标，具有很高的实用性；采用改进的遗传算法求解新模型，得到一组Pareto最优解，可以在兼顾机器使用率的同时使生产成本最小化．最后采用某机车厂的部分生产数据对改进的模型及算法进行了仿真实验，结果证明了模型和算法的有效性．

A More-Target Job Shop Scheduling Model with Uncertain Conditions and Its Genetic Optimization

For the job shop characteristics under uncertain conditions which are different from the traditional more-target scheduling model using decision-making factor strategy, a new more-target job shop scheduling model under uncertain conditions is proposed. The new model has two-dimensional scheduling function, and it can deal with emergency scheduling problem in the production process and shorten scheduling time based on the scheduling goal with high availability considering production cost and inventory, machinery and human resource constraints. The improved genetic algorithm is used to solve the new model, and a set of Pareto optimal solution is obtained to balance the machine utilization while minimizing the cost of production. Employing the production data of a locomotive factory on simulation results of the improved model and algorithm show the effectiveness of the model and algorithm.