Job shop scheduling is a typical NP problem.
车间作业调度是典型的NP难题。
The first problem is the fuzzy flexible job shop scheduling problem.
第一类问题为模糊柔性作业车间调度问题。
In this research field, it is a none-standard Job Shop Scheduling Problem.
在该研究领域里，它属于非标准型的的作业车间调度问题。
Job shop scheduling is one of the effective measures to resolve the problem.
车间作业计划是解决这一难点的有效措施之一。
The dramatic characteristic of the job shop scheduling problem is its complexity.
车间调度问题的突出特点是其复杂性。
While applied to Job Shop scheduling Problems, it has some limitations to be solved.
但在解决单件车间作业计划问题时，仍存在一些局限。
The second problem is the fuzzy flexible job shop scheduling problem with preventive maintenance.
第二类问题为具有预防性维修的模糊柔性作业车间调度问题。
The purpose of this paper is using the algorithm to solve the dynamic Job shop scheduling problems.
在此基础上，本文尝试将缩短空闲时间法应用到多作业动态车间作业调度问题上。
Most job shop scheduling algorithms deal with static scheduling, but in reality the shop is dynamic.
目前多数作业车间调度算法考虑的是静态调度，但在实际生产中车间总是处于动态变化中。
A discrete Particle Swarm Optimization (PSO) algorithm was presented for Job Shop scheduling problem.
提出了用于解决作业车间调度问题的离散版粒子群算法。
Job shop scheduling is a complex NP problem, scheduling system need considerable flexibility mechanism.
车间调度问题是一个复杂的NP问题，车间调度系统需要具有相当的柔性机制。
The job shop scheduling problem is a classical problem with both highly theoretical and practical value.
工件车间调度问题具有很高的理论价值和实际价值。
In this paper, we propose an improved Lagrangian relaxation algorithm to solve job shop scheduling problems.
针对车间调度问题，提出了一种改进的拉氏松弛算法。
The Job shop scheduling problem is a combinatorial optimum problem that is constrained with time, sequence and resource.
车间作业调度问题是一类具有时间约束、次序约束和资源约束的组合优化问题。
As a popular research realm in manufacture system, Job Shop Scheduling is one of the most difficult problems in theoretic.
作业车间调度是制造系统的一个研究热点，也是理论研究中最为困难的问题之一。
Because there is much difficulty in processing in these two types of job shop scheduling, few optional algorithms are available.
由于这两类车间调度问题存在高度的计算难处理性，因而可供选择的算法比较少。
Thus, seeking the effective methods used to solve flexible job shop scheduling has important theoretical and applied significance.
因此，寻找有效的方法对柔性作业车间调度问题进行求解具有重要的理论价值和应用意义。
Then construct a distributed job shop scheduling system around the shop scheduling algorithm, so that it can be applied in practice.
接着围绕车间调度算法构建了分布式的车间调度系统，使其能在实际中得到应用。
Job oriented scheduling (JOS) has been the most commonly used in actual job shop scheduling. It loads jobs one by one onto machines.
面向作业调度在当今实际生产企业作业车间调度中得到普遍的应用，其基本思想是将作业一个个地安排到工作机器上。
In order to solve NP - shop scheduling combinatorial optimization problems, an immune forgotten algorithm for job shop scheduling is proposed.
为了解决车间调度NP组合优化的难题，提出了基于免疫遗忘的车间调度算法。
Finally, the design and realization of the job shop scheduling system based on GASA are expounded, the functions and operations of the system modules are introduced detailedly.
最后，本文阐述了基于混合算法的车间调度系统的设计与实现，详细介绍了各个模块的功能与操作。
The particle swarm algorithm is introduced to job shop scheduling operation. Consequently, the function of job shop scheduling and control of the integration module is realized.
把二倍体混合遗传算法引入动态车间优化调度运算，从而使集成模型中动态生产调度与控制功能得以实现。
Job shop scheduling problem is one of the typical combinatorial optimization problems with constraints. To get its encoding has always been one of the main and difficult points of the problem.
车间作业调度问题是一类带有约束的典型的组合优化问题，目前采用人工鱼群算法解决车间作业调度问题没有检索到参考文献。
The novel transition rule and the different pheromone reinforcement rules are discussed in this paper when ant colony systems are applied to minimizing the make-span in job shop scheduling problem.
在原有标准蚁群算法的基础上采用了新的状态转移规则，讨论了各种不同的轨迹更新规则对仿真结果的影响，并通过统计数据验证了改进型蚁群算法优于标准的蚁群优化算法。
The novel transition rule and the different pheromone reinforcement rules are discussed in this paper when ant colony systems are applied to minimizing the make-span in job shop scheduling problem.
在原有标准蚁群算法的基础上采用了新的状态转移规则，讨论了各种不同的轨迹更新规则对仿真结果的影响，并通过统计数据验证了改进型蚁群算法优于标准的蚁群优化算法。
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