Flow shop scheduling problems.
流水作业排序问题。
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。
Then, found its application in flow shop scheduling problems with two objects.
并应用该算法解决了双目标的流水车间调度问题。
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。
A shop scheduling prototype system based on multi agent architecture is implemented.
本文实现了一个基于多智能体结构的车间调度原型系统。
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。
An ant colony optimization based algorithm for permutation flow shop scheduling was proposed.
提出了一种求解置换流水车间调度的蚁群优化算法。
Flow-shop scheduling problem is known to be notoriously intractable, both in theory and practice.
流水作业调度问题在理论和实践方面均是一个著名的难题。
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。
Multi Objective Flexible Job-shop Scheduling(MOFJS) was studied, and equipment dispatch scheme was optimized.
研究了多目标柔性作业车间调度问题，优化了设备分派方案。
Firstly, a Flexible Job-shop Scheduling model under uncertain information environment is proposed in this thesis.
本文首先建立了不确定信息条件下的柔性作业车间调度模型。
Therefore, the choice of sea area is the coupling of the facility location problem and job-shop scheduling problem.
因此试验海区的选择是工厂选址运输问题和车间调度问题的耦合。
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。
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。
A hybrid flow shop scheduling problem with no-wait is derived from the optimal matching of furnaces and machines in steel making casting production.
将炼钢连铸生产计划中炉机优化匹配问题归结为一个不允许等待的混合流水车间排序问题来进行研究，提出了一个启发式算法-最小偏差算法。
We introduce an intelligent scheduling system and give a case for job-shop scheduling and some dynamic optimum solutions to multi-objective function.
介绍了基于生物免疫机理的智能生产调度关键算法，给出了作业车间生产调度案例及其多目标动态优化结果。
However, this way of introducing MES to strengthen job-shop scheduling and improve the running efficiency of plans faces many problems in the process.
然而，这种通过引入MES来加强车间级的生产计划调度、提高中长期计划的运行效率的方法在实际实施过程中遇到了很多问题。
No-wait flow shop scheduling problems with total flow time minimization is an important manufacturing system widely existing in industrial environments.
以总完工时间为目标的无等待流水车间调度问题是一个重要的制造加工系统，广泛应用于工业生产中。
No-wait flow shop scheduling problems with total flow time minimization is an important manufacturing system widely existing in industrial environments.
以总完工时间为目标的无等待流水车间调度问题是一个重要的制造加工系统，广泛应用于工业生产中。
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