第一类问题为模糊柔性作业车间调度问题。
The first problem is the fuzzy flexible 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。
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。
This paper proposes a method of adaptive neural network based on constraint satisfaction for 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。
In this research field, it is a none-standard Job Shop Scheduling Problem.
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 second problem is the fuzzy flexible job shop scheduling problem with preventive maintenance.
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。
Multi Objective Flexible Job-shop Scheduling(MOFJS) was studied, and equipment dispatch scheme was optimized.
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。
Firstly, a Flexible Job-shop Scheduling model under uncertain information environment is proposed in this thesis.
建立了针对柔性作业车间的动态调度问题数学模型和目标函数。
To study the flexible job shop scheduling problem, the author builds mathematical model and establishes objective functions for flexible job shop dynamic scheduling.
适用：大型(工厂)厂房，仓库，大型建筑物、有害气体、高温作业车间。
Apply: large (plant) plants, warehouses, large buildings, hazardous gases, high temperature job-shop.
在研究作业车间模糊调度问题的基础上，建立了两种作业车间模糊调度模型。
Two kinds of model are formulated based on the studies of job shop fuzzy scheduling problem.
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。
As a popular research realm in manufacture system, Job Shop Scheduling is one of the most difficult problems in theoretic.
为了更好的解决作业车间调度问题，各种智能计算方法逐渐被引入到调度问题求解中。
In order to better solve the problem of workshop scheduling, a variety of intelligent computation methods have been gradually introduced to the solve of workshop scheduling.
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。
Thus, seeking the effective methods used to solve flexible job shop scheduling has important theoretical and applied significance.
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。
Most job shop scheduling algorithms deal with static scheduling, but in reality the shop is dynamic.
在作业车间调度问题中，存在大量的不可行调度解，严重影响了遗传算法查找最优调度的质量。
There are a number of unfeasible scheduling solutions in the Job-shop scheduling Problem (JSP), it seriously affects the quality of Genetic Algorithms (GA) searching for the best solution.
很多公司不提供住房，任由工人们在破败的贫民窟中寻找栖身之所，或干脆鼓励他们以作业车间为家。
Many companies did not supply housing, leaving workers to find shelter in dodgy slums or encouraging them to sleep on the assembly line.
在作业车间调度问题中，存在大量的不可行调度解，严重影响了遗传算法查找最 优调度的质量。
There are a number of unfeasible scheduling solutions in the Job-shop Scheduling Problem (JSP), it seriously affects the quality of Genetic Algorithms(GA) searching for the best solution.
多种时间因素作业车间的调度问题正越来越受到研究人员和技术人员的重视，成为研究的热点和重点。
The JSP with multi-time is paid more and more attention by the researchers and technologists, and it is becoming the focus and emphasis of research.
提出一种基于遗传算法的作业计划算法，用于解决作业车间的中小批量多工艺加工作业计划的优化问题。
This paper presented a scheduling approach developed to address the scheduling problem of small batch and multiple process routes in job shop environment.
针对作业车间调度问题，提出了最小化空闲时间的处理过程及其变异算子，设计了一种自适应遗传算法。
Classic scheduling benchmark problem test shows:the self-adaptive measure can efficiently keep current populations diversity, can use very small population size; shortest idle time .
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。
We introduce an intelligent scheduling system and give a case for job-shop scheduling and some dynamic optimum solutions to multi-objective function.
首先，针对经典作业车间调度问题的局限性，结合车间生产的实际情况，研究建立了有限能力作业车间调度模型。
Firstly, direct against the limitation of classical job-shop scheduling, combine the actual conditions of the workshop, deployment model of finite capacity job-shop scheduling.
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。
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。
Job oriented scheduling (JOS) has been the most commonly used in actual job shop scheduling. It loads jobs one by one onto machines.
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