工件车间调度问题具有很高的理论价值和实际价值。
The job shop scheduling problem is a classical problem with both highly theoretical and practical value.
针对车间调度问题，提出了一种改进的拉氏松弛算法。
In this paper, we propose an improved Lagrangian relaxation algorithm to solve job shop scheduling problems.
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.
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.
提出了一种求解置换流水车间调度问题的离散粒子群优化算法。
Solving Rectangular Packing Problem Based on Discrete Particle Swarm Optimization Algorithm;
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。
Therefore, the choice of sea area is the coupling of the facility location problem and job-shop scheduling problem.
阐述研究基于未来敏捷制造模式的虚拟车间调度问题的重要性和必要性。
In this paper, the importance and necessity are expressed, which are concerned with the scheduling problems in virtual shop oriented to future agile manufacturing.
炼钢-连铸生产的浇次组合与排序是带有工艺约束的并行机流水车间调度问题。
The cast grouping and ordering problem of steelmaking continuous casting hot rolling (SM-CC) is a combinatorial optimization problem.
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。
Job shop scheduling is a complex NP problem, scheduling system need considerable flexibility mechanism.
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。
Because there is much difficulty in processing in these two types of job shop scheduling, few optional algorithms are available.
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.
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.
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.
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.
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 .
针对混合流水车间调度问题，提出了分阶段加工优先级的调度原则，并将迭代局部搜索算法应用于求解此问题。
Multistage processing priority was developed for the hybrid flow shop problem and an iterated local search algorithm was used for solving this kind of problem.
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.
产品开发和生产是制造型企业运营的两个关键环节，产品开发项目调度和车间调度问题是这两个关键环节中的核心问题。
Production development and manufacturing are playing important parts in this process, and the problems of production development project scheduling and shop scheduling are the kernel of them.
本论文依据邻域结构和禁忌列表这两个关键点，提出了一种基于混合邻域结构的禁忌搜索算法来对加工车间调度问题进行研究。
In this paper, based on the neighborhood structure and the tabu list, a tabu Search algorithm based on hybrid neighborhood structure is propsed for solving JSSP.
车间生产调度问题是企业生产管理系统中需要解决的重要问题之一。
Production scheduling is one of the important problems which should be settled in the production management system of enterprises.
车间生产调度问题是企业生产管理系统中需要解决的重要问题之一。
Production scheduling is one of the important problems which should be settled in the production management system of enterprises.
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