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。
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。
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.
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。
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.
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。
Job-Shop scheduling problem, as the core of manufacturing business, is a class of time constraints, sequence constraints, and resource constraints of the combinatorial optimization problem.
车间调度的优化问题，作为制造企业的核心，是一类具有时间约束、次序约束以及资源约束的组合优化问题。
The job-shop scheduling research belongs to the combination optimization category, it is the typical NP difficult question, its research has great theory significance and practical significance.
车间作业调度研究属于组合优化范畴，是典型的NP难问题，对它的研究具有重大的理论意义和现实意义。
A simple and easy operation method of coding and decoding was designed first. Then job-shop scheduling could be solved by conducting convergence and dissimilation of Mind Evolutionary Computation.
首先设计了简单、易操作的编码和解码方法，然后应用思维进化计算的趋同和异化操作求解该问题。
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。
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。
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。
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。
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。
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。
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。
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。
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