With the increasing growth of data parallel applications, divisible job scheduling problem has been on the new focus of parallel scheduling area.
随着数据并行应用需求的日益增长，可任意划分负载的调度问题已经成为并行调度领域新的研究热点。
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。
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。
In the grouping jobs scheduling optimal problem, jobs are divided into several job classes according to their processing characters.
成组作业优化调度问题中的作业根据其加工特点要求可分成若干作业类。
Two kinds of model are formulated based on the studies of job shop fuzzy scheduling problem.
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。
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。
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。
We consider a single machine scheduling problem involving both the processing and scheduling of job delivery.
本文考虑了一个包含工件生产和工件送货的单机调度问题。
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。
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。
Job shop scheduling is a typical NP problem.
车间作业调度是典型的NP难题。
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 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。
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 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。
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。
The key technology to improve the grid system efficiency is the job scheduling algorithm, how integrate various factors to making the scheduling algorithm more comprehensive is a challenging problem.
提升网格系统调度效率的关键在于作业调度算法，如何综合各种因素使得调度策略更为全面是一个有挑战性的问题。
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。
This paper presented a scheduling approach developed to address the scheduling problem of small batch and multiple process routes in job shop environment.
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。
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。
To study the flexible job shop scheduling problem, the author builds mathematical model and establishes objective functions for flexible job shop dynamic scheduling.
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。
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 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。
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。
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