Description: 该为遗传算法解决比赛项目排序问题，给出了过程分析，列出了比赛项目排序的模型，进行了方法的研究，最后用c语言编程实现了矩阵的导入，矩阵的转化，遗传算法．程序在解决此类问题，效果良好-The events for the genetic algorithm to solve scheduling problem, given the process of analysis, a list of events sort of model, carried out research on methods to the final with c language programming into a matrix, matrix transformation, genetic algorithm . Procedures in resolving such problems, the effect of good Platform: |
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Author:gexiaozhong |
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Description: 遗传算法车间调度,车间作业调度问题遗传算法
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输入参数列表
M 遗传进化迭代次数
N 种群规模(取偶数)
Pm 变异概率
T m×n的矩阵，存储m个工件n个工序的加工时间
P 1×n的向量，n个工序中，每一个工序所具有的机床数目
输出参数列表
Zp 最优的Makespan值
Y1p 最优方案中，各工件各工序的开始时刻，可根据它绘出甘特图
Y2p 最优方案中，各工件各工序的结束时刻，可根据它绘出甘特图
Y3p 最优方案中，各工件各工序使用的机器编号
Xp 最优决策变量的值，决策变量是一个实数编码的m×n矩阵
LC1 收敛曲线1，各代最优个体适应值的记录
LC2 收敛曲线2，各代群体平均适应值的记录
最后，程序还将绘出三副图片：两条收敛曲线图和甘特图（各工件的调度时序图）-Genetic algorithm scheduling, job shop scheduling problems with genetic algorithms -------------------------------------------------------------------------- genetic evolution of the input parameter list, the number of iterations M N population size (taken even) Pm mutation probability T m × n matrix, stored m one piece n a process of processing time 1 × n vector, n a process in which each machine processes the number of Zp with the best value Y1p Optimal Makespan programs, the start time of each process the workpiece can be drawn based on its optimal solution Gantt Y2p, each time the workpiece end of the process, according to its draw Gantt Y3p optimal solution, each piece of the processes using machine code Xp optimal decision variable, decision variable is a real m × n matrix encoded LC1 convergence curve 1, the generation of the best individual record of LC2 fitness convergence curve 2, the average fitness value on behalf of groups record Finally, the program will draw three pict Platform: |
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Author:王龙隐 |
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Description: 用遗传算法解决作业车间调度问题。作业车间调度可描述为集合{J，M，Op，T}(其中:J代表工件，M代表机器，Op代表工序，T代表工件的加工时间)，即如何将工件的工序按照合理的顺序分配到机器上，以达到目标函数的要求。-Using Genetic Algorithm for Job Shop Scheduling Problems. Job shop scheduling can be described as a set {J, M, Op, T} (where: J on behalf of the workpiece, M stands for machine, Op behalf of processes, T on behalf of the workpiece processing time), that is how the parts of the process in accordance with the allocation of a reasonable order to the machine in order to achieve the requirements of the objective function. Platform: |
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Author:刘浏 |
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Description: 遗传算法车间调度,车间作业调度问题遗传算法,各工件各工序的结束时刻，可根据它绘出甘特图,各代群体平均适应值的记录.-The genetic algorithm shop scheduling, job shop scheduling problems with genetic algorithms, each moment of the end of the process of the workpiece, it plotted the Gantt chart, the average fitness of the population values recorded. Platform: |
Size: 51200 |
Author:陈湖 |
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Description: 流水线型车间作业调度问题可以描述如下：n个任务在流水线上进行m个阶段的加工，每一阶段至少有一台机器且至少有一个阶段存在多台机器，并且同一阶段上各机器的处理性能相同，在每一阶段各任务均要完成一道工序，各任务的每道工序可以在相应阶段上的任意一台机器上加工，已知任务各道工序的处理时间，要求确定所有任务的排序以及每一阶段上机器的分配情况，使得调度指标(一般求Makespan)最小。下面的源码是求解流水线型车间作业调度问题的遗传算法通用MATLAB源码，属于GreenSim团队原创作品，博客上发布的是不完全的源码，本人将其补充完成已成功运行，转载请注明GreenSim团队原创作品。-Pipelined job-shop scheduling problem can be described as follows: n tasks on the assembly line m processing stages, each stage has at least one machine and at least one stage there are multiple machines, and the same processing performance of each machine on the same stage , at every stage to complete each task are a process, each process of each task can be processed on any machine on the corresponding stage of the processing time of each process known tasks, all tasks required to determine the sort and every Distribution of the machine on a stage, making scheduling index (typically seek Makespan) Min. The following code is a Genetic Algorithm for Job Shop Scheduling Problem pipelined general MATLAB source code, original works belong GreenSim team, the blog post is incomplete source code, I will supplement has been successfully completed its operation, please indicate the original team GreenSim works. Platform: |
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Author:yyl |
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Description: 流水线车间生产调度的遗传算法MATLAB源代码
n个任务在流水线上进行m个阶段的加工，每一阶段至少有一台机器且至少有一个阶段存在多台机器，并且同一阶段上各机器的处理性能相同，在每一阶段各任务均要完成一道工序，各任务的每道工序可以在相应阶段上的任意一台机器上加工，已知任务各道工序的处理时间，要求确定所有任务的排序以及每一阶段上机器的分配情况，使得调度指标(一般求Makespan)最小-Line workshop production scheduling genetic algorithm MATLAB source code n task m phase of the process on the assembly line, each phase and at least one machine exist multiple machines at least one stage, and all the same, the processing performance of the machine on the same phase in each stage of the task to complete the procedure, the task of every working procedure can be found in the corresponding stage of any one machine processing, known task processing time of each working procedure request sort of all the tasks and each stage of the distribution of the machine, making scheduling index minimum Makespan (o) Platform: |
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Author:樊奕 |
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Description: 遗传算法（Genetic Algorithm）是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法。遗传算法可以解决多种优化问题，如：TSP问题、生产调度问题、轨道优化问题等，在现代优化算法中占据了重要的地位，本例使用遗传算法求解最优解。-Genetic Algorithm (Genetic Algorithm) is a simulation of Darwinian natural selection and genetic mechanism of biological evolution of computational models of biological evolution, the process is a method of searching the optimal solution by simulating natural evolution. Genetic algorithms can solve a variety of optimization problems, such as: TSP problem, production scheduling, track optimization problems in modern optimization algorithms to occupy an important position, in this case the use of genetic algorithm for optimal solutions. Platform: |
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Author:jiangsiqi |
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Description: 为了解决考虑生产总成本的车间调度问题，对引入加工成本、库存管理成本和拖期惩罚成
本的车间调度问题进行了研究，提出了一种改进的遗传算法求解该问题。论述了改进遗传算法的
编码、初始化、选择、交叉和变异操作，通过与基本遗传算法的仿真比较，验证了该算法在收敛速度
和最优解值上的优越性。
-In order to solve the job shop scheduling problem considering the total production cost, the process cost, inventory management cost and delay penalty are introduced.
This job shop scheduling problem is studied, and an improved genetic algorithm is proposed to solve this problem. The improved genetic algorithm is discussed.
Encoding, initialization, selection, crossover and mutation operation, the convergence speed of the algorithm is verified through simulation comparison with the basic genetic algorithm.
And the superiority of the optimal solution.
Platform: |
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Author:袁杰 |
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Search - scheduling genetic process