生产计划调度大作业_生产计划调度单
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《作业车间调度的非合作博弈模型与混合自适应遗传算法》 作者：周光辉，王蕊，江平宇，张国海
摘要：采用博弈理论，建立了一种基于非合作博弈的作业车间任务调度模型，在该任务调度模型中，将源于不同客户的制造任务映射为非合作博弈模型中的局中人，并将与制造任务包括的工序集所对应的可选加工设备映射为可行方案集，将使各制造任务的加工完成时间和成本组合形成的多目标综合指标映射为收益函数，从而将对任务调度模型的求解转换为寻求非合作博弈模型的Nash均衡点，通过设计的爬山搜索混合自适应遗传算法、自适应交叉和变异算子，实现了对该任务调度非合作博弈模型的Nash均衡点的有效求解，同时算例仿真结果也验证了所提出的调度方法的正确性。
根据数学模型和假设条件，竞争驱动的作业车间任务调度目标就是寻求使得每个制造任务均能达到综合目标值最小、利益均衡的调度结果。
《基于自适应遗传算法的Job Shop 调度问题研究》 作者：沈斌，周莹君，王家海
Job Shop 求解过程的计算量随问题的规模呈指数增长，已被证明是NP完全问题。因此近年来倾向于利用人工智能的原理和技术进行搜索，寻找复杂问题的较优解，特别是以效仿生物处理模式以获得智能信息处理功能的遗传算法研究最为深入。但是也有不足之处，早熟收敛问题，局部搜索能力，算子的无方向性，正因为这些不足限制了以遗传算法的进一步推广和应用，因此对遗传算法进行改进显得尤为重要。本文提出一种新的自适应遗传算法用以求解Job Shop调度问题。
Job Shop问题描述
一个加工系统有m台设备，要求加工n个工件，第i个工件ji包含m个操作（工序），需要考虑如下假设：
1）每道工序必须按照工艺顺寻依次在指定的设备上加工，且必须在前一道工序（如果存在））加工完成后才一开始加工；
2）工件在一台设备上一旦开始加工，便不能中断，必须等到加工完成后，才能加工另外工件，即某一时刻一台设备只能加工一个工件； 3）同一个工件不能同时在两个设备上加工；
4）同一台设备不能同时加工两个工件；
5）每个工件在每台设备上必须加工一次，也只能加工一次；
6）各工件的工艺路线jsn和每到工序的加工时间jt已知，且不随加工排序的改变而改变，转移时间和辅助时间忽略不计或计入加工时间。
《A Hybrid Genetic Algorithm for Job Shop Scheduling Problem to Minimize Makespan》 作者：Lin Liu, Yugeng Xi
In this paper, we present a hybrid genetic algorithm for the job shop scheduling problem to mimize makespan.How to improve GA performance is a critical iue when using a GA to solve optimization problems.The general way focuses on tuning its parameters such as population size, croover rate and mutation rate.However, if all parameters have attained the useful bounds, the expected improvement is often not worth the efforts of finding even better parameters.More potential improvements can be only explored by modifying the size of search space.The set of active schedules is usually large and includes a lot of schedules with relatively large idle times on machines, and thus with relatively large idle times on machines, and thus with poor performance in terms of makespan.The proposed algorithm used the idea of hybrid scheduler to reduce the search space as well as the computational efforts.The search space can be reduced or increased by controlling the upper bound of idle times allowed on machines.Since the parameters of the hyubrid scheduler are unlikely to be determined appropriately in advance, we search better values of them in the hybrid GA evolution.Diimilar to Gas in literatures, a chromosome includes not only genes representing the relative priorities of all operations but also genes representing the parameters to determine the upper bound of idle times permitted on a given machine before scheduling an operation.The random keys representation is used to encode a chromosome.Each element of the chromosome is a real number of [0,1].During the schedule generation phase, the SPV rule is used to convert a real number vector into a job repetition representation.Based on the hybrid scheduler, a chromosome is decoded into a feasible schedule.Finally, a local search is executed in the neighborhood determined by the critical active chain to improve the performance of the schedule generated in the schedule generation phase.nd In the 2section, we present the formulation of job shop scheduling problem to minimize makespan.In the 3 section, we describe the proposed hybrid genetic algorithm in detail.In the 4 section, the proposed algorithm is evaluated on benchmark instances.Finally, we conclude the paper with a summary in 5th section.《Hybrid Genetic Algorithm for Solving Job-Shop Scheduling Problem》 作者：S.M.Kamrul Hasan
The Job-Shop Scheduling Problem(JSSP)is a well-known difficult combinatorial optimization problem.Many algorithms have been proposed for solving JSSP in the last few decades, including algorithms based on evolutionary techniques.However, there is room for improvement in solving medium to large scale problems effectively.In this paper, we present a Hybrid Genetic Algorithm(HGA)that includes a heuristic job ordering with a Genetic Algorithm.We apply HGA to a number of benchmark problems.It is found that the algorithm is able to improve the solution the solution obtained by traditional genetic algorithm.《Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm》
Most flexible job shop scheduling models aume that the machines are available all of the time.However, in most realistic situations, machines may be unavailable due to maintenances, pre-schedules and so on.In this paper, we study the flexible job shop scheduling problem with availability constraints.The availability constraints are non-fixed in that the completion time of the maintenance tasks is not fixed and has to be determined during the scheduling procedure.We then propose a hybrid genetic alogorithm to solve the flexible job shop scheduling problem with non-fixed availability constraints.The genetic algorithm uses an innovative representation method thrdand applies genetic operations in phenotype space in order to enhance the inheritability.We also define two kinds of neighbourhood for the problem based on the concept of critical path.A local search procedure is then integrated under the framework of the genetic algorithm.Representative flexible job shop scheduling benchmark problems and fJSP-nfa problems are solved in order to test the the effectivene and efficiency of the suggested methodology.《A Hybrid genetic algorithm for no-wait job shop scheduling problems》 作者：Jason Chao-Hsien Pan, Han-Chiang Huang
A no-wait job shop describes a situation where every job has its own proceing sequence with the constraint that no waiting time is allowed between operations within any job.A NWJS problem with the objective of minimizing total completion time is a NP-hard problem and this paper proposes a hybrid genetic algorithm(HGA)to solve this complex problem.A genetic operation is defined by cutting out a section of genes from a chromosome and treated as a subproblem.This subproblem is then transformed into an asymeetric traveling salesman problem(ATSP)and solved with a heuristic algorithm.Subsequently, this section with new sequence is put back to replace the original section of chromosome.The incorporation of this problem-specific genetic operator is responsible for the hybrid adjective.By doing so, the course of the search of the proposed genetic algorithm is set to more profitable regions in the solution space.The experiemental results show that this hybrid genetic algorithm can accelerate the convergence and improve solution quality as well.
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