存在多工序同时结束的综合调度算法

针对实际生产中存在多工序同时结束约束条件的复杂产品综合调度问题, 提出了存在多工序同时结束的综合调度算法。该算法将具有同时结束约束条件的工序虚拟成一个工序组, 在提出虚拟工序组概念的基础上, 对虚拟工序组的前、后序标准工序, 采用拟关键路径法和首次适应调度算法确定工序的调度顺序和开始加工时间; 对虚拟工序组中工序采用预计晚结束优先策略和同时结束策略确定调度顺序和开始加工时间。实例表明, 所提出的综合调度算法不仅能在较低的复杂度时解决具有实际意义的工序间存在多工序同时结束约束的复杂产品综合调度问题, 而且调度效果更好。     

基于工序集的动态关键路径多产品制造调度算法

针对已有的树状结构多产品加工和装配综合调度方法,不能兼顾树状结构纵横双向、影响调度结果的问题,提出基于工序集的动态关键路径多产品制造调度算法.该算法首先采用根对齐的方法,将多个树状结构的产品转化成一个虚拟加工树,使多产品调度问题简化为单产品调度问题;在调度过程中,根据虚拟加工树动态生成可调度的备选工序集合,按提出的动态...     

紧密衔接工序组联动的综合调度算法

由于现有的工序间存在紧密衔接条件的复杂产品综合调度问题, 采用的移动交换算法不易于软件实现且没有考虑移动工序后产生的连锁反应引起较高算法复杂度的问题, 提出将具有紧密衔接约束条件的工序组进行统一联动的综合调度算法.该算法利用将具有紧密衔接约束条件的工序分组的扩展加工工艺树模型, 按路径上属于工序组的工序个数多少确定所在路径工序组调度的次序, 通过降低对工序组的限制要求降低算法复杂度; 对于被调度工序组中各工序的前序工序, 按工序组中工序的加工顺序确定调度次序, 对某个工序的前序工序采用复杂度较低的拟关键路径法确定工序的调度次序; 调度完所有紧密衔接工序组后, 剩余的标准工序按拟关键路径法确定调度顺序; 采取工序首次适应调度算法调度标准工序和工序组, 由于工序组中工序采取按序紧密衔接的联动调度方式确定工序组的开始时间, 避免了二次调整, 进一步降低了算法复杂度. 分析和实例表明, 所提出的综合算法比以往算法复杂度更低, 调度结果更优且更易于实现.     

考虑后续工序的择时综合调度算法

针对目前综合调度算法不能兼顾产品工艺树中并行工序的并行性和串行工序之间紧密度,影响调度结果的问题,提出考虑后续工序的择时综合调度算法.该算法提出工序序列排序策略,从工艺树的整体结构出发,将其划分成若干内部工序只具有串行关系的工序序列,并按路径长度从长到短的顺序确定其调度次序;提出择时调度策略和考虑后续工序策略,根据工艺树自身特点,从来自不同工序序列的并行工序的不同组合方案中,选择最接近调度目标的方案作为工序调度方案,若该工序调度方案不唯一,则在其中选择该工序加工开始时间最早的调度方案.该算法既保证了工序的并行处理,又提高了串行工序的紧密度,优化了综合调度的结果.最后通过实例说明本文算法对解决综合调试问题具有普遍意义.     

基于剪枝分层的柔性加工车间调度算法

针对柔性作业车间调度中工序间存在的冗余调度次序约束关系问题和工序-设备间存在的多加工模式情况,提出基于剪枝分层的柔性加工车间调度算法.该算法首先用有向无环图表示工序及工序间的调度次序关系,采用剪枝法消除图中的冗余弧,采用分层法对图中结点分层;其次对加工模式进行分类,制定工序-设备预约策略和工序-设备预分配策略;最后,采用事件驱动策略,驱动时刻按所提出的柔性加工策略调度工序加工.理论分析和实例表明,所提出的算法具有较好的调度效果.     

可动态生成具有优先级工序集的动态Job-Shop调度算法

为了在满足约束条件的前提下使不同时刻加入到作业集合中的全部作业所用的加工总时间尽可能地少,提出算法:根据动态的作业集合构造虚拟加工树,虚拟加工树上的叶结点代表最先加工的工序,虚拟根节点代表最后一道工序,边代表偏序关系;以层优先为虚拟加工树上的工序设置优先级,工序的优先级可作为调度过程中考虑的一个因素;除了在工序需要动态调整的情况下,在调度过程中始终遵循着机器忙原则.在调度过程中,首先根据虚拟加工树动态地生成备选工序集合,然后根据工序的优先级并且结合其它的调度策略从备选集合中调度工序,直到备选集合为空,即所有作业加工完毕.这里提到的调度策略包括短用时策略、长路径策略和动态调整策略.通过实例验证,该算法对于动态Job-Shop问题可以获得比较好的解.     

基于调度长路径的复杂产品综合调度算法

针对目前复杂产品加工和装配综合调度算法中考虑纵横双向调度优化时以横向为主,而忽略产品工序内在纵向约束对制造效率产生主要影响的问题,提出了以关键路径为主的纵横双向调度优化算法——基于调度长路径的复杂产品调度算法。该算法考虑了复杂产品树状工艺结构,根据优先级策略并结合调度长路径和长用时等策略来确定工序的调度次序。采用优先级策略可以兼顾其他分支上的同层工序;采用调度长路径策略是在兼顾其他分支的同时优先考虑了关键路径上工序对总加工时间的影响;采用长用时策略可以优先调度对加工时间影响大的工序。对已定调度次序的工序采用工序尽早加工的原则确定开始加工时间。实例表明,提出的调度策略简便可行且调度结果更优。     

工件工序可并行的作业车间调度模型与求解

针对传统作业车间调度模型没有考虑工件工序存在并行性的不足,提出一种以最小化完工时间为目标的工件工序可并行作业车间调度模型,且在模型中考虑了工序加工设备柔性;设计了基于遗传算法的调度算法,其中染色体编码采用分段编码方式,并提出一种适用于工件工序存在并行性的染色体解码方法.实验结果表明,文中算法能够有效地解决工件工序可并行的作业车间调度问题.     

基于动态规划的资源受限随机工序调度

为解决资源受限条件下的随机工序调度问题,该文提出一种基于离散随机动态系统描述的加工时间离散随机分布且同时具有不兼容和多种可更新资源约束的资源受限项目调度模型,使得在满足资源约束和工序约束的前提下,总的平均加工时间最短。该系统研究了动态规划算法求解该问题的方法。通过实例,验证了该方法的有效性和可行性。     

基于ACPM和BFSM的动态Job-Shop调度算法

通过对不同时刻开始加工的产品加工树的分解，可将产品加工工序分为具有惟一紧前、紧后的相关工序和独立工序．在对这两类工序研究分批综合应用拟关键路径法(ACPM)和最佳适应调度方法(BFSM)调度时，考虑了关键设备的工序紧凑性．通过分析与实例验证，所提出的调度方法对解决动态的Job—Shop调度问题不仅算法简练而且效果较好．     

A new algorithm for complex product flexible scheduling with constraint between jobs

In order to simplify the complex product flexible scheduling problem with constraint between jobs, a new hierarchical scheduling algorithm based on improved processing operation tree is presented. Aiming at the routing problem, short-time strategy and machine-balance strategy are adopted to assign each operation to a machine out of a set of machines. And in order to solve the sequencing problem, the allied critical path method is first adopted to confirm the scheduling sequence of operations, and then operations are divided into dependent operations and independent ones according to their characteristics. For the dependent operations, forward greedy rule is adopted in order to make the completion time of operation as soon as possible and the scheduling algorithm of shortening idle time is adopted by analyzing the characteristics of the independent operations. Experiment shows that the proposed algorithm solves for the first time the complex product flexible scheduling problem with constraint between jobs.     

Branch-and-bound and PSO algorithms for no-wait job shop scheduling

This paper deals with the no-wait job shop scheduling problem resolution. The problem is to find a schedule to minimize the makespan (\(C_{max}\)), that is, the total completeness time of all jobs. The no-wait constraint occurs when two consecutive operations in a job must be processed without any waiting time either on or between machines. For this, we have proposed two different resolution methods, the first is an exact method based on the branch-and-bound algorithm, in which we have defined a new technique of branching. The second is a particular swarm optimization (PSO) algorithm, extended from the discrete version of PSO. In the proposed algorithm, we have defined the particle and the velocity structures, and an efficient approach is developed to move a particle to the new position. Moreover, we have adapted the timetabling procedure to find a good solution while respecting the no-wait constraint. Using the PSO method, we have reached good results compared to those in the literature.     

The two-machine flowshop no-wait scheduling problem with a single server to minimize the total completion time

This work studies the scheduling problem where a set of jobs are available for processing in a no-wait and separate setup two-machine flow shop system with a single server. The no-wait constraint requires that the operations of a job have to be processed continuously without waiting between two machines. The setup time is incurred and attended by a single sever which can perform one setup at a time. The performance measure considered is the total completion time. The problem is strongly NP-hard. Optimal solutions for several restricted cases and some properties for general case are proposed. Both the heuristic and the branch and bound algorithms are established to tackle the problem. Computational experiments indicate that the heuristic and the branch and bound algorithm are superior to the existing ones in term of solution quality and number of branching nodes, respectively.     

基于逆序信号驱动的紧密衔接综合调度算法

针对以往综合调度中紧密衔接调度算法只能处理单一紧前工序的情况,使算法具有局限性问题,提出基于逆序信号驱动的紧密衔接综合调度算法.该算法先建立设备和调度2个子系统,并通过相互间传递的信号驱动逆序调度;对于紧密衔接工序组包含非单一紧前工序的情况,将紧密衔接工序组定义为一棵特殊的紧密衔接子树,采用逆序调度的方式自顶向下对紧密衔接问题求解;当信号驱动时刻存在多个可调度工序(组)时,按最大可并行性选择策略选择子节点关键路径长的工序(组)调度;对于可调度工序直接锁定该工序的加工时间段;对于可调度工序组,则按锁定紧密衔接工序组的前沿贪心策略锁定工序组的加工时间段.由于采用逆序调度和锁定紧密衔接工序组的前沿贪心策略,可使各紧密衔接工序组独立调度,实现紧密衔接紧前工序数无限制的综合调度.     

Production capacity planning and scheduling in a no-wait environment with controllable processing times: An integrated modeling approach

This paper develops an integrated model between a production capacity planning and an operational scheduling decision making process in which a no-wait job shop (NWJS) scheduling problem is considered incorporating with controllable processing times. The duration of any operations are assumed to be controllable variables based on the amount of capacity allocated to them, whereas in classical NWJS it is assumed that the machine capacity and hence processing times are fixed and known in advance. The suggested problem which is entitled no-wait job shop crashing (NWJSC) problem is decomposed into the crashing, sequencing and timetabling subproblems. To tackle the addressed NWJSC problem, an improved hybrid timetabling procedure is suggested by employing the concept of both non-delay and enhanced algorithms which provides better solution than each one separately. Furthermore, an effective two-phase genetic algorithm approach is devised integrating with hybrid timetabling to deal with the crashing and sequencing components. The results obtained from experimental evaluations support the outstanding performance of the proposed approach.     

On the exact solution of the no-wait flow shop problem with due date constraints

This paper deals with the no-wait flow shop scheduling problem with due date constraints. In the no-wait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, the jobs should be completed before their respective due dates; due date constraints are dealt with as hard constraints. The considered performance criterion is makespan. The problem is strongly NP-hard. This paper develops a number of distinct mathematical models for the problem based on different decision variables. Namely, a mixed integer programming model, two quadratic mixed integer programming models, and two constraint programming models are developed. Moreover, a novel graph representation is developed for the problem. This new modeling technique facilitates the investigation of some of the important characteristics of the problem; this results in a number of propositions to rule out a large number of infeasible solutions from the set of all possible permutations. Afterward, the new graph representation and the resulting propositions are incorporated into a new exact algorithm to solve the problem to optimality. To investigate the performance of the mathematical models and to compare them with the developed exact algorithm, a number of test problems are solved and the results are reported. Computational results demonstrate that the developed algorithm is significantly faster than the mathematical models.     

微电子生产过程调度问题基于指标快速预报的分解算法

微电子生产过程调度问题具有规模大和约束复杂等特点,如菜单、Setup时间和组批约束等,其优化调度具有一定难度.针对以最小化平均流经时间为调度目标的较大规模微电子生产过程调度问题,提出一种基于指标快速预报的分解方法(DM-IFP).首先,通过松弛不可中断约束,设计一种代理方法,即基于机器负载的操作完工时间快速预测方法(CTP-ML);其次,设计基于CTP-ML的问题分解方法,将原问题迭代分解为多个连续交迭的子问题;然后,提出一种基于双信息素的蚁群算法(ACO-D)用于求解分解后的子问题,其全局调度目标采用CTP-ML获取,有效保证了全局优化性能;最后,针对一些不同规模的仿真数据,将所提出方法与一些代表性的算法进行详尽的数值对比,计算结果表明所提出方法在所获解的质量和收敛性上均有改善.     

A hybrid genetic algorithm for no-wait job shop scheduling problems

A no-wait job shop (NWJS) describes a situation where every job has its own processing 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 asymmetric 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 experimental results show that this hybrid genetic algorithm can accelerate the convergence and improve solution quality as well.