就绪时间受限的负荷单机环境下返工工件重调度方法

研究了返工工件的单机重调度问题.在初始调度中初始工件带有不同的就绪时间,优化目标为最小化初始工件等待时间和;重调度时在满足每个初始工件最大等待时间约束情况下安排返工工件的生产,优化目标为最小化所有工件等待时间和.文中首先建立了RRSM （Rescheduling for reworks on single machine）问题模型,并证明其为NP难问题.然后,提出并证明了三个RRSM问题性质,进而根据诸性质设计了求解RRSM问题的动态插入启发式（Dynamic insert heuristic,DIH）算法.证明了应用DIH算法能在多项式时间内求得两种特殊RRSM问题的最优解. 最后,分析了DIH算法解的特点,给出了最优解的判定方法,并通过算例说明了DIH算法的有效性.     

基于改进的RA算法的混合Flowshop调度问题的求解

针对混合Flowshop系统的最小化Makespan调度问题,提出基于改进的RA斜度指标的启发式算法来对工件进行排序,采用FAM算法来分配设备并给出其最优值的下界检验该算法。仿真结果表明该方法优于目前最好的启发式算法能较好地解决混合Flowshop的调度问题。     

基于改进的RA逄法的混合Flowshop调度问题的求解

针对混合Flowshop系统的最小化akespan调度问题，提出基于改进的RA斜度指标的启发式算法来对工件进行排序，采用FAM算法来分配设备，并给出最优值的下界检验该算法。仿真结果表明，该方法优于目前最好的启发式算法，能较好地解决混合Flowshop的调度问题。     

集装箱码头岸桥最优调度理论研究和高效算法

岸桥调度问题是集装箱码头中最核心的调度问题之一.现有研究成果无法在可行时间内计算出对较大规模业务的最优调度,因此现有岸桥调度算法普遍采用启发式策略,以保障在可行时间内计算出一种调度.首先从理论角度证明了完工时间下界的正确性,设计了一种最优调度构造方法,完备了岸桥调度问题的理论体系;其次,在此理论工作基础上,设计了线性时间复杂度的算法求出最优调度;最后,用实验验证了所提方法在解的质量和效率上显著优于现有方法.     

考虑恶化效应的MapReduce模型下的同类机调度

本文研究了MapReduce模型中考虑恶化效应的同类机调度问题. 在MapReduce模型中每个工件加工必须经 过两道工序. 其中在第1道工序中每个工件加工任务可分割成若干个子任务且能并行加工, 当某个工件中的所有子 任务全部完成后, 才允许启动第2道工序, 且第2道工序只能在一台机器上连续加工. 本文考虑了工件实际加工时间 与其开工前的等待时间呈线性函数关系的恶化效应, 构建了以最小化所有工件的逗留时间和为目标函数的混合整 数规划模型, 同时给出了问题的一个下界, 最后设计了采用正余弦差分扰动机制的改进蝙蝠优化算法来求解模型. 通过数值仿真对蝙蝠优化算法、遗传算法、CPLEX结果与下界进行对比, 验证了模型的正确性和改进算法的有效 性.     

基于工件间隙的Blocking流程车间调度启发式算法

考虑Blocking流程车间调度的特殊性质,提出一种基于工件间隙以达到减少机器闲置和工件滞留时间的初始排序规则,结合插入搜索机制,构造解决Blocking流程车间的调度问题的启发式算法.通过大量的计算实验并与有效地解决该调度问题的NEH算法进行比较,结果表明本算法在解的质量上有改进.     

一种基于分解交货期的Job Shop启发式调度算法

针对以拖期加权和为目标的Job shop调度问题,提出一种基于分解交货期的启发式调度方法,首先根据工件的允许流比率确定每道工序的初始交货期,然后在活动调度框架下应用改进的MOD规则确定工件在机器上的加工顺序．在迭代优化过程中不断调整关键工序的交货期以改善调度的质量,并考虑了工件之间的相互影响．算例仿真研究表明,该算法可以在较短计算时间内得到较好解。可以满足实际Jobshop系统对调度质量和计算效率的要求。     

稳态调度下单臂组合设备时间延迟分析与优化

在半导体晶圆制造过程中,驻留时间延迟过长对晶圆质量具有消极影响.本文研究单臂组合设备稳态调度中如何合理地分配机械手等待时间,抵消驻留时间延迟的问题.首先,采用Petri网模型描述晶圆制造过程,分析了单臂组合设备稳态调度的时间特性,获得了稳态下工序驻留时间延迟计算表达式.其次,通过解构机械手等待时间对驻留时间延迟的影响机理,提出了一种机械手等待时间分配优先级规则.进一步,将虚拟瓶颈工序用于辅助分配机械手等待时间,结合优先级规则,提出了一种单臂组合设备稳态调度启发式算法.最后,通过例子验证了算法的可行性与有效性.与传统拉式策略和尽早加工策略对比,该算法能有效地减少单臂组合设备稳态调度下的驻留时间延迟并能满足晶圆制造的严格要求.     

考虑运输能力限制的跨单元调度方法

工件在生产单元之间频繁转移产生了跨单元调度问题.本文结合我国装备制造业的生产实际,提出考虑运输能力的跨单元调度方法,设计了一种基于离散蜂群与决策块结构的超启发式算法.针对传统超启发式算法的局限性提出动态决策块策略, 同时改进传统蜂群算法的侦查蜂策略,使之具有更好的优化性能.实验表明,动态决策块具有比静态决策块更好的性能,算法在优化能力和计算效率的综合性能上优势显著,并且问题的规模越大,优势越明显.     

瓶颈指向的启发式算法求解混合流水车间调度问题

针对以最小化时间表长为目标的复杂混合流水车间调度问题,提出了一种将机器布局和工件加工时间特征紧密结合的启发式算法.首先,充分利用各阶段平均机器负荷一般不相等的特点确定瓶颈阶段,构建初始工件排序.其次,针对在瓶颈阶段前加工时间较短而瓶颈阶段后加工时间相对较长的工件,在第1阶段优先开始加工.同时,在瓶颈阶段前的每一个阶段,每当有工件等待加工或同时完工时,优先选择瓶颈阶段前剩余加工时间最短的工件加工;在瓶颈阶段以及瓶颈阶段之后,则优先选择这台机器后剩余加工时间最长的工件加工.最后,采用工件交换和插入操作改进初始调度.用Carlier和Neron的Benchmark算例测试提出的启发式算法.将计算结果与NEH启发式算法进行了比较,平均偏差降低了0.0555%,表明这个启发式算法是有效的.     

工件可拒绝的有限等待置换流水车间调度算法

有限等待限定了工件在相邻机器间的等待时间上下限,普遍存在于中间产品性质不稳定且存在运输作业的车间环境中.工件可拒绝的有限等待置换流水车间调度是对工件拒绝和工件调度的联合决策,要求确定拒绝工件集合并给出被接受工件的调度方案.针对这一联合决策问题,以最小化总拒绝成本与总拖期成本之和为目标,并为最大完工时间(Makespan)设置上限约束,结合问题特征提出一种协同进化遗传算法.该算法将染色体编码分解为工件拒绝和工件序列两个子集,基于调度规则生成初始种群,引入协同进化策略依次进化子集种群,并提出基于记忆的动态概率参数设计方法以确定遗传算子的执行概率,设计解码规则以保证解的可行性并优化总成本.最后,通过数据实验验证了所提出算法及相关策略的可行性和有效性,并分析了问题参数对算法性能的影响.     

Bi-criteria minimization for the permutation flowshop scheduling problem with machine-based learning effects

In traditional scheduling problems, the processing time for the given job is assumed to be a constant regardless of whether the job is scheduled earlier or later. However, the phenomenon named “learning effect” has extensively been studied recently, in which job processing times decline as workers gain more experience. This paper discusses a bi-criteria scheduling problem in an m-machine permutation flowshop environment with varied learning effects on different machines. The objective of this paper is to minimize the weighted sum of the total completion time and the makespan. A dominance criterion and a lower bound are proposed to accelerate the branch-and-bound algorithm for deriving the optimal solution. In addition, the near-optimal solutions are derived by adapting two well-known heuristic algorithms. The computational experiments reveal that the proposed branch-and-bound algorithm can effectively deal with problems with up to 16 jobs, and the proposed heuristic algorithms can yield accurate near-optimal solutions.     

Minimizing makespan in three-machine flow shops with deteriorating jobs

In this paper, a three-machine permutation flow shop scheduling problem with time-dependent processing times is considered. By the time-dependent processing times we mean that the job's processing time is an increasing function of its starting time. The objective is to find a sequence that minimizes the makespan. This problem is well known to be NP-hard. Several dominance properties and a lower bound are derived to speed up the elimination process of a branch-and-bound algorithm. Moreover, two heuristic algorithms are proposed to overcome the inefficiency of the branch-and-bound algorithm. Computational experiments on randomly generated problems are conducted to evaluate the branch-and-bound algorithm and heuristic algorithm. Computational results show that the proposed heuristic algorithm M-NEH perform effectively and efficiently.     

混合迭代贪婪算法求解准时生产分布式流水线调度问题

针对以最小化总延迟时间为优化目标的分布式置换流水线问题(distributed permutation flowshop scheduling problem,DPFSP),建立问题排序模型,并提出混合迭代贪婪算法(hybrid iterated greedy,HIG)进行求解.基于问题特点提出最小工期差值(smallest due date difference value,SDV)规则及3种工厂分配规则, 同时结合问题性质提出两种工件插入各工厂内部时问题目标值的下界估计方法.首先,通过实验确定使用分配规则1将工件向各工厂进行分配,同时结合下界估计方法的NEH作为改进启发式算法以生成较高质量初始解;其次,为了增加解的多样性,提出一种关键工厂的移除策略和适用于问题的模拟退火机制;然后,设计基于4种有效邻域操作的两阶段变邻域下降搜索策略,用于在HIG每代中对问题解空间的不同区域进行较深入和细致的搜索;最后,通过仿真实验和算法比较验证了采用HIG求解所提出问题的有效性.     

超启发式遗传算法求解模糊柔性作业车间调度

本文提出一种混合超启发式遗传算法(HHGA),用于求解一类采用三角模糊数表示工件加工时间的模糊柔性作业车间调度问题(FFJSP),优化目标为最小化最大模糊完工时间(即makespan).首先,详细分析现有三角模糊数排序准则性质,并充分考虑取大操作的近似误差和模糊度,设计一种更为准确的三角模糊数排序准则,可合理计算FFJSP和其他各类调度问题解的目标函数值.其次,为实现对FFJSP解空间不同区域的有效搜索,HHGA将求解过程分为两层,高层利用带自适应变异算子的遗传算法对6种特定操作(即6种有效邻域操作)的排列进行优化;低层将高层所得的每种排列作为一种启发式算法,用于对低层相应个体进行操作来执行紧凑的变邻域局部搜索并生成新个体,同时加入模拟退火机制来避免搜索陷入局部极小.最后,仿真实验和算法比较验证了所提排序准则和HHGA的有效性.     

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.     

A discrete colonial competitive algorithm for hybrid flowshop scheduling to minimize earliness and quadratic tardiness penalties

Recently introduced colonial competitive algorithm (CCA) has shown its excellent capability on different optimization problems. The aim of this paper is to propose a discrete version of this method to determine a schedule that minimizes sum of the linear earliness and quadratic tardiness in the hybrid flowshops scheduling problem with simultaneously considering effects of sequence-dependent setup times and limited waiting time. In other word we assume that the waiting time for each job between two consecutive stages cannot be greater than a given upper bound. Also for this problem, a mixed integer program is formulated. Computational results are presented to evaluate the performance of the proposed algorithms for problems with different structures.     

Scheduling jobs on a single serial-batching machine with dynamic job arrivals and multiple job types

This paper investigates a scheduling model with certain co-existing features of serial-batching, dynamic job arrival, multi-types of job, and setup time. In this proposed model, the jobs of all types are first partitioned into serial batches, which are then processed on a single serial-batching machine with an independent constant setup time for each new batch. In order to solve this scheduling problem, we divide it into two phases based on job arrival times, and we also derive and prove certain constructive properties for these two phases. Relying on these properties, we develop a two-phase hybrid algorithm (TPHA). In addition, a valid lower bound of the problem is also derived. This is used to validate the quality of the proposed algorithm. Computational experiments, both with small- and large-scale problems, are performed in order to evaluate the performance of TPHA. The computational results indicate that TPHA outperforms seven other heuristic algorithms. For all test problems of different job sizes, the average gap percentage between the makespan, obtained using TPHA, and the lower bound does not exceed 5.41 %.     

Permutation flowshop problems with bi-criterion makespan and total completion time objective and position-weighted learning effects

In this paper, we study the problem of minimizing the weighted sum of makespan and total completion time in a permutation flowshop where the processing times are supposed to vary according to learning effects. The processing time of a job is a function of the sum of the logarithms of the processing times of the jobs already processed and its position in the sequence. We present heuristic algorithms, which are modified from the optimal schedules for the corresponding single machine scheduling problem and analyze their worst-case error bound. We also adopt an existing algorithm as well as a branch-and-bound algorithm for the general m-machine permutation flowshop problem. For evaluation of the performance of the algorithms, computational experiments are performed on randomly generated test problems.     

Makespan minimization on single batch-processing machine via ant colony optimization

This paper investigates the problem of minimizing makespan on a single batch-processing machine, and the machine can process multiple jobs simultaneously. Each job is characterized by release time, processing time, and job size. We established a mixed integer programming model and proposed a valid lower bound for this problem. By introducing a definition of waste and idle space (WIS), this problem is proven to be equivalent to minimizing the WIS for the schedule. Since the problem is NP-hard, we proposed a heuristic and an ant colony optimization (ACO) algorithm based on the theorems presented. A candidate list strategy and a new method to construct heuristic information were introduced for the ACO approach to achieve a satisfactory solution in a reasonable computational time. Through extensive computational experiments, appropriate ACO parameter values were chosen and the effectiveness of the proposed algorithms was evaluated by solution quality and run time. The results showed that the ACO algorithm combined with the candidate list was more robust and consistently outperformed genetic algorithm (GA), CPLEX, and the other two heuristics, especially for large job instances.