混合遗传算法求解含机器可利用约束的HFSP

针对含机器阻塞和可利用约束的混合流水车间调度优化问题,考虑工件运输时间,以最小化总加权完工时间为优化目标,建立混合整数规划模型,提出一种基于启发式规则的自适应混合遗传算法求解该模型.在传统遗传算法的基础结构上,引入五种启发式规则生成部分初始种群,从而改善部分初始解的质量;设计分段自适应交叉概率和变异概率计算公式,以加快算法收敛;利用局域搜索对得到的调度解进行再次优化,进一步提高算法搜索能力.对不同规模问题进行仿真实验,结果验证了该算法的可行性和有效性.     

一类具有特殊阻塞约束的两阶段流水车间成组调度模型与算法

从钢铁企业的管加工生产中抽象出一类具有特殊阻塞约束的两阶段流水车间成组调度问题.与传统阻塞约束不同,工件是否发生阻塞并非取决于缓冲区容量,而是取决于工件自身的规格、尺寸等属性.针对此调度问题,以最小化最大完工时间(makespan)为目标建立混合整数线性规划模型,并通过三划分问题的多项式归结证明问题的强NP难特性,进而将问题划分为工件组排序和工件组内工件排序两个子问题,提出一种基于协同进化的分布估计算法.算法针对两个子问题各自特点进行独立编码,分别设计启发式规则构造初始种群,并提出带有工件区块结构特征的概率模型来指导种群进化.基于实际生产数据设计多种问题规模的实验,从而表明所提出模型和算法的有效性.     

考虑模糊作业时间的再制造加工车间多目标调度方法

针对再制造加工过程中作业时间的不确定性以及现行车间调度问题中多目标并行的特点,以三角模糊数描述再制造加工车间作业时间的不确定性,建立以完工时间、加工成本、设备负载平衡和加工能耗为目标的再制造加工车间调度模型,并提出一种基于多种群协同进化的混合人工鱼群算法对模型进行求解.该算法采用多种群协同进化的思想提高单种群混合人工鱼群算法的搜索能力,并考虑对多目标再制造加工车间调度问题的适用性,最后以个体分散程度为指标更新Pareto解集中的最优解.仿真实验验证了所提出方法的可行性.     

动态零等待流水线调度问题的滚动策略及优化算法

针对工件动态到达的零等待流水线调度问题,提出一种基于工件的滚动策略.证明了在该策略下全局调度性能随着局部调度的逐步滚动可得到不断改善.将该策略与基于差分进化的混合算法有机结合,能有效处理动态零等待流水线调度问题.最后通过实验验证了所提出策略和算法的有效性.     

带运输时间混合流水车间成组调度的协同进化文化基因算法EI北大核心CSCD

研究了一类带有序列相关准备时间和阶段间运输时间的混合流水车间成组调度问题,以最小化最大完工时间为目标建立混合整数线性规划模型,结合问题特征提出一种协同进化文化基因算法.算法采用置换序列的方式对工件组间调度、各工件组内工件间调度以及各工件组在各阶段上并行机的指派3个子问题进行统一编码,基于负载均衡思想和改进的先到先得策略将染色体解码为问题的可行解;进化过程中采用多种遗传算子执行全域搜索,并设计了一种基于破坏和重新构造的协同进化局部搜索策略.通过不同问题规模的数据实验和与对比算法的比较分析,验证了所提模型和算法的有效性.     

基于广义粗糙集的流程工业车间调度研究

针对流程工业某电化厂聚氯乙烯车间的生产过程,基于统一时间离散化方法,用广义粗糙集理论对投入产出比、设备转化率等不确定参数进行描述,建立了基于广义粗糙集有限中间存储的流程车间调度问题模型.该模型以最大化产值及精度要求为目标,考虑了设备容量约束、设备生产能力、物料平衡等多种约束,并应用基于改进进化策略的差分进化算法进行求解.仿真结果表明了该算法和模型的可行性及有效性.     

基于位置与连接概率的EDA算法求解PFSP问题

针对置换流水车间调度问题(PFSP)的特点,提出一种基于工件加工位置与连接概率相结合的混合分布估计算法。该算法将工件位置概率与工件的连接概率结合在一起,作为新一代种群的生成概率从而使得进化趋势更加合理化。为了提高局部搜索能力,算法引入了启发式方法提高初始解质量,优化了邻域搜索策略。同时引入了变异操作及限定操作来提高算法全局搜索能力。实验结果表明,该算法在求解置换流水车间调度问题时具有良好的性能。     

混合文化优化算法及在车间调度中的应用

针对文化算法收敛速度慢、易陷入局部最优解以及种群多样性少的问题, 本文对文化算法进行优化设计, 提出一种将带有精英保留策略的遗传算法(GA)和模拟退火算法(SA)纳入文化算法(CA)框架的混合优化算法. 此算法基于协同进化的思想, 算法分为下层种群空间和上层信念空间, 两个空间采用了相同的进化机制, 但使用不同的参数. 在文化算法的基础上加入带有精英保留策略的遗传算法, 使种群中的优秀个体直接进入下一代, 以此提高收敛速度; 加入模拟退火算法, 利用其具有突变的特点, 概率性的跳出局部最优并接受劣质解, 以此增加种群多样性. 函数优化结果证明了算法的有效性, 将此算法用于求解最小化最大完工时间的流水车间调度问题, 仿真结果显示, 此算法在收敛速度和精度方面都优于其他几个具有代表性的算法.     

一种多尺度协同变异的萤火虫粒子群混合算法及在车间调度中的应用

车间调度对于制造企业提高生产效率、降低生产成本具有重要的作用,针对单一优化算法在解决调度优化问题时存在的不足,探索求解速度和求解质量的均衡,提出了一种多尺度协同变异的萤火虫粒子群混合算法;引入动态自适应策略把种群分为两组,对两组族群平行进化,在保持种群多样性的同时提高求解速度;引入多尺度协同变异算子,利用不同大小方差的自适应高斯变异机制使种群以尽量分散的变异尺度来搜索解空间,通过混沌初始化种群进一步提高算法的局部检索能力;将提出的算法应用于函数优化和流水车间调度问题求解,实验结果显示,算法在求解效率、精度方面优于对比算法,具有较好的性能和应用价值。     

区间数可重入混合流水车间调度与预维护协同优化

针对加工时间不确定的可重入混合流水车间调度与预维护协同优化问题,构建以区间最大完工时间、区间总碳排放和区间总预维护费用为优化目标的集成调度模型.针对问题特性,通过设计改进的可能度计算方法,定义区间意义下解的Pareto占优关系.提出一种改进的离散鲸鱼群算法,通过同步调度与维护策略,实现制造与维护的联合优化;设计个体间距离计算策略,寻找“最近较优个体”;设计个体位置移动策略以及多邻域搜索策略,有效地平衡全局搜索和局部搜索,提高收敛精度.通过大量的仿真实验和结果对比分析,表明了所提出的算法对于求解区间数可重入混合流水车间调度和预维护协同优化问题的有效性和可行性.     

A hybrid local search algorithm for scheduling real-world job shops with batch-wise pending due dates

This paper aims at solving a real-world job shop scheduling problem with two characteristics, i.e., the existence of pending due dates and job batches. Due date quotation is an important decision process for contemporary companies that adopt the MTO (make to order) strategy. Although the assignment of due dates is usually performed separately with production scheduling, there exist strong interactions between the two tasks. Therefore, we integrate these two decisions into one optimization model. Meanwhile, each order placed by the customer defines a batch of jobs, for which the same due date should be set. Thus, the completion times of these jobs should be close to one another in order to reduce waiting time and cost. For this purpose, we propose a dispatching rule to synchronize their manufacturing progresses. A two-stage local search algorithm based on the PMBGA (probabilistic model-building genetic algorithm) and parameter perturbation is proposed to solve the integrated scheduling problem and its superiority is revealed by the applications to a real-world mechanical factory.     

On the equivalence of single machine earliness/tardiness problems with job rejection

We present optimal algorithms for single-machine scheduling problems with earliness criteria and job rejection and compare them with the algorithms for the corresponding problems with tardiness objectives. We present an optimal O(n log n) algorithm for minimizing the maximum earliness on a single machine with job rejection. Our algorithm also solves the bi-criteria scheduling problem is which the objective is to simultaneously minimize the maximum earliness of the scheduled jobs and the total rejection cost of the rejected jobs. We also show that the optimal pseudo-polynomial time algorithm for the total tardiness problem with job rejection can be used to solve the corresponding total earliness problem with job rejection.     

An autonomous decentralized scheduling algorithm for a scheduling problem in a metal mould assembly process

This paper deals with a scheduling problem in a metal mould assembly process. The process is of job shop type with several additional constraints. One constraint is that precedence relations exist not only among operations but also among jobs. The other constraint is that the system has two types of machines in parallel. The single-function machine executes a specific operation of each job and the multi-function machine can execute several operations. Therefore selection of the machine is necessary for executing each operation. In addition the problem has two objective functions. One is to minimize the sum of the tardiness of each job, and the other is to maximize the working time of the multi-function machine because of reducing the operating cost of machines. An autonomous decentralized scheduling algorithm is proposed to obatin a compromise solution of the multi-objective problem. In this algorithm, a number of decision makers are called subsystems, which co-operate with one another in order to attain the goal of the overall system. In our algorithm, all jobs and the set of multi-function machine are defined as the subsystem because their objective functions are competitive. They determine the scheduling plan on the basis of their co-operation and the satisfaction of their own objective function levels. The effectiveness of the algorithm is investigated by examining numerical results.     

Hadoop下资源匹配最大集作业调度算法

针对目前层级队列作业调度算法中资源占比高的作业执行效率低的问题,提出一种资源匹配最大集算法。该算法分析作业特征,引入完成度、等待时间、优先级、重调度次数为紧迫值因子,优先考虑资源占比高或等待时间长的作业,以改善作业公平性;采用双队列结构在可用资源总量内优先选择高紧迫值作业,在不同资源占比作业集比较中选择作业数最大集,以实现调度平衡。在与最大最小公平(Max-min fairness)算法的实例对比中发现,该算法可降低作业集平均等待时间、提高资源利用率。实验对比结果表明,该算法可将不同资源占比的单一类型作业集执行时间缩短18.73%,其中资源占比高的作业执行时间缩短27.26%;在混合型作业集中对应的执行时间可分别缩短22.36%与30.28%。所提算法能有效减少资源占比高作业的等待,提高作业整体执行效率。     

考虑工件移动时间的柔性作业车间调度问题研究

分析生产车间的实际生产状况,建立了考虑工件移动时间的柔性作业车间调度问题模型,该模型考虑了以往柔性作业车间调度问题模型所没有考虑的工件在加工机器间的移动时间,使柔性作业车间调度问题更贴近实际生产,让调度理论更具现实性。通过对已有的改进遗传算法的遗传操作进行重构,设计出有效求解考虑工件移动时间的柔性作业车间调度问题的改进遗传算法。最后对实际案例进行求解,得到调度甘特图和析取图,通过对甘特图和析取图的分析验证了所建考虑工件移动时间的柔性作业车间调度问题模型的可行性和有效性。     

Efficient meta-heuristics based on various dominance criteria for a single-machine bi-criteria scheduling problem with rejection

In this paper, we try to fill in the gap between theory and practice in production scheduling by defining a new term as “rejection” and treating the corresponding scheduling problem with multi-objective optimization approach. We study a bi-objective single machine scheduling problem with rejection. At the beginning of scheduling time horizon, scheduler needs to decide which job shall be rejected due to the resource constraints regarding two objective functions: minimization of total weighted completion time of accepted jobs and total rejection penalty of rejected jobs. We develop different algorithms to find the best estimation of Pareto-optimal front for this problem. In order to improve the quality of the solutions, on the one hand, and facilitate the process of selecting best solution for the final decision maker, on the other hand, we integrate various dominance criteria into our proposed algorithms. Finally we compare the performance of those methods by testing on a large set of instances and highlight the advantages and weak points of each one.     

Single-machine scheduling with learning effect and resource-dependent processing times

We consider resource allocation scheduling with learning effect in which the processing time of a job is a function of its position in a sequence and its resource allocation. The objective is to find the optimal sequence of jobs and the optimal resource allocation separately. We concentrate on two goals separately, namely, minimizing a cost function containing makespan, total completion time, total absolute differences in completion times and total resource cost; minimizing a cost function containing makespan, total waiting time, total absolute differences in waiting times and total resource cost. We analyse the problem with two different processing time functions. For each combination of these, we provide a polynomial time algorithm to find the optimal job sequence and resource allocation.     

Preemptive scheduling on uniformly related machines: minimizing the sum of the largest pair of job completion times

We revisit the classic problem of preemptive scheduling on m uniformly related machines. In this problem, jobs can be arbitrarily split into parts, under the constraint that every job is processed completely, and that the parts of a job are not assigned to run in parallel on different machines. We study a new objective which is motivated by fairness, where the goal is to minimize the sum of the two maximal job completion times. We design a polynomial time algorithm for computing an optimal solution. The algorithm can act on any set of machine speeds and any set of input jobs. The algorithm has several cases, many of which are very different from algorithms for makespan minimization (algorithms that minimize the maximum completion time of any job), and from algorithms that minimize the total completion time of all jobs.     

带尺寸约束的二机流水车间生产运输协调博弈调度问题

研究二机流水车间生产运输协调调度问题,当工件在第1台机器加工完成后,由1台带有容量限制的运输车分批次运输到第2台机器加工,运输过程考虑工件尺寸约束,目标函数为最小化最大完工时间.考虑到源于不同客户的工件对机器及运输设备的竞争,以工件为博弈方,工件在生产运输过程中等待时间为策略,各工件完工时间为收益,建立非合作博弈模型.通过将问题转化为马尔可夫决策过程,设计线性逼近值函数的Q-learning算法求解纳什均衡调度.实验结果表明Q-learning算法求得的纳什均衡调度具有较好的全局最优性,从而能够在满足客户的利益下,提高企业的生产效率,实现客户与企业的双赢.