考虑周期预防性维护的两阶段混合流水线调度

针对两阶段且每阶段都存在并行机的混合流水线系统,考虑设备具有时间窗的周期性维护需求,以最小化最大完成时间为调度指标,建立调度与维护的联合优化模型,提出了基于最长加工时间规则和最小化机器松弛时间的构造式算法。对不同规模下的问题进行数值验证,并与基于LPT、Johnson规则的构造式算法进行对比,结果表明所提算法具有更优的性能。利用遗传算法对调度解进行搜索优化,并与传统的生产与维护独立决策的结果相比较,证明了生产调度与维护联合决策模型是有效的,更加符合生产车间的实际调度背景。     

船舶平面分段的非完全混合流水线调度

针对船舶平面分段流水线生产中存在的计划不准确、调度滞后、生产效率低等问题,分析平面分段流水线的特点,提出一类非完全混合流水线调度模型,并对该类调度问题进行优化研究。同时,综合考虑非完全混合流水线调度问题的特殊性及船舶建造的复杂性,以分段最大完工时问最小化为优化目标,建立两阶段的非线性整数规划模型,并利用分支定界法优化求解。结合某船厂实际数据进行实例验证和多次数值实验,并与混合遗传算法进行对比。结果表明,该模型和算法能有效解决平面分段流水线调度中存在的问题。     

基于多目标优化的改进遗传算法求解柔性车间调度问题

文章主要研究多目标的柔性车间调度问题。在实际生产过程中,调度结果受完工时间、机器负荷、成本控制和资源消耗等多方面因素影响,因此提出了一种基于多目标优化的改进遗传算法,针对最小化最大完成时间、最小化机器负荷和最小化资源消耗3个目标函数进行优化,结合改进的Pareto多目标优化方法,以及最短加工时间变异和邻域变异方法,提高了算法的寻优能力。最后通过实验验证了算法适用于求解多目标的柔性车间调度问题。     

带阻塞限制的船舶分段生产流水线调度

针对船舶分段生产流水线中船舶分段所占空间和重量大的特点,结合流水线阻塞限制、准备时间等特点,构建了阻塞流水线调度模型。以最小化分段完工时间为目标,采用基于最小位置值的编码,将连续的布谷鸟搜索算法用于求解离散的调度问题;提出一种基于改进的随机NEH启发式规则的初始化方法改进布谷鸟搜索算法,使初始种群具有多样性,提高搜索的性能。通过算例验证了改进布谷鸟搜索算法解决带阻塞的Flow-Shop问题的有效性。结合某造船企业实际数据进行应用,结果表明该调度模型和方法在企业中能获得比当前生产流水线更好的调度方案。     

基于改进候鸟算法的柔性作业车间分批调度问题

为了将可变批次的调度策略应用于生产,以提高大规模柔性作业车间的生产效率和设备利用率,针对柔性作业车间可变子批问题的特点,建立了以最小化完成时间和最小化批次数目为优化目标的多目标柔性作业车间调度模型和析取图模型,提出一种改进的候鸟算法求解该问题.算法设计了精英分批和可行邻域结构两种策略用于提高算法的搜索效率.通过对比实验验证了可变批次划分策略的优势和所提算法的有效性.     

基于离散Jaya算法的线缆生产调度方法

线缆生产调度优化问题广泛存在于电线电缆行业的生产实际中，本质上属于带安装时间和复杂资源约束的柔性作业车间调度问题。针对线缆生产调度优化问题，建立了以最小化最大完工时间为目标的问题数学模型，提出一种离散Jaya算法解决该问题。在Jaya算法框架下，基于单列编码方式和左移解码策略，融入优先工序交叉算子和反向学习搜索策略，引导算法更有效地搜索问题空间，以提升算法性能，从而实现最大完工时间最小化。基于企业生产实际生成的实例集，通过仿真实验与对比分析，表明了所提算法在求解线缆生产调度问题中具备较高的有效性和稳定性。     

基于目标级联法的动态零件族跨单元生产的单元调度研究

研究单元生产环境中零部件生产工艺不相同情况下动态零件族跨单元生产的单元调度问题。以最小化单元制造系统的总流程时间为目标,对各生产单元的重组零件族进行生产调度,采用分级调度算法对该问题进行研究。算法将调度过程分为三层,即时间决策层、分配决策层和路径决策层,以时间决策层为最终优化目标,通过将时间分解至分配决策层再至路径决策层,下层时间达到最优后反馈至上层,层层优化来实现对单元制造系统的有效管理。最后通过算例验证该算法在单元生产环境下,能够根据加工时间和加工数量动态、合理分配零件到各生产单元,形成动态零件族,并优化工件在各单元的加工路径,具有一定的合理性和有效性。     

自适应混合EDA求解一类三阶段装配流水线调度问题

针对生产过程中广泛存在的一类三阶段装配流水线调度问题,即带序相关设置时间的三阶段装配流水线调度问题,提出一种自适应混合分布估计算法,用于最小化平均完成时间和最大延迟时间的加权和。提出初始种群和初始概率分布模型生成机制,使概率分布模型能适当地积累较多优质解的信息,以提高AHEDA在进化初期的搜索能力。设计了基于信息熵的概率分布模型自适应更新机制和保留优良模式的新种群采样生成方法,增强了算法的全局搜索能力。引入基于Insert的邻域搜索来增强算法的局部搜索能力。最后通过仿真实验和算法比较验证了AHEDA的有效性。     

考虑衰退的流水车间生产与预防性维护集成调度方法

为有效解决流水车间生产与预防性维护的集成调度问题,提出考虑设备衰退的基于改进人工蜂群算法的集成调度方法。对具有设备衰退特征的流水车间集成调度问题域进行了描述,并以最小化完工时间和最小化维护成本为优化目标建立了数学规划模型。针对生产与维护两个决策变量,提出改进双目标人工蜂群算法。该算法融合改进的基于分类排序的Pareto遗传算法的快速排序规则,引入局域禁忌搜索策略和概率接受准则以提高搜索性能。仿真实验表明了该算法的可行性和有效性。     

求解分布式混合流水线调度问题的改进双层嵌套式遗传算法

针对分布式混合流水线生产的生产调度问题,模拟实际排产中的排产到线和排产到时的排产策略,提出了基于改进双层嵌套式遗传算法的两层优化模型。外层依据流水线分配平衡和准时交货等基本原则总体上解决生产订单在流水线之间的分配问题,内层以最小生产时间为主要目的求解流水线的生产订单生产次序问题。考虑到双层嵌套式遗传算法的时间复杂性,基于模糊逻辑理论设计了一种模糊控制器来动态调整遗传算子,并采用主动检测停止方法,提高算法效率。使用某空调工厂的实际生产数据验证了算法的可行性、计算结果的准确性及排产策略的有效性,为高级计划与排程(Advanced Planning and Scheduling,APS)中大规模复杂供应链调度问题提供了可借鉴的方法。     

Solving a multi-objective overlapping flow-shop scheduling

In flow-shop manufacturing scheduling systems, managers attempt to minimize makespan and manufacturing costs. Job overlaps are typically unavoidable in real-life applications as overlapping production shortens operation throughput times and reduces work-in-process inventories. This study presents an ant colony optimization (ACO) heuristic for establishing a simple and effective mechanism to solve the overlap manufacturing scheduling problem with various ready times and a sequentially dependent setup time. In the proposed approach, the scheduling mechanism and ACO heuristics are developed separately, thereby improving the performance of overlapping manufacturing flow by varying parameters or settings within the ACO heuristics and allowing for flexible application of manufacturing by altering scheduling criteria. Finally, the experimental results of the scheduling problem demonstrate that the ACO heuristics have good performance when searching for answers.     

Scheduling stochastic job shop subject to random breakdown to minimize makespan

The problem of scheduling stochastic job shop subject to breakdown is seldom considered. This paper proposes an efficient genetic algorithm (GA) for the problem with exponential processing time and non-resumable jobs. The objective is to minimize the stochastic makespan itself. In the proposed GA, a novel random key representation is suggested to represent the schedule of the problem and a discrete event-driven decoding method is applied to build the schedule and handle breakdown. Probability stochastic order and the addition operation of exponential random variables are also used to calculate the objective value. The proposed GA is applied to some test problems and compared with a simulated annealing and a particle swarm optimization. The computational results show the effectiveness of the GA and its promising advantage on stochastic scheduling.     

多生产线作业协调调度的PSOGA混合优化算法

为了求解多目标多生产线调度问题,采用协同进化思想,提出了多种群PSOGA混合优化算法(MC-HPSOGA)。以最小化最大完工时间、最大化生产线利用率和最大化客户满意度为目标函数,建立了多生产线作业协调调度问题的多目标批量调度数学模型,并且设计最小批量动态分批策略,将MC-HPSOGA算法应用于BSPT公司角磨机装配线的多目标多生产线调度问题实例中,通过与粒子群(PSO)和遗传算法(GA)的比较,验证了MC-HPSOGA算法和模型的有效性。     

一种求解Flow-Shop调度问题的混合量子进化算法

针对Flow—Shop调度问题,在量子进化算法的基础上,提出了一种求解置换流水车间调度问题的混合量子进化算法（HQEA）,融合了量子进化算法和经典遗传算法的优点,并提出了一种新的针对置换流水车间调度问题的解码方法和一种新的量子门更新旋转角策略,最后针对一系列典型置换流水车间调度问题进行了对比仿真。研究结果表明,所提出的混合量子进化算法HQEA具有良好的全局搜索能力和较快的收敛速度。     

Minimizing makespan in reentrant flow-shops using hybrid tabu search

This study considers the problem of reentrant flow-shop (RFS) scheduling, and applies hybrid tabu search (HTS) to minimize the makespan of jobs. The hybridization method is used to enhance the performance of pure tabu search. The HTS is compared to the optimal solutions generated by the integer programming technique, and to the near optimal solutions generated by pure tabu search and the non-delay schedule generation procedure. Computational experiments are performed to illustrate the effectiveness and efficiency of the proposed HTS algorithm.     

A scheduling model for serial jobs on parallel machines with different preventive maintenance (PM)

In factories during production, preventive maintenance (PM) scheduling is an important problem in preventing and predicting the failure of machines, and most other critical tasks. In this paper, we present a new method of PM scheduling in two modes for more precise and better machine maintenance, as pieces must be replaced or be repaired. Because of the importance of this problem, we define multi-objective functions including makespan, PM cost, variance tardiness, and variance cost; we also consider multi-parallel series machines that perform multiple jobs on each machine and an aid, the analytic network process, to weight these objectives and their alternatives. PM scheduling is an NP-hard problem, so we use a dynamic genetic algorithm (GA) (the probability of mutation and crossover is changed through the main GA) to solve our algorithm and present another heuristic model (particle swarm optimization) algorithm against which to compare the GA’s answer. At the end, a numerical example shows that the presented method is very useful in implementing and maintaining machines and devices.     

Robust metaheuristics for group scheduling with sequence-dependent setup times in hybrid flexible flow shops

This paper considers group scheduling problem in hybrid flexible flow shop with sequence-dependent setup times to minimize makespan. Group scheduling problem consists of two levels, namely scheduling of groups and jobs within each group. In order to solve problems with this context, two new metaheuristics based on simulated annealing (SA) and genetic algorithm (GA) are developed. A design procedure is developed to specify and adjust significant parameters for SA- and GA-based metaheuristics. The proposed procedure is based on the response surface methodology and two types of objective function are considered to develop multiple-objective decision making model. For comparing metaheuristics, makespan and elapsed time to obtain it are considered as two response variables representing effectiveness and efficiency of algorithms. Based on obtained results in the aspect of makespan, GA-based metaheuristic is recommended for solving group scheduling problems in hybrid flexible flow shop in all sizes and for elapsed time SA-based metaheuristic has better results.     

一种新调度类型及其在作业车间调度中的应用

研究改进遗传算法解决作业车间调度问题,问题染色体的编码采用基于工序的编码。针对传统的调度类型的局限性,提出全主动调度及其基于工序编码的产生机制。为了克服传统遗传算法求解调度问题易于早熟收敛的缺点,设计基于优先工序交叉(Precedence operation crossover,POX)和改进子代产生模式的遗传算法。用改进的遗传算法求解传统调度问题、交货期调度问题和提前/拖期(Earliness/Tardiness, E/T)调度问题,研究半主动、主动和全主动三种不同的调度解码机制对遗传算法提供解质量的影响。     

基于遗传算法的多资源作业车间智能优化调度

提出一种基于遗传算法的调度算法，用于解决作业车间的加工受到机床、操作工人和机器人等多种生产资源制约条件下的优化调度。以生产周期为目标进行的优化调度，将遗传算法和分派规则相结合，通过交叉、交异等遗传操作，得到目标的最优或次优解。最后对算法进行了仿真研究，并给出了算法运行结果，仿真结果表明该算法是可行的。     

An enhanced estimation of distribution algorithm for solving hybrid flow-shop scheduling problem with identical parallel machines

In this paper, an enhanced estimation of distribution algorithm (EEDA) is proposed to solve the hybrid flow-shop scheduling problem with identical parallel machines to minimize makespan. To evaluate the individuals, some decoding rules including the improved permutation scheduling rule, the improved list scheduling rule and the backward scheduling rule are designed for the permutation-based encoding scheme, and then a hybrid decoding method is proposed. To describe the distribution of the solution space for the EEDA, a probability model is built and used to generate new individuals by sampling. To well trace the region with promising solutions, a mechanism is provided to update the model with the superior sub-population. To enhance the exploitation capability, multiple local search operators are incorporated in the framework of the EEDA. The influence of the parameter setting is investigated based on the Taguchi method of design-of-experiment. Extensive numerical testing results based on sets of the well-known benchmarks and the comparisons with some existing algorithms demonstrate the effectiveness of the proposed algorithm.