A two-stage flexible flow-shop scheduling problem with m identical parallel machines on one stage and a batch processor on the other stage

This paper considers a hybrid two-stage flow-shop scheduling problem with m identical parallel machines on one stage and a batch processor on the other stage. The processing time of job Jj on any of m identical parallel machines is aj≡a （j∈N）, and the processing time of job Jj is bj（j∈N） on a batch processorM. We take makespan （Cmax） as our minimization objective. In this paper, for the problem of FSMP-BI （m identical parallel machines on the first stage and a batch processor on the second stage）, based on the algorithm given by Sung and Choung for the problem of 1 ｜ri, BI｜Cmax under the constraint of the given processing sequence, we develop an optimal dynamic programming Algorithm H1 for it in max {O（nlogn）, O（nB）} time. A max {O（nlogn） , O（nB）}time symmetric Algorithm H2 is given then for the problem of BI-FSMP （a batch processor on the first stage and m identical parallel machines on the second stage）.

Self-adaptive large neighborhood search algorithm for parallel machine scheduling problems

A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.

MODIFIED BOTTLENECK-BASED PROCEDURE FOR LARGE-SCALE FLOW-SHOP SCHEDULING PROBLEMS WITH BOTTLENECK

A new bottleneck-based heuristic for large-scale flow-shop scheduling problems with a bottleneck is proposed, which is simpler but more tailored than the shifting bottleneck （SB） procedure. In this algorithm, a schedule for the bottleneck machine is first constructed optimally and then the non-bottleneck machines are scheduled around the bottleneck schedule by some effective dispatching rules. Computational results show that the modified bottleneck-based procedure can achieve a tradeoff between solution quality and computational time comparing with SB procedure for medium-size problems. Furthermore it can obtain a good solution in quite short time for large-scale scheduling problems.

Improved Estimation of Distribution Algorithm for Solving Unrelated Parallel Machine Scheduling Problem

Scheduling problem is a well-known combinatorial optimization problem.An effective improved estimation of distribution algorithm(IEDA) was proposed for minimizing the makespan of the unrelated parallel machine scheduling problem(UPMSP).Mathematical description was given for the UPMSP.The IEDA which was combined with variable neighborhood search(IEDA_VNS) was proposed to solve the UPMSP in order to improve local search ability.A new encoding method was designed for representing the feasible solutions of the UPMSP.More knowledge of the UPMSP were taken consideration in IEDA_ VNS for probability matrix which was based the processing time matrix.The simulation results show that the proposed IEDA_VNS can solve the problem effectively.

Approximation algorithm for multiprocessor parallel job scheduling

P k |fix| C max problem is a new scheduling problem based on the multiprocessor parallel job, and it is proved to be NP hard problem when k ≥3. This paper focuses on the case of k =3. Some new observations and new techniques for P 3 |fix| C max problem are offered. The concept of semi normal schedulings is introduced, and a very simple linear time algorithm Semi normal Algorithm for constructing semi normal schedulings is developed. With the method of the classical Graham List Scheduling, a thorough analysis of the optimal scheduling on a special instance is provided, which shows that the algorithm is an approximation algorithm of ratio of 9/8 for any instance of P 3|fix| C max problem, and improves the previous best ratio of 7/6 by M.X.Goemans.

Generalized multiple time windows model based parallel machine scheduling for TDRSS

The scheduling efficiency of the tracking and data relay satellite system（TDRSS）is strictly limited by the scheduling degrees of freedom（DoF）,including time DoF defined by jobs＇ flexible time windows and spatial DoF brought by multiple servable tracking and data relay satellites（TDRSs）.In this paper,ageneralized multiple time windows（GMTW）model is proposed to fully exploit the time and spatial DoF.Then,the improvements of service capability and job-completion probability based on the GMTW are theoretically proved.Further,an asymmetric path-relinking（APR）based heuristic job scheduling framework is presented to maximize the usage of DoF provided by the GMTW.Simulation results show that by using our proposal 11%improvement of average jobcompletion probability can be obtained.Meanwhile,the computing time of the time-to-target can be shorten to 1/9 of the GRASP.

Adaptive subsequence adjustment with evolutionary asymmetric path-relinking for TDRSS scheduling

Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduling model. Therefore, the improvement of scheduling efficiency in the TDRSS can not only help to increase the resource utilities, but also to reduce the scheduling failure ratio. A model of nonhomogeneous parallel machines scheduling problems with time window （NPM-TW） is firstly built up for the TDRSS, considering the distinct features of the variable preparation time and the nonhomogeneous transmission rates for different types of antennas on each tracking and data relay satellite （TDRS）. Then, an adaptive subsequence adjustment （ASA） framework with evolutionary asymmetric path-relinking （EvAPR） is proposed to solve this problem, in which an asymmetric progressive crossover operation is involved to overcome the local optima by the conventional job inserting methods. The numerical results show that, compared with the classical greedy randomized adaptive search procedure （GRASP） algorithm, the scheduling failure ratio of jobs can be reduced over 11% on average by the proposed ASA with EvAPR.

一类加工需要额外资源的平行机调度问题的算法设计

给出了一类加工需要额外资源的平行机调度问题的精确算法。针对在平行机上加工的工件,除需要机器资源外,还需要一个单位额外资源的问题,考虑额外资源的种类和数量有限,以给出问题的最优调度使工件的完工时间最小为目标。该问题源于地球观测卫星的数据下载,在智能制造和信息处理等领域亦有广泛应用。给出了该问题的整数规划模型、最优解下界和分支定界算法;给出了一种有效的分支策略以避免重复分支,设计了相应的定界方法以提高算法的收敛速度。通过小规模实例和大量的数值仿真实验,验证了算法的正确性和在不同参数配置下的有效性。

Multidirection Update-Based Multiobjective Particle Swarm Optimization for Mixed No-Idle Flow-Shop Scheduling Problem

The Mixed No-Idle Flow-shop Scheduling Problem(MNIFSP)is an extension of flow-shop scheduling,which has practical significance and application prospects in production scheduling.To improve the efficacy of solving the complicated multiobjective MNIFSP,a MultiDirection Update(MDU)based Multiobjective Particle Swarm Optimization(MDU-MoPSO)is proposed in this study.For the biobjective optimization problem of the MNIFSP with minimization of makespan and total processing time,the MDU strategy divides particles into three subgroups according to a hybrid selection mechanism.Each subgroup prefers one convergence direction.Two subgroups are individually close to the two edge areas of the Pareto Front(PF)and serve two objectives,whereas the other one approaches the central area of the PF,preferring the two objectives at the same time.The MDU-MoPSO adopts a job sequence representation method and an exchange sequence-based particle update operation,which can better reflect the characteristics of sequence differences among particles.The MDU-MoPSO updates the particle in multiple directions and interacts in each direction,which speeds up the convergence while maintaining a good distribution performance.The experimental results and comparison of six classical evolutionary algorithms for various benchmark problems demonstrate the effectiveness of the proposed algorithm.

基于改进多种群候鸟迁徙算法的混合流水车间调度

针对带顺序依赖准备时间的混合流水车间调度(HFS-SDST)问题,以最小化总最大作业完成时间为调度目标,提出一种改进多种群候鸟迁徙优化(IMMBO)算法.算法中个体基于工件加工顺序进行编码,用改进的NEH(MNEH)算法产生初始种群,并按照适应度值分配到各子种群.子种群中领飞鸟和跟飞鸟分别利用串行和并行邻域策略产生邻域个体,如果跟飞鸟优于领飞鸟,二者互换,完成种群内部个体的信息交互;在IMMBO算法中嵌入离散鲸鱼优化策略对各子种群的领飞鸟进行优化,实现子种群之间信息交互;为提高算法的局部搜索(LS)能力,对种群中最优个体执行LS,同时,为了避免算法早熟收敛,针对每个种群的领飞鸟设计了种群多样化控制策略.最后,在实验法调整算法参数的基础上,对IMMBO的4个变体进行了仿真实验,通过测试Ta自适应算例验证IMMBO算法各部分的作用;将IMMBO算法与现有3个算法测试Ta自适应算例,进行实验结果比较,证明了IMMBO算法求解混合车间调度问题的有效性.

基于并行协同的多车间协同调度问题研究

传统企业在实际生产中,其多个关联车间之间的生产计划与调度存在难以协作的问题。为此,针对多车间协同调度问题建立了调度模型,提出了一种多车间协同调度的并行协同进化遗传算法(PCE-GA),并且采用该算法对上述模型进行了求解。首先,以最小化订单完工时间为目标,建立了单目标调度模型;然后,采用了并行协同进化遗传算法,对上述单目标调度模型进行了求解,基于工件、机器、装配关系的三层整数编码的染色体编码方案,提出了一种协同适应度值计算的方法;最后,以某液压缸生产企业为例,针对单目标调度问题,采用该算法与单车间遗传算法(JSP-GA)、并行协同模拟退火算法(PCE-SA)分别进行了求解,并对其结果进行了比较,以验证PCE-GA算法的优越性。研究结果表明:采用PCE-GA算法得到的优化率为13.3%,比单车间作业调度遗传算法求解的数据优化11.5%,该结果证明了PCE-GA算法在解决多车间协同优化问题时的优越性。

采用改进两点交叉算子的改进自适应遗传算法求解不相关并行机混合流水车间调度问题

针对不相关并行机的混合流水车间调度问题,提出了改进两点交叉算子(ITPX)的自适应遗传算法。首先,利用精确取点方式提高两点交叉算子的求解性能;其次,论证了基于激素调节的自适应选择概率引导种群的收敛趋势;然后,建立优质染色体池和记忆因子来记录种群迭代的优质解,并实现两种不同区域的交叉。实验结果表明,ITPX可节省优化时间,提高求解性能;自适应概率可增强收敛性;改进两点交叉算子的改进自适应遗传算法(ITPX-IAGA)可缩短40%以上的求解时间,并提高求解性能。

混合并行机调度问题的多目标优化模型及算法

针对生产工序的合并造成一种串并联共存的生产布局,研究了一种特殊的混合并行机调度问题,并考虑以最小化总流水时间和最小化总延迟工件数量为目标的多目标调度问题,建立了混合整数规划模型.针对模型特点,设计了一种改进的非支配排序遗传算法进行求解,采用基于启发式方法的初始种群生成方式以提高种群的质量和多样性,并引入一种局域搜索策略以改善求解算法所获得的非支配解的质量及分布性.通过对大量数值算例进行仿真实验,并与典型的多目标优化算法进行比较,结果表明所提出的模型和算法在收敛性、分布性及极端点质量方面均具有优势,能够较好的解决多目标混合并行机调度问题.

并行加工设备组生产调度的一般模型及算法

给出了一个描述并行加工设备组生产调度问题的一般模型及两个启发式算法（极大消去法和ＥＬＰＴ方法），对ＥＬＰＴ方法，另提供了一个误差分析结果，对极大消去法给出了一个数值计算实例。

基于两阶段蚁群算法的带非等效并行机的作业车间调度

针对带非等效并行机的作业车间生产调度问题,以制造系统的生产成本、准时交货率等为目标,构建生产调度多目标模型。利用蚁群算法在求解复杂优化问题方面的优越性,建立调度问题与蚁群并行搜索的映射关系,将调度过程分成任务分派和任务排序两个阶段,每个阶段分别设计蚁群优化算法,并将两阶段寻优蚂蚁有机结合,构建一种具有继承关系的两阶段蚁群并行搜索算法,可以大大提高获得较优解的概率,并且压缩求解空间,快速获得较优解。通过均匀试验和统计分析确定算法的关键参数组合,将两阶段蚁群算法应用不同规模的8组算例。结果表明,无论是优化结果还是计算效率,两阶蚁群算法均优于改进的遗传算法。将所提出两阶段蚁群算法应用于实际车间的生产调度,减少了生产过程中工序间等待时间和缩短了产品交付周期。

基于蚁群系统的工件排序问题的一种新算法

工件排序问题中如何使加工效率最高 ,一直是一个非常重要而且又非常困难的问题 .特别是问题的规模很大时 ,目前各种算法计算就非常困难 ,有的甚至无法得到合理的方案 .蚁群系统是近年来发展起来的解决组合优化问题的一种有效方法 .根据工件排序问题的特点 ,建立了在不同种类的并行机上加工一批不同种类工件的优化数学模型 .在蚁群算法的基础上对其进行了改进 ,成功地把改进的蚁群算法用于工件排序问题的优化中 .通过与其他算法的仿真比较 ,表明基于蚁群系统的算法是有效的 。

混合流水车间调度问题的果蝇优化算法求解

针对不相关并行机混合流水车间调度问题,根据果蝇优化算法种群更新方式的特点,采用基于权重的编码方式进行编码操作,通过增加权重系数来提高算法的随机搜索能力。对算法参数的设置进行了分析,得到了最优参数组合。采用标杆实例进行仿真验证并与经典算法进行对比,验证了果蝇优化算法的有效性。

解并行多机提前/拖后调度问题的并行遗传算法

为有效地解决带有公共交货期的非等同并行多机提前/拖后调度问题,设计了一种分段扩展排列编码的混合遗传算法,使遗传编码能同时反映调度方案和公共交货期,并对其初始种群产生、交叉和变异方法也进行了研究。同时为了更好地适应调度实时性和解大规模此类问题的需要,基于遗传算法自然并行性特点的基础上,实现了主从式控制网络模式下并行混合遗传算法。计算结果表明,此算法是有效的,优于启发式算法和遗传算法,有着较高的并行性,并能适用于大规模非等同并行多机提前/拖后调度问题。

并行多机调度问题的一种基于组合规则的遗传算法

本文对最小化完工时间并行多机调度问题提出了一种基于组合规则的遗传算法 .用遗传算法来优化调度策略 ,使得在不同的调度阶段 ,可采用不同的调度规则以提高算法性能 ,并用计算实例表明了该遗传算法优于基于机器编码的模拟退火算法和遗传算法 ,并能适用于大规模并行多机调度问题 ,算法计算量小 ,鲁棒性强 .

染整车间染缸优化调度算法研究

根据制衣企业染整车间实际需求,建立了染缸加工的调度模型。该模型与传统调度模型不同,它既考虑了小作业的合并,也考虑了大作业的拆分,并在最小化总拖期的同时提高了染缸利用率。改进了已有的启发式算法,使之能够求解本问题,同时提出了一种新的启发式算法。利用某制衣企业染整车间实际生产数据,测试和分析了所提算法的可行性,结果表明新启发式算法既能减小作业总拖期,还可有效提高染缸利用率。