基于到达时间两台并行机上在线批调度

考虑两台同构并行机上在线批调度问题．每个批具有不确定的到达时间,一旦机器可以利用,要在当前可以利用的批中选择出合适的批,并将其中的工件调度到机器上,且工件在加工过程中不允许中断．目标函数是使调度的最大完成时间最小．给出了一个批在线调度ＲＢＬＰＴ 算法,即选择当前批中加工时间之和最大的批按ＬＰＴ 规则调度．另外,利用反证法,对算法的最坏情况进行了分析．

     

并行机生产与具有等待时间限制的成批运输协调调度问题

研究了运输阶段具有等待时间限制的成批运输与并行机生产协调调度问题,目标为最小化制造期与运输费用之和．通过复杂性分析,证明其是强NP难问题,提出启发式算法并证明其最坏情况性能比为4—1／m．当一个运输批必须在同一台机器加工时,证明其也是强NP难问题．将加工时间与等待时间限定值进行比较,分别提出两个启发式算法,并证明其最坏...     

求解考虑机器调整时间的并行机分批优化调度问题

基于目前车间调度问题是以单个或整批进行生产加工的并行机调度模型已不再符合实际工况下的车间生产。提出以最小化最大完工时间为优化目标,对遗传差分进化混合算法,灰狼差分进化混合算法进行了比较。为提高加工工件进行分批及分批之后子批的分配与排序效率,该问题是对不同规模的经典并行机调度问题进行求解并展示两种算法的求解,证明了灰狼差分进化混合算法在寻优性能上优于遗传差分进化混合算法,不仅具有更好的解的稳定性,而且具有更高的寻优精度。     

面向流程工业的批在线调度问题

从钢铁生产热轧流程中提炼出了在同构并行机上的批在线调度问题,它是流程工业中MES的重要环节。从理论上给出算法并研究了算法的性能。工件以批的形式到达,目标函数是使工件的最大完成时间最小。当一个批到达时,将这一批中的工件分成若干组,要求在同一组中的工件可以具有不同的开始加工时间但必须具有相同的完成时间。通过将批调度与在线调度的结合,给出了最坏情况比（竞争率）分别为m／（1＋（m-1）ε）,m（1-ε）／（1-ε）,m／（1＋gε）的批在线调度算法。     

解并行机模糊调度问题的自适应遗传算法

针对家纺企业车间调度的实际情况,建立了一种产品优先级约束的模糊车间调度模型。在模型中,完工时间和交货期都是模糊的,交货期平均满意度最大为调度目标。基于此模型,提出了一种自适应的遗传算法,该算法通过比例选择及局部搜索保证种群的优良特性,并通过自动调节变异率和交叉率的方式保证种群的多样性,有效跳出局部收敛。仿真结果表明,自适应遗传算法能有效求解,并优于免疫遗传算法。     

求解异构并行机调度问题的混合烟花算法

以加工时间可控的机器调度为研究对象,考虑一类以优化能耗和延迟成本为目标的异构并行机调度问题。对该调度问题进行描述,并构建混合整数线性规划模型;提出混合烟花求解算法(HFWA),设计特定的编解码方法以表示问题的解,并融入反向学习初始化方法以提升初始解的质量;构建基于变邻域搜索算法的局部优化流程用以强化基本算法的寻优性能。仿真实验验证了该算法的可行性和有效性。     

基于AGA的最小加权完成时间并行机调度

针对最小化加权完工时间的等同和非等同并行多机调度问题,通过一种新的扩展顺序表达方式编码,采用自适应遗传算法来优化.此编码与调度方案一一对应,并且适于用多种交叉算子.计算结果表明,基于该编码方式的自适应遗传算法是有效的,能适用于大规模等同和非等同并行多机调度问题,且算法操作简单,收敛速度快.     

启发式列生成算法求解带恶化效应的同构并行机调度问题

针对实际生产中广泛存在的一类带恶化效应的同构并行机调度问题,以最小化最大完工时间为优化目标,构建该问题的整数规划模型,并提出一种启发式列生成算法(HCGA)进行求解.在HCGA中,首先,利用Dantzig-Wolfe分解方法,将原问题分解为一个主问题(MP)和多个子问题;然后,设计启发式算法获得初始列,其中每列为一台机器上的一个调度方案,基于初始列构建限制主问题(RMP)模型;接着,设计快速有效的动态规划算法求解子问题,以得到需添加至RMP的列集,同时,考虑传统列生成算法收敛速度较慢,设计一系列方法来加速列生成过程;最后,基于所获取的MP线性松弛解,设计深潜启发式算法确定原问题的整数解.HCGA与商用求解器GUROBI的对比实验结果表明,HCGA可在较短时间内获得更优的解.     

基于时间反演的到达时间定位

针对传统算法在室内超宽带(UWB)到达时间(TOA)定位系统里很难准确搜寻出第一条直射路径,从而导致定位精度不高的问题,提出了基于时间反演(TR)的TOA室内UWB定位算法.首先,利用TR处理的空时聚焦特性确定第一条直射路径,从而估计这条路径的TOA;其次,通过加权最小二乘(WLS)定位算法对不同的估计分量赋予相应的权...     

求解并行机拖期与能耗成本优化调度的混合教一学算法

针对加工时间可控的并行机调度,提出了一类考虑拖期与能耗成本优化的调度问题。首先对调度问题进行了问题描述,并建立了整数线性规划模型以便于CPLEX求解。为了快速获得问题的满意解,提出了一种混合教-学算法。结合问题的性质,设计了编码与解码方法以克服标准教-学算法无法直接适用于离散问题的缺点。同时,构建了基于变邻域搜索的局部搜索算子以强化混合算法的搜索性能。最后,对加工时间可控的并行机调度问题进行了仿真实验,测试结果验证了本文构建的整数线性规划模型和混合算法的可行性和有效性。     

Preemptive scheduling on two parallel machines with a single server

This paper addresses a preemptive scheduling problem on two parallel machines with a single server. Each job has to be loaded (setup) by the server before being processed on the machines. The preemption is allowed in this paper. The goal is to minimize the makespan. We first show that it is no of use to preempt the job during its setup time. Namely, every optimal preemptive schedule can be converted to another optimal schedule where all the setup times are non-preemptively performed on one machine. We then present an algorithm with a tight bound of 4/3 for the general case. Furthermore, we show that the algorithm can produce optimal schedules for two special cases: equal processing times and equal setup times, which are NP-hard in the non-preemptive version.     

Size-reduction heuristics for the unrelated parallel machines scheduling problem

In this paper we study the unrelated parallel machines problem where n independent jobs must be assigned to one out of m parallel machines and the processing time of each job differs from machine to machine. We deal with the objective of the minimisation of the maximum completion time of the jobs, usually referred to as makespan or C_max. This is a type of assignment problem that has been frequently studied in the scientific literature due to its many potential applications. We propose a set of metaheuristics based on a size-reduction of the original assignment problem that produce solutions of very good quality in a short amount of time. The underlying idea is to consider only a few of the best possible machine assignments for the jobs and not all of them. The results are simple, yet powerful methods. We test the proposed algorithms with a large benchmark of instances and compare them with current state-of-the-art methods. In most cases, the proposed size-reduction algorithms produce results that are statistically proven to be better by a significant margin.     

Scheduling unrelated parallel batch processing machines with non-identical job sizes

Scheduling unrelated parallel batch processing machines to minimize makespan is studied in this paper. Jobs with non-identical sizes are scheduled on batch processing machines that can process several jobs as a batch as long as the machine capacity is not violated. Several heuristics based on best fit longest processing time (BFLPT) in two groups are proposed to solve the problem. A lower bound is also proved to evaluate the quality of the heuristics. Computational experiments were undertaken. These showed that J_SC-BFLPT, considering both load balance of machines and job processing times, was robust and outperformed other heuristics for most of the problem categories.     

Minimizing resource consumption on uniform parallel machines with a bound on makespan

With the crucial issue of environmental protection, managing natural resources efficiently and/or reducing the amount of carbon emissions have become more important than ever. In this paper, we introduce a uniform parallel machine scheduling problem where the objective is to minimize resource consumption given that the maximum completion time does not exceed a certain level. We show that the problem is strongly NP-hard. A tight lower bound and a particle swarm optimization algorithm are then developed. Finally, some computational results are provided.     

Online batch scheduling on parallel machines with delivery times

We study the online batch scheduling problem on parallel machines with delivery times. Online algorithms are designed on m parallel batch machines to minimize the time by which all jobs have been delivered. When all jobs have identical processing times, we provide the optimal online algorithms for both bounded and unbounded versions of this problem. For the general case of processing time on unbounded batch machines, an online algorithm with a competitive ratio of 2 is given when the number of machines m=2 or m=3, respectively. When m≥4, we present an online algorithm with a competitive ratio of 1.5+o(1).     

含换模时间的平行机调度问题研究

在工厂实际生产中,模具的换模时间在生产调度中不可忽略。为了更合理地研究平行机车间调度问题,本文将存在序依赖的换模时间考虑进调度模型之中,同时以最小完工时间和最小拖期时间为目标,在经典遗传算法的基础上,对算法选择算子以及交叉变异概率进行改进,避免早熟现象的发生。通过计算结果的比较,证明本文中调度模型更符合实际生产情况,改进后的算法能够得出更高质量的解,且求解效率更高。     

Scheduling unrelated parallel batch processing machines with non-identical job sizes and unequal ready times

This research analyzes the problem of scheduling a set of n jobs with arbitrary job sizes and non-zero ready times on a set of m unrelated parallel batch processing machines so as to minimize the makespan. Unrelated parallel machine is a generalization of the identical parallel processing machines and is closer to real-world production systems. Each machine can accommodate and process several jobs simultaneously as a batch as long as the machine capacity is not exceeded. The batch processing time and the batch ready time are respectively equal to the largest processing time and the largest ready time among all the jobs in the batch. Motivated by the computational complexity and the practical relevance of the problem, we present several heuristics based on first-fit and best-fit earliest job ready time rules. We also present a mixed integer programming model for the problem and a lower bound to evaluate the quality of the heuristics. The small computational effort of deterministic heuristics, which is valuable in some practical applications, is also one of the reasons that motivates this study. The results show that the heuristic proposed in this paper has a superior performance compared to the heuristics based on ideas proposed in the literature.