单位工件的平行机并行分批在线排序问题的算法

本文研究一类批容量有界的并行分批、平行机在线排序问题。模型中有n个相互独立的工件J={J₁,…,J_n}要在m台批处理机上加工。批处理机每次可同时加工至多B(Bj(1≤j≤n)的到达时间为r_j,加工时间为1,工件是否会到达事先未知,而只有等到工件的到达时间才能获知它的到达。目标为最小化工件的最大完工时间。针对该排序问题,本文设计了两个竞争比均达到最好可能的在线算法。     

最后完工机器至多两个空闲的自由作业稠密时间表(英文)

对于自由作业问题,在安排工件时避免不必要空闲所得的时间表称为稠密时间表.稠密时间表的加工总长不超过最优值的2-1/m倍,是一个在机器数m6时尚未被证明的猜想.本文通过引入工件与机器特征函数及机器关于工件非间断等概念,研究当最后完工机器至多有两个空闲区间时,性能比猜想成立的充分条件.     

两台平行机的实时到达在线排序

本文考虑一的的在线平行机排序模型－－实时到达在线问题,该模型中,工件是陆续到达的,工件的个数及到达时间是事先未知的,而且只有当工件到达,才知其加工时间,所求目标是使所有工件都加工完的时间达到最小,对两台平行机的情形,Ｃｈｅｎ与Ｖｅｓｔｊｅｎｓ给出近似比为３／２的线ＬＰＴ算法,并证明了不存在近似小于（５－√５）／２的算法,我们利用黄金分割数设计了一个 算法,其近似比不超过（１８－√５）／１１。     

带机器准备时间的两台同型机复合半在线排序问题

本文研究了预知两种信息,带机器准备时间的两台同型平行机复合半在线排序问题,即已知所有工件加工时间总和和工件按加工时间非增顺序到达,目标为极小化最大机器完工时间的半在线排序模型.我们分析了它的下界,并给出了竞争比为7/6的最优算法.     

具有学习效应的两台机上最优混合流水作业算法

研究相同工件在两台机器（分别称为机器M₁和M₂）上的混合流水作业问题,每个给定工件有两个任务,分别称之为任务A和任务B,任务B只能在任务A完工后才能开始加工,每个工件有两种加工模式供选择：模式1是将两个任务都安排在机器M₂上加工;模式2是将任务A和B分别安排在机器M₁和M₂上加工.假设在加工工件时,机器具有学习效应,即工件的实际加工时间与工件的加工位置有关.目标函数是最小化最大完工时间.分别讨论了具有无缓冲区与无限缓冲区两种加工环境情况,两种情况下都得到了最优算法.     

Tabu list management methods for a discrete–continuous scheduling problem

A problem of scheduling jobs on parallel, identical machines under an additional continuous resource to minimize the makespan is considered. Jobs are non-preemtable and independent and all are available at the start of the process. The total amount of the continuous resource available at a time is limited and the resource is a renewable one. Each job simultaneously requires for its processing a machine and an amount (unknown in advance) of the continuous resource. Processing rate of a job depends on the amount of the resource allotted to this job at a time. The problem is to find a sequence of jobs on machines and, simultaneously, a continuous resource allocation that minimizes the makespan. The tabu search (TS) metaheuristic is presented to attack the problem. Three different tabu list management methods: the tabu navigation method (TNM), the cancellation sequence method (CSM) and the reverse elimination method (REM) are discussed and examined. A computational experiment is described and the results obtained for the methods tested are compared to optimal solutions. A few conclusions and final remarks are presented.     

Parallel machine scheduling with machine availability and eligibility constraints

In this paper we consider the problem of scheduling n independent jobs on m identical machines incorporating machine availability and eligibility constraints while minimizing the makespan. Each machine is not continuously available at all times and each job can only be processed on specified machines. A network flow approach is used to formulate this scheduling problem into a series of maximum flow problems. We propose a polynomial time binary search algorithm to either verify the infeasibility of the problem or solve it optimally if a feasible schedule exists.     

ON-LINE PROBLEMS OF MINIMIZING MAKESPAN ON A SINGLE BATCH PROCESSING MACHINE WITH NONIDENTICAL JOB SIZES

The on-line problem of scheduling on a batch processing machine with nonidentical job sizes to minimize makespan is considered. The batch processing machine can process a number of jobs simultaneously as long as the total size of these jobs being processed does not exceed the machine capacity. The processing time of a batch is given by the longest processing time of any job in the batch. Each job becomes available at its arrival time, which is unknown in advance, and its processing time becomes known upon its arrival. The paper deals with two variants： the case only with two distinct arrival times and the general case. For the first case, an on-line algorithm with competitive ratio 119/44 is given. For the latter one, a simple algorithm with competitive ratio 3 is given. For both variants the better ratios can be obtained if the problem satisfies proportional assumption.     

关于带运输的单机调度在线问题的研究

本文研究了带运输机的单机在线调度问题。问题假设工件实时在线到达,系统中有一台运输机,该运输机每次最多运输$k$个工件,每个工件需要先在单机上完成加工,然后再被运输机运往目的地,问题的优化目标为最小化完工时间,即所有工件被加工完并且运往目的地的时间最短。针对该问题,作者研究了工件满足一致性条件的模型,并且基于贪心思想给出了竞争比为$\frac{\sqrt{5}+1}{2}$的在线算法,并且证明该算法是最优在线算法。     

关于带运输的单机调度在线问题的研究

本文研究了带运输机的单机在线调度问题。问题假设工件实时在线到达,系统中有一台运输机,该运输机每次最多运输$k$个工件,每个工件需要先在单机上完成加工,然后再被运输机运往目的地,问题的优化目标为最小化完工时间,即所有工件被加工完并且运往目的地的时间最短。针对该问题,作者研究了工件满足一致性条件的模型,并且基于贪心思想给出了竞争比为$\frac{\sqrt{5}+1}{2}$的在线算法,并且证明该算法是最优在线算法。     

On a methodology for discrete–continuous scheduling

Discrete–continuous problems of scheduling nonpreemptable jobs on parallel machines are considered. The problems arise e.g. when jobs are assigned to multiple parallel processors driven by a common electric, hydraulic or pneumatic power source. Existing models have assumed job processing rates as a function of the number of jobs currently being processed, or equivalently the number of machines currently in operation. In this paper a more general model is proposed in which processing rates of a job assigned to a machine depend on the amount of a continuous, i.e. continuously divisible resource (e.g. power) allotted to this job at a time. Thus the problem consists of two interrelated subproblems: (i) to sequence jobs on machines, and (ii) to allocate the continuous resource among jobs already sequenced. We provide a comprehensive analysis of the problem. This includes properties of optimal schedules, efficiently (in particular analytically) solvable cases, formulations of the possibly simplest mathematical programming problems for finding optimal schedules in the general case, heuristics and the worst-case analysis. Although our objective function in this paper is to minimize makespan of a set of independent jobs, the presented methodology can be applied to other criteria, precedence-related jobs, and many resource types (apart from, or instead of machines).     

Scheduling incompatible tasks on two machines

The paper studies the problem of scheduling tasks on two machines to minimize the makespan. The tasks are assigned to the machine in advance. An incompatibility relation is defined over the tasks which forbids any two incompatible tasks to be processed at the same time. The problem can serve as a mathematical model for some batching problems in which the jobs are grouped in pairs on two machines. A linear-time algorithm is presented.     

Optimal on-line flow time with resource augmentation

We study the problem of scheduling n jobs that arrive over time. We consider a non-preemptive setting on a single machine. The goal is to minimize the total flow time. We use extra resource competitive analysis: an optimal off-line algorithm which schedules jobs on a single machine is compared to a more powerful on-line algorithm that has ? machines. We design an algorithm of competitive ratio , where Δ is the maximum ratio between two job sizes, and provide a lower bound which shows that the algorithm is optimal up to a constant factor for any constant ?. The algorithm works for a hard version of the problem where the sizes of the smallest and the largest jobs are not known in advance, only Δ and n are known. This gives a trade-off between the resource augmentation and the competitive ratio.We also consider scheduling on parallel identical machines. In this case the optimal off-line algorithm has m machines and the on-line algorithm has ?m machines. We give a lower bound for this case. Next, we give lower bounds for algorithms using resource augmentation on the speed. Finally, we consider scheduling with hard deadlines, and scheduling so as to minimize the total completion time.     

A Branch-and-Bound Approach for a Two-machine Flowshop Scheduling Problem

In this paper, we present a branch-and-bound approach for solving a two-machine flow shop scheduling problem, in which the objective is to minimize a weighted combination of job flowtime and schedule makespan. Experimental results show that the algorithm works very well for certain special cases and moderately well for others. In fact, it is able to produce optimal schedules for 500-job problems in which the second machine dominates the first machine. It is also shown that the algorithm developed to provide an upper bound for the branch-and-bound is optimal when processing times for jobs are the same on both machines. The primary reason for developing the branch-and-bound approach is that its results can be used to guide other heuristic techniques, such as simulated annealing, tabu search and genetic algorithms, in their search for optimal solutions for larger problems.     

Little-Preemptive Scheduling on Unrelated Processors

We consider the preemptive scheduling of n independent jobs on m unrelated machines to minimize the makespan. Preemptive schedules with at most 2m–3 preemptions are built, which are optimal when the maximal job processing time is no more than the optimal schedule makespan. We further restrict the maximal job processing time and obtain optimal schedules with at most m–1 preemptions. This is better than the earlier known best bound of 4m ²–5m+2 on the total number of preemptions. Without the restriction on the maximal job processing time, our (2m–3)-preemptive schedules have a makespan which is no more than either of the following two magnitudes: (a) the maximum between the longest job processing time and the optimal preemptive makespan, and (b) the optimal nonpreemptive makespan. Our (m–1)-preemptive schedules might be at most twice worse than an optimal one.     

带两个服务等级的三台机最优在线算法

研究了带服务等级约束的三台平行机在线排序问题.每台机器和每个工件的服务等级为1或者2,工件只能在等级不高于它的机器上加工,即等级为1的工件只能在等级为1的机器上加工,等级为2的工件可在所有机器上加工.每个工件的加工时间为一个单位,目标是极小化所有工件的总完工时间.考虑两种情形:当一台机器等级为1,两台机器等级为2时,给出了竞争比为17/14的最优在线算法;当两台机器等级为1,一台机器等级为2时,给出了竞争比为43/36的最优在线算法.     

平行机上带有前瞻区间的不相容工件组在线排序问题

研究当不相容工件组的个数与机器数相等时,具有前瞻区间的单位工件平行机无界平行分批在线排序问题.工件按时在线到达, 目标是最小化 最大完工时间. 具有前瞻区间是指在时刻t, 在线算法能预见到时间区间(t,t+\beta) 内到达的所有工件的信息.不可相容的工件组是指属于不同组的工件不能被安排在同一批中加工. \beta\geq 1 时, 提供了一个最优的在线算法; 当0\leq \beta < 1时, 提供了一个竞争比为1+\alpha 的最好可能的在线算法, 其中\alpha是方程\alpha^{2}+(1+\beta) \alpha+\beta-1=0的一个正根.最后, 给出了当\beta =0 时稠密算法竞争比的下界,并提供了达到该下界的最好可能的稠密算法.     

需要安装时间的平行多功能机排序问题的启发式算法

考虑需要安装时间的平行多功能机排序问题。在该模型中,每个工件对应机器集合的一个子集,其只能在这个子集中的任一台机器上加工,称这个子集为该工件的加工集合;工件分组,同组工件具有相同的加工时间和加工集合,不同组中的工件在同一台机器上连续加工需要安装时间,目标函数为极小化最大完工时间。对该问题NP-难的一般情况设计启发式算法:首先按照特定的规则将所有工件组都整组地安排到各台机器上,然后通过在各机器间转移工件不断改进当前最大完工时间。通过与下界的比较检验算法的性能,大量的计算实验表明,算法是实用而有效的。     

Tabu search for discrete–continuous scheduling problems with heuristic continuous resource allocation

Problems of scheduling non-preemptable, independent jobs on parallel identical machines under an additional continuous renewable resource to minimize the makespan are considered. Each job simultaneously requires for its processing a machine and an amount (unknown in advance) of the continuous resource. The processing rate of a job depends on the amount of the resource allotted to this job at a time. The problem is to find a sequence of jobs on machines and, simultaneously, a continuous resource allocation that minimize the makespan. A heuristic procedure for allocating the continuous resource is used. The tabu search metaheuristic to solve the considered problem is presented. The results produced by tabu search are compared with optimal solutions for small instances, as well as with the results generated by simple search methods – multi-start iterative improvement and random sampling for larger instances.     

On the optimality of static policy in stochastic open shop

Consider n jobs and two machines. Each job has to be processed on both machines. The order in which it is dome is immaterial. However, the decision maker has to decide in advance which jobs will be processes first on machine 1 (2). We assume that processing times on each machine are identically exponentially distributed random variables. We prove that assigning equal number of jobs to be first processed by machine 1 (2) stochastically minimizes the makespan.