Semi-on-line problems on two identical machines with combined partial information

This paper considers the semi-on-line versions of scheduling problem P2||C_max. We study the semi-on-line problems with combination of two types of information. Five basic types of partial information are considered. For two kinds of pairwise combination, we present their respective optimal semi-on-line algorithms which show that combination can admit to construct better algorithms.     

Scheduling open shops with parallel machines

The parallel shop and the open shop are two machine environments that have received much attention in the literature of scheduling theory. A common generalization—the open shop with parallel machines—is considered in this paper. Polynomial-time algorithms are presented for obtaining minimum-length preemptive schedules for three cases. Open shops with single-operation machines of equal speed are scheduled with essentially no more difficulty than an ordinary open shop. Open shops with multiple-operation machines of equal speed are scheduled with the aid of a sequence of network flow computations. The general open shop problem with parallel machines of arbitrary speeds can be solved by linear programming, in much the same way as an optimal preemptive schedule can be found for unrelated parallel machines.     

带有拒绝的单机和同型机排序问题

研究了带有拒绝的单机和同型机排序问题. 对于单机情形, 工件的惩罚费用是对应加工时间的\alpha倍.如果工件有到达时间, 目标为最小化时间表长与惩罚费用之和, 证明了这个问题是可解的.如果所有工件在零时刻到达, 目标为最小化总完工时间与惩罚费用之和, 也证明了该问题是可解的.对于同型机排序问题, 研究了工件分两批在线实时到达的情形, 目标为最小化时间表长与惩罚费用之和.针对机器台数2和m, 分别给出了竞争比为2和4-2/m的在线算法.     

具有时间与位置相关的两类平行机排序问题

研究带有维修时间限制的时间和位置效应平行机排序问题,涉及同型机和非同类机两种机器类型.工件的实际加工时间同时受到位置效应和时间效应影响,且机器具有维修限制.目标函数由机器负载,总完工时间与总等待时间组成.非同类机情形下,通过将排序问题转化为指派问题,给出多项式时间算法,其算法的时间复杂度为O（n^k+2/（k-1）!）.同型机情形下通过转化目标函数,使用匹配算法得出排序问题的多项式时间解,其时间复杂度为O（（2n+m+n log n）n^k-1/（k-1）!）.     

An approximation algorithm for scheduling two parallel machines with capacity constraints

We consider the problem of scheduling n independent jobs on two identical parallel machines, with a limit on the number of jobs that can be assigned to each single machine, so as to minimize the total weighted completion time of the jobs. We study a semidefinite programming-based approximation algorithm for solving this problem and prove that the algorithm has a worst case ratio at most 1.1626.     

Scheduling on a Batch Processing Machine with Split Compatibility Graphs

We consider the problem of minimizing the makespan on a batch processing machine, in which jobs are not all compatible. Only compatible jobs can be included into the same batch. This relation of compatibility is represented by a split graph. All jobs are available at the same date. The capacity of the batch processing machine is finite or infinite. The processing time of a batch is given by the processing time of the longest job in the batch. We establish the NP-hardness of the general problem and present polynomial algorithms for several special cases.     

Dynamic Scheduling on a Single Batch Processing Machine with Split Compatibility Graphs

We consider the problem of minimizing the makespan on a batch processing machine, in which jobs are not all compatible. Only compatible jobs can be included into the same batch. This relation of compatibility is represented by a split graph. Jobs have release dates. The capacity of the batch processing machine is finite or infinite. The processing time of a batch is given by the processing time of the longest job in the batch. We establish the NP-hardness of the general problem and present polynomial algorithms for several special cases. Relating scheduling theory and graph theory appears to be an interesting and important concept.     

Preemptive scheduling of two uniform parallel machines to minimize total tardiness

We consider the problem of preemptive scheduling n jobs on two uniform parallel machines. All jobs have equal processing requirements. For each job we are given its due date. The objective is to find a schedule minimizing total tardiness ∑T_i. We suggest an O(n log n) algorithm to solve this problem.     

基于外包决策的并行调度随机模型

在传统的并行机器调度问题基础上引入了不确定随机变量,同时考虑了以产品外包为能力拓展形式的现代生产模式,建立了基于外包决策的并行调度随机模型.模型以带有拖期惩罚函数的最大化利润为目标,以遗传算法这种进化的启发式计算方法寻找最优解.同时引入虚拟机器的概念,实现了对外包情形下机器调度问题的有效处理和简化.实例证明,该模型更符合现代生产模式,极大地提高了企业的工作效率和经济效益.     

Scheduling tasks with exponential duration on unrelated parallel machines

This paper introduces a stochastic scheduling problem. In this problem a directed acyclic graphs (DAG) represents the precedence relations among $m$ tasks that $n$ workers are scheduled to execute. The question is to find a schedule $\Sigma$ such that if tasks are assigned to workers according to $\Sigma$, the expected time needed to execute all the tasks is minimized. The time needed to execute task $t$ by worker $w$ is a random variable expressed by a negative exponential distribution with parameter ${\lambda }_{wt}$ and each task can be executed by more than one worker at a time. In this paper, we will prove that the problem in its general form is NP-hard, but when the DAG width is constant, we will show that the optimum schedules can be found in polynomial time.     

Sequencing jobs that require common resources on a single machine: A solvable case of the TSP

In this paper a one-machine scheduling model is analyzed wheren different jobs are classified intoK groups depending on which additional resource they require. The change-over time from one job to another consists of the removal time or of the set-up time of the two jobs. It is sequence-dependent in the sense that the change-over time is determined by whether or not the two jobs belong to the same group. The objective is to minimize the makespan. This problem can be modeled as an asymmetric Traveling Salesman Problem (TSP) with a specially structured distance matrix. For this problem we give a polynomial time solution algorithm that runs in O(n logn) time. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.     

Preemptive scheduling of equal length jobs with release dates on two uniform parallel machines

We consider a problem of scheduling n jobs on two uniform parallel machines. For each job we are given its release date when the job becomes available for processing. All jobs have equal processing requirements. Preemptions are allowed. The objective is to find a schedule minimizing total completion time. We suggest an O(n³) algorithm to solve this problem.     

Scheduling interfering job sets on parallel machines

We consider bicriteria scheduling on identical parallel machines in a nontraditional context: jobs belong to two disjoint sets, and each set has a different criterion to be minimized. The jobs are all available at time zero and have to be scheduled (non-preemptively) on m parallel machines. The goal is to generate the set of all non-dominated solutions, so the decision maker can evaluate the tradeoffs and choose the schedule to be implemented. We consider the case where, for one of the two sets, the criterion to be minimized is makespan while for the other the total completion time needs to be minimized. Given that the problem is NP-hard, we propose an iterative SPT–LPT–SPT heuristic and a bicriteria genetic algorithm for the problem. Both approaches are designed to exploit the problem structure and generate a set of non-dominated solutions. In the genetic algorithm we use a special encoding scheme and also a unique strategy – based on the properties of a non-dominated solution – to ensure that all parts of the non-dominated front are explored. The heuristic and the genetic algorithm are compared with a time-indexed integer programming formulation for small and large instances. Results indicate that the both the heuristic and the genetic algorithm provide high solution quality and are computationally efficient. The heuristics proposed also have the potential to be generalized for the problem of interfering job sets involving other bicriteria pairs.     

Minimizing the sum of job completion times on capacitated two-parallel machines

This paper considers a scheduling problem with two identical parallel machines. One has unlimited capacity; the other can only run for a fixed time. A given set of jobs must be scheduled on the two machines with the goal of minimizing the sum of their completion times. The paper proposes an optimal branch and bound algorithm which employs three powerful elements, including an algorithm for computing the upper bound, a lower bound algorithm, and a fathoming condition. The branch and bound algorithm was tested on problems of various sizes and parameters. The results show that the algorithm is quite efficient to solve all the test problems. In particular, the total computation time for the hardest problem is less than 0.1 second for a set of 100 problem instances. An important finding of the tests is that the upper bound algorithm can actually find optimal solutions to a quite large number of problems.     

两台恒速机上的MapReduce排序算法研究

研究源自于MapReduce系统的一类排序问题。给定两台恒速机和一组按列表到达的工件,每个工件包含两类任务：Map Task和Reduce Task。假设Map任务和Reduce任务都是不可中断的,Map任务可以并行处理,即可以任意分割成若干小的任务并在两台机器上同时处理,而Reduce任务只可以在单台机器上处理。一旦工件到达,必须为其指派机器和开工时间,目标是使得最后完工时间最小。对LSc算法的竞争比进行了分析,得到其一般情形下的竞争比当$sgeq（1+sqrt{5}）/2$时为$1+1/s$,否则为$1+s/（s+1）$。而当每个工件$J_j$都满足其Map任务总长大于等于Reduce任务总长时,其竞争比当$sgeq（1+sqrt{3}）/2$时不超过$1+1/（2s）$,否则为不超过$1+s/（2s+1）$。     

Scheduling three chains on two parallel machines

We consider the problem of scheduling n tasks subject to chain-precedence constraints on two identical machines with the objective of minimizing the makespan. The problem is known to be strongly NP-hard. Here, we prove that it is binary NP-hard even with three chains. Furthermore, we characterize the complexity of this case by presenting a pseudopolynomial time algorithm and a fully polynomial time approximation scheme.     

Scheduling problems with two competing agents to minimize minmax and minsum earliness measures

A relatively new class of scheduling problems consists of multiple agents who compete on the use of a common processor. We focus in this paper on a two-agent setting. Each of the agents has a set of jobs to be processed on the same processor, and each of the agents wants to minimize a measure which depends on the completion times of its own jobs. The goal is to schedule the jobs such that the combined schedule performs well with respect to the measures of both agents. We consider measures of minmax and minsum earliness. Specifically, we focus on minimizing maximum earliness cost or total (weighted) earliness cost of one agent, subject to an upper bound on the maximum earliness cost of the other agent. We introduce a polynomial-time solution for the minmax problem, and prove NP-hardness for the weighted minsum case. The unweighted minsum problem is shown to have a polynomial-time solution.     

带批运输的两台同型机排序问题的改进算法

研究带批运输的两台同型机排序问题. 在该问题中,工件在两台同型机上加工,完工的工件由一辆容量为z的车运输到客户. 这里假设工件有不同的物理大小,目标是求一个时间表使得所有工件送达客户且车回到机器所在位置的时间最小,给出了一个(14/9+ε)-近似算法     

并行加工系统中的一种排序算法

通过对现有单机和相同机组并行加工系统排序问题的研究，建立了一类多机非相同机组并行加工系统的排序模型，模型的优化目标是工件排序的拖期总数为极小。由于已经证明它是一个NP问题，本提出了一个针对该问题的快速、实用的启发式排序算法，并用实例说明了算法的有效性。