单机两组工件继列分批与平行分批混合排序

研究单机两组工件继列分批与平行分批混合排序.在问题中有两组工件J^A和JA和J^B.A-工件可以在平行批中进行加工,B-工件可以在继列批中进行加工.对若干正则目标函数给出了多项式时间算法.主要结果如下:·排序问题1|s-p-batch,s(B),(∞,∞)|L_(max)在O(n_An_Bn)时间可解.·排序问题1|s-p-batch,s(B),(∞,b(B))|∑C_j在O(n_An_Bn)时间可解.·排序问题1|s-p-batch,p_j=1,s(B),(b(A),b(B))|∑w_jC_j在O(n_An_Bn)时间可解.·排序问题1|s-p-batch,s(B),(∞,b(B))|f_(max)可以在时间界为O(log(max_jf_j(M))×(nlogM+n_An_Bn))内可解.其中,M是工件完工时间的一个上界.     

加工时间和尺寸成正比单机排序和工件运输的最小化最大完工时间问题

在单机排序和工件运输的最小化最大完工时间问题中,工件首先在一台机器上加工,然后被一辆有容量限制的汽车运送到一个顾客．当工件的加工时间和尺寸无关时, Chang和Lee已经证明该问题是强NP困难的．他们也给出了一个启发式算法,它的最差执行比为5／3,并且这个界是紧的．本文考虑工件的加工时间和尺寸成正比的情形,证明了Chang和Lee的算法有更好的最差执行比53／35,并提供了一个新的启发式算法,它的最差执行比是3／2,并且这个界是最好的．     

有尺寸的同型机分批排序问题的近似算法

对工件有不同到达时间、不同加工时间和尺寸的同型机分批排序问题寻找近似算法.对于大工件(工件的体积严格大于机器容量的÷)的加工时间不小于小工件(工件的体积小于或等于机器容量的÷)的加工时间的特定情形,利用动态规划的方法和拆分的技巧,我们设计了近似算法并分析了其最差性能比.     

带有链优先序的分批排序问题

本文首次就带有优先序的分批排序问题进行了讨论，目标函数为最大完工时间．当优先序为链，一条链上的工件个数为饨，而其它链的工件个数为常数，分批的容量B大于等于链的条数，在这种情况下，问题为多项式可解的．文中并讨论了几种特殊情况的多项式算法．     

工件带就绪时间的单机供应链排序问题

研究工件带就绪时间的单机供应链排序问题,即工件到达后按何种顺序在机器上加工,并将完工工件如何由运输工具发送给客户,使得生产费用与发送费用总和最少.这里,每个工件的生产费用为工件的发送时刻,多个工件可组成一批一次发送给客户,发送费用与发送次数成正比.对于工件允许中断加工的问题,基于SRPT规则给出多项式时间的动态规划算法求解最优序;对于工件不允许中断加工的问题,证明问题是强NP难的,并提出了性能比为2的近似算法.     

序列错位限制下最小化完工时间和的继列分批重新排序

在单机分批排序中,一个原始工件集已经分好批排好顺序,使得给定的目标函数最小.当一个新的工件集到来时,决策者需要插入这些新工件到原来的顺序中,这样使得原始工件就会产生一些错位.但为了满足对原始工件集的要求而不过分的打乱它们的顺序的条件下,使得新的目标值为最优.本文主要研究的是在序列错位量限制的条件下,继列分批最小化总完工时间的重新排序问题,对于最大序列错位和总序列错位的不同约束情况下,研究可行排序和最优排序的结构性质,进而设计了它们的多项式时间算法.     

离散加工时间的可控排序问题

本文主要研究了离散加工时间的可控排序问题,目标函数是总压缩费用约束下极小化最大完工时间,对单机工件有不同到达时间以及同型机工件到达时间都相同这两个问题,我们设计了伪多项式时间的动态规划算法,并给出了相应的FPTAS算法．     

工件满足一致性的同类机在线分批排序问题

研究了工件满足一致性,批容量无界的两台同类机在线分批排序问题,目标为极小化工件的最大完工时间和极小化工件的最大流程时间,三元素法分别表示为Q_2|r_ir_j?p_i≤p_j,B=∞, on-line|C_(max),Q_2|r_ir_j?p_i≥p_j,B=∞, on-line|F_(max).不失一般性,假设第一台机器速度为1,第二台机器速度为s,s≥1.对于上述两类问题设计了一个在线算法,并分析了算法竞争比的上界.对第一类问题该在线算法的竞争比不超过s+α,这里α为α~2+sα-1=0的正根,特别地,当s=1时,该算法的竞争比不超过1.618.对第二类排序问题,该在线算法的竞争比不超过1+1/α.     

并行分批排序综述

并行分批排序起源于半导体芯片制造过程。在并行分批排序中,工件可成批加工,批加工机器最多可同时加工B个工件,批的加工时间为批中所有工件的最大工时。首先根据传统的机器环境和目标函数对并行分批排序已有成果进行分类介绍,主要为单机和平行机的机器环境,以及极小化最大完工时间、极小化总完工时间、极小化最大延迟、极小化误工工件数、极小化总延误和极小化最大延误的目标函数;然后梳理了由基本问题所衍生出来的具有新特点的16类新型并行分批排序,包括差异尺寸工件、多目标、工件加工时间或顺序存在限制、考虑费用和具有特殊机制等情况;最后展望未来的研究方向。     

工件可转包加工的排序问题研究

研究工件可以转包加工的单台机排序问题: 有n个工件, 在零时刻已经到达一个单台机处, 每个工件可以由加工者自有的单台机器加工或者转包给其他机器加工. 如果工件被转包加工, 那么其完工时间等于在自有机器上的加工时间, 而产生的加工费用与在自有机器上加工的费用不同. 假设被转包加工的工件的完工时间和加工费用与转包加工机器的总负载没有关系.目标函数是最小化工件最大完工时间与总加工费用的加权和. 该问题已经被证明是NP-难的. 最后给出该问题的伪多项式时间最优算法, 并且提出一个完全多项式时间近似方案(FPTAS).     

The coordination of transportation and batching scheduling

We study a coordinated scheduling problem of production and transportation in which each job is transported to a single batching machine for further processing. There are m vehicles that transport jobs from the holding area to the batching machine. Each vehicle can transport only one job at a time. The batching machine can process a batch of jobs simultaneously where there is an upper limit on the batch size. Each batch to be processed occurs a processing cost. The problem is to find a joint schedule of production and transportation such that the sum of the total completion time and the total processing cost is optimized. For a special case of the problem where the job assignment to the vehicles is predetermined, we provide a polynomial time algorithm. For the general problem, we prove that it is NP-hard (in the ordinary sense) and present a pseudo-polynomial time algorithm. A fully polynomial time approximation scheme for the general problem is obtained by converting an especially designed pseudo-polynomial dynamic programming algorithm.     

堆取料机调度问题的一个近似算法

研究源自煤炭港口料场管理的堆取料机调度问题.该问题中,堆取料机从长为L的料场作业区最左端开始空机或载料运行,两者的速度之比为s：1（s ≥ 1）.决策者需确定储料堆在作业区的分布以及堆取料机处理它们的先后次序,目的是极小化一批煤料运输船的在港服务时间.考虑堆取料机处理完毕需要回到料场终端的作业模型,证明了存在最坏情况界不超过1+1/4s的近似算法.     

具有学习效应的三层供应链排序问题

研究了具有学习效应的三层供应链排序问题. 多个客户分布在不同位置,每个客户都有订 单需要制造商进行生产. 制造商需要针对每一个不同订单的客户从不同的地方进购对应的原材料进行生产,生产完工后需要利用有限的车辆将工件运输到相应客户处. 要求每辆运输车装载尽可 能多的货物才开始运输. 利用动态规划算法研究了最大流程时间、总流程时间以及最大延迟三个目标函数.     

优化交货期窗口的两阶段供应链排序问题

研究一类优化交货期窗口的两阶段供应链排序问题. 优化交货期窗口是指交货期窗口的开始与结束时刻是决策变量, 不是输入常量. 两阶段是指工件先加工, 后运输: 加工阶段是一台加工机器逐个加工工件;运输阶段是无限台车辆分批运输完工的工件. 工件的开始运输时刻与完工时刻之差定义为工件的储存时间, 且有相应的储存费用. 若工件的运输完成时刻早于(晚于)交货期窗口的开始(结束)时刻, 则有相应的提前(延误)惩罚费用. 目标是极小化总提前惩罚费用、总延误惩罚费用、总储存费用、总运输费用以及与交货期窗口有关的费用之和. 针对单位时间的延误惩罚费用不超过单位时间的储存费用、单位时间的储存费用不超过单位时间的提前惩罚费用的情形, 给出了时间复杂性为O(n^{8})的动态规划算法.     

Uniform machine scheduling with machine available constraints

1.IntroductionIntheclassicalparallelmachineschedulingareaweassumethatmachinesarealwaysavailable.However,aspointedin[1],inrealindustrysettingsthisassumptionmaynotbetrue.Forexample,machinesmaynotalwaysbeavailablebecauseoftheirpreventivemaintenanceduringtheschedulingperiod.Thatistosay,eachmachineiisunavailablefromsibuntilrib(05sib5rib),where0SkSm,withmbeingthenumberofunavailabilityperiodsformachineiduringtheplanninghorizon.Inotherwords,somepapersstatethatmachinesareavailableintimewindows,whichi…     

具有可变配送费用和固定配送时刻的单机排序问题

研究了单机环境下生产与配送的协同排序问题.有多个工件需要在一台机器上进行加工,加工完的工件需要分批配送到一个客户.每批工件只能在固定的几个配送时刻出发,不同的配送时刻对应着不同的配送费用.我们的目标是找到生产与配送的协同排序,极小化排序的时间费用与配送费用的加权和.研究了排序理论中主要的四个目标函数,构建了单机情况下的具体模型,分析了问题的复杂性,对于配送费用单调非增的情况给出了它们的最优算法.     

两台平行机完工时间平方和最小的排序问题

在两个竞争公司进行零和博弈过程中, 最大化两个公司收益的乘积, 在两台平行机的离线排序问题中相当于最小化两台机器完工时间的平方和. 给出了该问题修改的延缓开始\ LPT\ 算法: 首先, 将工件按照加工时间$\p_j\ $的\ LPT\ 序重新标记; 若加工时间最长的前\ $2m$\ 个工件的总加工时间\ $P(2m)< (2m+1)p_{2m+1}$, 最优的安排加工前\ $2m+1$\ 个工件, 一旦有机器空闲, 依次从第\ $2m+2$\ 个工件安排加工; 否则,\ $P(2m)\geq (2m+1)p_{2m+1}$, 最优的安排加工前\ $2m$\ 个工件, 一旦有机器空闲, 依次从第\ $2m+1$\ 个工件安排加工. 证明了该算法的最差性能比不超过\ $1+ ( \frac{1}{2m+2} )^2$, 且界是紧的.     

Using genetic algorithms for the coordinated scheduling problem of a batching machine and two-stage transportation

This paper considers a coordinated scheduling problem. For the first-stage transportation there is a crane available to transport the product from the warehouse to a batching machine. For the second-stage transportation there is a vehicle available to deliver the completed jobs from the machine shop floor to the customer. The coordinated scheduling problem of production and transportation deals with sequencing the transportation of the jobs and combining them into batches to be processed. The problem of minimizing the sum of the makespan and the total setup cost was proven by Tang and Gong [1] to be strongly NP-hard. This paper proposes two genetic algorithm (GA) approaches for this scheduling problem, with different result representations. The experimental results demonstrate that a regular GA and a modified GA (MGA) can find near-optimal solutions within an acceptable amount of computational time. Among the two proposed metaheuristic approaches, the MGA is superior to the GA both in terms of computing time and the quality of the solution.     

Approximation algorithms for scheduling unrelated parallel machines

We consider the following scheduling problem. There arem parallel machines andn independent jobs. Each job is to be assigned to one of the machines. The processing of jobj on machinei requires timep _ij . The objective is to find a schedule that minimizes the makespan.Our main result is a polynomial algorithm which constructs a schedule that is guaranteed to be no longer than twice the optimum. We also present a polynomial approximation scheme for the case that the number of machines is fixed. Both approximation results are corollaries of a theorem about the relationship of a class of integer programming problems and their linear programming relaxations. In particular, we give a polynomial method to round the fractional extreme points of the linear program to integral points that nearly satisfy the constraints.In contrast to our main result, we prove that no polynomial algorithm can achieve a worst-case ratio less than 3/2 unlessP = NP. We finally obtain a complexity classification for all special cases with a fixed number of processing times.A preliminary version of this paper appeared in theProceedings of the 28th Annual IEEE Symposium on the Foundations of Computer Science (Computer Society Press of the IEEE, Washington, D.C., 1987) pp. 217–224.     

Supply chain scheduling: Sequence coordination

A critical issue in supply chain management is coordinating the decisions made by decision makers at different stages, for example a supplier and one or several manufacturers. We model this issue by assuming that both the supplier and each manufacturer have an ideal schedule, determined by their own costs and constraints. An interchange cost is incurred by the supplier or a manufacturer whenever the relative order of two jobs in its actual schedule is different from that in its ideal schedule. An intermediate storage buffer is available to resequence the jobs between the two stages. We consider the problems of finding an optimal supplier's schedule, an optimal manufacturer's schedule, and optimal schedules for both. The objective functions we consider are the minimization of total interchange cost, and of total interchange plus buffer storage cost. We describe efficient algorithms for all the supplier's and manufacturers’ problems, as well as for a special case of the joint scheduling problem. The running time of these algorithms is polynomial in both the number of jobs and the number of manufacturers. Finally, we identify conditions under which cooperation between the supplier and a manufacturer reduces their total cost.