具有学习效应的超前有奖延误受罚的排序问题(英)

本文考虑具有学习效应和共同交货期的单机排序问题.目标函数是加权超前有奖延误受罚总和.我们的目标是寻找一个最优序使得目标函数的值最小.由于该问题是NP-hard的,我们给出一些特殊情况下多项式时间可解的特例.同时在快速估计下界的基础上给出了分支定界算法来求一般情况下的最有排序.     

具有凸资源约束的单机指数学习效应问题

研究在所有工件的正常加工时间均相同的情况下具有指数学习效应和凸资源约束的单机排序问题.给出了两种模型:在资源消耗总费用有限的情况下,以工件的最大完工时间为目标函数;在工件的最大完工时间有限的情况下,以资源消耗总费用为目标函数.求两种模型下的最优排序和最优资源分配,使得目标函数最小.证明这两个问题都是多项式时间可解的,并给出了相应的算法.     

工件加工时间是开工时间线性函数的单机排序问题

研究了具有线性恶化工件的单机排序问题,其中线性恶化工件指的是工件的加工时间是开工时间的线性增长函数.在一般情况下,对目标函数为极小化完工时间平方和与极小化总误工数问题分别给出了最优算法.此外,在分段情况下,对目标函数为极小化最大完工时间问题也给出了最优算法.     

两代理具有可拒绝的单机分批配送排序问题

本文研究具有可拒绝的两代理分批配送排序问题,两个代理竞争一台机器的使用,每个代理有自己的工件集合.工件生产完后需要分批配送到代理处,每一批需要花费一定的时间和费用.我们的目标是在保证一个代理的目标函数不超过给定值的前提下,极小化另一个代理的目标函数.对于排序理论中主要的目标函数,构建了单机情况下的具体模型,分析了问题的复杂性,对具体的问题给出了它们的最优算法.     

带有固定区间的单机双代理可中断总误工问题

研究带有固定区间的两个代理单机排序问题.第一个代理工件可中断,且工件到达时间与工期满足一致关系,目标函数为最小化总误工.第二个代理工件被安排在固定时间窗口.目标是寻找一个排序,使得满足第二个代理目标可行情况下,第一个代理目标函数值最小.在固定区间等于加工时间的情况下,利用分块原则,提出了一个伪多项式时间动态规划算法,并给出了固定区间大于加工时间情况下的时间复杂度分析.     

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.     

有关单机两代理排序问题的两个结果

研究了两个单机两代理排序问题. 在第一个两代理排序问题中, 代理A的目标函数为极小化所有工件的加权完工时间总和, 代理B的目标函数为极小化最大工件费用. 在第二个两代理排序问题中, 代理A的目标函数为极小化所有工件的加权完工时间总和, 代理B的目标函数为极小化所有工件的最大完工时间. 证明了第一个问题是强NP-难的, 改进了已有的一般意义NP-难的结果; 对第二个问题给出了一个与现有的动态规划算法不同的动态规划算法.     

单机带有可拒绝的供应链排序问题

考虑了单机上带有工件拒绝的供应链排序问题.有多个客户分布在不同区域,每个客户都有一定数量的工件需要在一台机器上进行加工.制造商可以拒绝加工一些工件,但要支付相应的拒绝费用.工件生产完后需要运输到相应的客户处,每一批配送需要花费一定的时间和费用.我们研究了排序理论中主要的几个目标函数,构建了单机情况下的具体模型,分析了问题的复杂性,对具体的问题给出了它们的最优算法.     

工件带简单线性恶化函数和共同交货期单机排序问题

本文研究单机排序问题,其中工件加工时间具有简单线性恶化函数.同时,所有工件均具有一个给定共同交货期.目标函数为最小化提前有奖延误受罚之和.在逆一致性条件下,给出了求解该排序问题的一个伪多项式时间动态规划算法.同时借助于几何舍入技巧,对求解这类排序问题给出了一个充分多项式时间的近似算法(FPTAS)。     

非线性加工时间单机排序问题

讨论工件加工时间是等待时间的非线性增加函数的单机排序问题,目标函数为极小化完工时间和与极小化最大延误.基于对问题的分析,对于一般非线性函数的情况,给出了工件间的优势关系.对于某些特殊情况,利用工件间的优势关系得到了求解最优排序的多项式算法.推广了文献中的结论.     

An effective branch-and-bound algorithm for convex quadratic integer programming

We present a branch-and-bound algorithm for minimizing a convex quadratic objective function over integer variables subject to convex constraints. In a given node of the enumeration tree, corresponding to the fixing of a subset of the variables, a lower bound is given by the continuous minimum of the restricted objective function. We improve this bound by exploiting the integrality of the variables using suitably-defined lattice-free ellipsoids. Experiments show that our approach is very fast on both unconstrained problems and problems with box constraints. The main reason is that all expensive calculations can be done in a preprocessing phase, while a single node in the enumeration tree can be processed in linear time in the problem dimension.     

Analysis of transient queues with semidefinite optimization

We derive upper bounds on the tail distribution of the transient waiting time in the GI/GI/1 queue, given a truncated sequence of the moments of the service time and that of the interarrival time. Our upper bound is given as the objective value of the optimal solution to a semidefinite program (SDP) and can be calculated numerically by solving the SDP. We also derive the upper bounds in closed form for the case when only the first two moments of the service time and those of the interarrival time are given. The upper bounds in closed form are constructed by formulating the dual problem associated with the SDP. Specifically, we obtain the objective value of a feasible solution of the dual problem in closed from, which turns out to be the upper bound that we derive. In addition, we study bounds on the maximum waiting time in the first busy period.     

A note on upper bounds to the robust knapsack problem with discrete scenarios

We consider the knapsack problem in which the objective function is uncertain, and given by a finite set of possible realizations. The resulting robust optimization problem is a max–min problem that follows the pessimistic view of optimizing the worst-case behavior. Several branch-and-bound algorithms have been proposed in the recent literature. In this short note, we show that by using a simple upper bound that is tailored to balance out the drawbacks of the current best approach based on surrogate relaxation, computation times improve by up to an order of magnitude. Additionally, one can make use of any upper bound for the classic knapsack problem as an upper bound for the robust problem.     

带凸资源和恶化效应的单机松弛窗口排序

研究带凸资源和恶化效应的单机窗口指派排序问题,其中窗口指的是松弛窗口,凸资源和恶化效应指的是工件的实际加工时间是其开始加工时间的线性函数,是其资源消耗量的凸函数。目标是确定工件的加工顺序,资源分配量以及窗口的开始加工时间和长度使其在总资源消耗费用(与窗口有关的排序费用)有上界限制的条件下,极小化与窗口有关的排序费用(总资源消耗费用)。获得了求解上述问题的最优算法,证明了该问题是多项式时间可解的。     

工件延误和可拒绝下的单机重新排序问题的近似方案

研究工件延误产生干扰且延误工件可拒绝下的单机重新排序问题。在该问题中,给定计划在零时刻到达的一个工件集需在一台机器上加工,工件集中的每个工件有它的加工时间和权重,在工件正式开始加工前,按照最短赋权加工时间优先的初始排序已经给定,目标函数是极小化赋权完工时间和,据此每个工件的承诺交付截止时间也给定。然而,在工件正式开始加工时,工件集中的部分工件由于延误不能按时到达,这对初始排序的执行产生了干扰,所以需要对初始排序进行调整,即重新排序。为了保证服务水平,允许对延误工件拒绝加工,但需支付相应的拒绝费用。调整后的重新排序的目标是在保证接受工件集中工件的最大延误不超过给定的上界的约束下,使得接受工件集的赋权完工时间和,拒绝工件集的拒绝费用和以及接受工件集中工件的最大延误的赋权惩罚费用之和达到极小。对该问题,设计了一个伪多项式时间动态规划精确算法,并利用稀疏技术得到了一个完全多项式时间近似方案。     

Computable Error Bounds for Semidefinite Programming

We study computability and applicability of error bounds for a given semidefinite pro-gramming problem under the assumption that the recession function associated with the constraint system satisfies the Slater condition. Specifically, we give computable error bounds for the distances between feasible sets, optimal objective values, and optimal solution sets in terms of an upper bound for the condition number of a constraint system, a Lipschitz constant of the objective function, and the size of perturbation. Moreover, we are able to obtain an exact penalty function for semidefinite programming along with a lower bound for penalty parameters. We also apply the results to a class of statistical problems.     

加权完工时间和的调整时间可分离Flow shop排序问题

讨论了2台机器调整时间可分离的FlowShop排序问题,目标函数为极小化加权完工时间和.给出了对于一种特殊情况,问题存在多项式最优算法的充分条件.接着又给出了求解该问题的一个分枝定界法.     

Optimality-based bound contraction with multiparametric disaggregation for the global optimization of mixed-integer bilinear problems

We address nonconvex mixed-integer bilinear problems where the main challenge is the computation of a tight upper bound for the objective function to be maximized. This can be obtained by using the recently developed concept of multiparametric disaggregation following the solution of a mixed-integer linear relaxation of the bilinear problem. Besides showing that it can provide tighter bounds than a commercial global optimization solver within a given computational time, we propose to also take advantage of the relaxed formulation for contracting the variables domain and further reduce the optimality gap. Through the solution of a real-life case study from a hydroelectric power system, we show that this can be an efficient approach depending on the problem size. The relaxed formulation from multiparametric formulation is provided for a generic numeric representation system featuring a base between 2 (binary) and 10 (decimal).     

Single-machine total completion time scheduling with a time-dependent deterioration

In this paper, we consider the single-machine scheduling problems with a time-dependent deterioration. By the time-dependent deterioration, we mean that the processing time of a job is defined by an increasing function of total normal processing time of jobs in front of it in the sequence. The objective is to minimize the total completion time. We develop a mixed integer programming formulation for the problem. The complexity status of this problem remains open. Hence, we use the smallest normal processing time (SPT) first rule as a heuristic algorithm for the general cases and analyze its worst-case error bound. Two heuristic algorithms utilize the V-shaped property are also proposed to solve the problem. Computational results are presented to evaluate the performance of the proposed algorithms.