关于总误工损失的两个代理单机排序问题

研究了与总误工损失相关的两个代理的单机排序问题。第一个代理以工件的总误工损失为目标函数,第二个代理以工件的总完工时间或总误工工件数为目标函数。目标是寻找一个排序,使得在第二个代理的目标函数不超过给定的上界的条件下,第一个代理的目标函数值最小。对这两个与总误工损失相关的两个代理的单机排序问题,分别给出它们的拟多项式时间的动态规划算法。     

一个具有两类工件的多目标排序的NP-困难性

文章考虑具有两个工件集的单机排序问题.第一个工件集J1以加权完工时间和为目标函数,第二个工件集J2以最大加权完工时间为目标函数.问题的目标是寻找一种排序,使得两个目标函数的加权和达到最小,并证明该问题是强NP-困难的.     

一个具有两类工件的多目标排序的多项式时间算法

本文考虑具有两个工件集的单机排序问题.第一个工件集J1以完工时间和为目标函数,第二个工件集J2以最大加权完工时间为目标函数.问题的目标是寻找一种排序,使得两个目标函数的加权和达到最小.本文证明该问题可在O(n1n2(n1 n2))时间内求解.     

机器带准备时间的同类机分批排序算法

讨论了两类机器带准备时间的同类机分批排序问题.对工件无到达时间及有常数个到达时间,目标函数为极小化加权总完工时间这两类问题进行研究,给出了两个最优算法,并对算法及其计算复杂性给予了分析与证明.     

极小化加权总完工时间的分批排序问题

本文讨论了分批排序中极小化加权总完工时间的两个问题.就所有工件的加工时间都相等这一特殊情况,分别给出两个算法,并证明了算法的最优性.     

具有学习和退化效应的单机干扰管理问题

针对工件同时具有学习和退化效应、机器具有可用性限制这一问题,建立可预见性单机干扰管理模型。在这一模型中,工件的加工时间是既与工件所排的加工位置又与工件开始加工的时间有关的函数。同时,在生产过程中由于机器发生故障或定期维修等扰动事件导致机器在某段时间内不能加工工件。目标是在同时考虑原目标函数和由扰动造成的偏离函数的情况下,构建一个新的最优时间表序列。根据干扰度量函数的不同研究了两个问题,第一个问题的目标函数是极小化总完工时间与总误工时间的加权和;第二个问题的目标函数是极小化总完工时间与总提前时间的加权和。对于所研究的问题,首先证明了最优排序具有的性质,然后建立了相应的拟多项式时间动态规划算法。     

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．     

两类加工时间是一般函数的单机排序问题

考虑了两类有一般加工时间函数的排序问题. 工件的加工时间分别为基本加工时间与开工时间函数、位置函数的和. 对加工时间依赖开工时间的模型,证明了一定条件下极小化最大完工时间和极小化总完工时间是多项式可解的. 对加工时间依赖开工位置的模型,给出极小化最大完工时间和极小化总完工时间的最优序,同时证明了极小化加权总完工时间的一个最优排序性质并给出一个贪婪算法.     

分批加工中迟后范围的极小化问题

排序中以工件迟后范围作为极小化的目标函数体现了生产中对顾客的平等对待，对此目标函数以往的研究局限于非成批加工．随着成批加工大量出现于柔性制造系统中，其它一些目标函数如加权完工时间之和，最大迟后己出现在成批加工问题中，但还无人讨论工件迟后范围问题．本文对工件加工顺序给定时如何使迟后范围极小的最优分批问题建立了所需时间为多项式的动态规划算法，并进一步给出了一些性质．     

无关机上极小化求和问题的平行分批排序(英文)

本文我们考虑了无关机上的平行分批排序问题.对于批容量无限的平行批排序模型,目标是极小化总完工时间,我们对p_(ij)≤p_(ik)(i=1,…,m;1≤j≠k≤n)这种一致性的情况设计了多项式的动态规划算法.对于批容量有限的平行批排序模型,我们讨论了p_(ij)=p_i(i=1,…,m;j=1,…,n)这种情况,当不考虑工件可被拒绝时,对极小化加权总完工时间的排序,我们给出了其最优算法;当考虑工件可被拒绝时,对极小化被接收工件的加权总完工时间加上被拒绝工件的总拒绝费用的排序,我们设计了一拟多项时间算法.     

Polynomial time solutions for scheduling problems on a proportionate flowshop with two competing agents

In scheduling problems with two competing agents, each one of the agents has his own set of jobs and his own objective function, but both share the same processor. The goal is to minimize the value of the objective function of one agent, subject to an upper bound on the value of the objective function of the second agent. In this paper we study two-agent scheduling problems on a proportionate flowshop. Three objective functions of the first agent are considered: minimum maximum cost of all the jobs, minimum total completion time, and minimum number of tardy jobs. For the second agent, an upper bound on the maximum allowable cost is assumed. We introduce efficient polynomial time solution algorithms for all cases.     

Two-agent single-machine scheduling problems under increasing linear deterioration

A new scheduling model in which both two-agent and increasing linear deterioration exist simultaneously is investigated in this paper. The processing time of a job is defined as an increasing linear function of its starting time. Two agents compete to perform their respective jobs on a common single machine and each agent has his own criterion to optimize. We introduce an increasing linear deterioration model into the two-agent single-machine scheduling, where the goal is to minimize the objective function of the first agent with the restriction that the objective function of the second agent cannot exceed a given upper bound. We study two scheduling problems with the different combinations of two agents’ objective functions: makespan, maximum lateness, maximum cost and total completion time. We propose the optimal properties and present the optimal polynomial time algorithms to solve the scheduling problems, respectively.     

Two-agent scheduling with position-based deteriorating jobs and learning effects

Scheduling with deteriorating jobs and learning effects has been widely studied. However, multi-agent scheduling with simultaneous considerations of deteriorating jobs and learning effects has hardly been considered until now. In view of this, we consider a two-agent single-machine scheduling problem involving deteriorating jobs and learning effects simultaneously. In the proposed model, given a schedule, we assume that the actual processing time of a job of the first agent is a function of position-based learning while the actual processing time of a job of the second agent is a function of position-based deterioration. The objective is to minimize the total weighted completion time of the jobs of the first agent with the restriction that no tardy job is allowed for the second agent. We develop a branch-and-bound and several simulated annealing algorithms to solve the problem. Computational results show that the proposed algorithms are efficient in producing near-optimal solutions.     

同时最小化最大费用和最大完工时间的双代理无界平行分批排序

有两个代理A和B, 每个代理都各自有一个工件集. 同一个代理的工件可以在同一批中加工, 而且每一个代理都有一个需要最小化的函数. 研究在无界平行分批处理机上同时最小化代理A的最大费用和代理B的最大完工时间问题, 并给出一个算法, 它可在多项式时间内找到关于这个问题的所有Pareto最优点.     

一个具有退化的两个代理的排序问题

考虑两个代理的带有退化的单机排序问题.第一个代理J以完工时间和为目标函数,第二个代理J以最大延迟为目标函数,并且两个代理的加工时间是按时间退化的,所谓按时间退化就是每个工件的加工时间是其开始加工时间的函数.问题的目标是寻找一种排序,使得两个代理的目标函数之和达到最小.证明该问题可在O(n_1n_2(n_1+n_2))时间内求解.     

A single-machine scheduling problem with two-agent and deteriorating jobs

Deteriorating jobs scheduling problems have been extensively studied in recent years. However, it is assumed that there is a common goal to minimize for all jobs in most of the research. In many management situations, multiple agents compete on the usage of a common processing resource. In this paper, we considered a single-machine scheduling problem with a linear deterioration assumption where the objective is to minimize the total weighted completion time of jobs from the first agent with the restriction that no tardy job is allowed for the second agent. We proposed a branch-and-bound algorithm and three heuristic algorithms to search for the optimal solution and near-optimal solutions, respectively. A computational experiment was conducted to evaluate the performance of the proposed algorithms.     

A note on two-agent scheduling on an unbounded parallel-batching machine with makespan and maximum lateness objectives

This paper studies the two-agent scheduling on an unbounded parallel-batching machine. In the problem, there are two agents A and B with each having their own job sets. The jobs of a common agent can be processed in a common batch. Moreover, each agent has an objective function to be minimized. The objective function of agent A is the makespan of his jobs and the objective function of agent B is maximum lateness of his jobs. Yazdani Sabouni and Jolai [M.T. Yazdani Sabouni, F. Jolai, Optimal methods for batch processing problem with makespan and maximum lateness objectives, Appl. Math. Model. 34 (2010) 314–324] presented a polynomial-time algorithm for the problem to minimize a positive combination of the two agents’ objective functions. Unfortunately, their algorithm is incorrect. We then dwell on the problem and present a polynomial-time algorithm for finding all Pareto optimal solutions of this two-agent parallel-batching scheduling problem.     

Scheduling two agents with controllable processing times

We consider several two-agent scheduling problems with controllable job processing times, where agents A and B have to share either a single machine or two identical machines in parallel while processing their jobs. The processing times of the jobs of agent A are compressible at additional cost. The objective function for agent B is always the same, namely a regular function _fmax$f_{\text{max}}$. Several different objective functions are considered for agent A, including the total completion time plus compression cost, the maximum tardiness plus compression cost, the maximum lateness plus compression cost and the total compression cost subject to deadline constraints (the imprecise computation model). All problems are to minimize the objective function of agent A subject to a given upper bound on the objective function of agent B. These problems have various applications in computer systems as well as in operations management. We provide NP-hardness proofs for the more general problems and polynomial-time algorithms for several special cases of the problems.