带学习效应和资源依赖的单机排序模型

在工业生产中,随着员工操作技能的熟练程度的增加,对于相同的任务越往后加工,所花的时间将会减少。 同时,为了尽早完工,管理者也会考虑给加工工件分配一定量的额外资源来缩短工件加工时间。 本文基于以上实例,讨论了工件的实际加工时间既具有学习效应又依赖所分配资源的单机排序问题。 在问题中,假设工件的学习效应是之前已加工工件正常加工时间和的指数函数。 同时随着分配给工件资源量的增加,工件的实际加工时间呈线性减少,所需费用呈线性增加。对这一排序模型,主要探讨以下五个目标函数:最小化最大完工时间与资源消耗量总费用的和;最小化总完工时间与资源消耗量总费用的和;最小化加权总完工时间与资源消耗量总费用的和;最小化总提前、总延误、总共同交货期与资源消耗量总费用的和以及最小化总提前、总延误、总松弛交货期与资源消耗量总费用的和。 本文对前三个目标函数相应的排序问题给出了多项式时间可求解的算法。 对后两个目标函数所涉及的排序问题借助于指派问题分别给出了时间复杂性为O(n³)的算法。     

工期窗口安排与具有退化效应和维修活动的单机排序

考虑具有工件相关的退化效应和维修活动的单机排序模型,讨论了工期窗口安排问题.在这一模型中,机器在加工过程中产生退化使效率降低,工件的实际加工时间不仅与其所在排序中的位置有关并且与其本身的退化率有关;然而,维修活动能使机器的加工效率得到恢复.工期窗口的开始时间是已给定的常量,而工期窗口的结束时间是需要确定的变量.目标是得到安排维修活动的最佳时间、最佳工期窗口的大小和最优排序以便最小化流时间、提早、延误和工期窗口大小的总处罚函数.对这一问题,给出了一多项式算法.     

Single machine scheduling with batch deliveries

The single machine batch scheduling problem is studied. The jobs in a batch are delivered to the customer together upon the completion time of the last job in the batch. The earliness of a job is defined as the difference between the delivery time of the batch to which it belongs and its completion time. The objective is to minimize the sum of the batch delivery and job earliness penalties. A relation between this problem and the parallel machine scheduling problem is identified. This enables the establishment of complexity results and algorithms for the former problem based on known results for the latter problem.     

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。     

具有CON/SLK交货期指派的一类单机排序问题

研究工件的实际加工时间既具有指数学习效应,又依赖所消耗资源的准时制排序问题.在模型中,探讨了共同交货期(CON)和松弛交货期(SLK)两种情形.管理者的目标是确定最优序、最优资源分配方案和最佳工期(共同交货期或松弛交货期)以便极小化工件的总延误、总提前、总工期和资源消耗费用的总和.对于工件的实际加工时间是资源消耗量的线性函数的排序问题,通过将其转化为指派模型,给出了时间复杂性为O(n~3)的算法,从而证明该类排序问题是多项式时间可求解的.针对工件的实际加工时间是资源消耗量的凸函数的排序问题,也给出了多项式算法.     

Single machine scheduling with truncated job-dependent learning effect

In this paper we consider the single machine scheduling problem with truncated job-dependent learning effect. By the truncated job-dependent learning effect, we mean that the actual job processing time is a function which depends not only on the job-dependent learning effect (i.e., the learning in the production process of some jobs to be faster than that of others) but also on a control parameter. The objectives are to minimize the makespan, the total completion time, the total absolute deviation of completion time, the earliness, tardiness and common (slack) due-date penalty, respectively. Several polynomial time algorithms are proposed to optimally solve the problems with the above objective functions.     

The single-machine earliness-tardiness scheduling problem with due date assignment and resource-dependent processing times

We study the earliness-tardiness scheduling problem on a single machine with due date assignment and controllable processing times. We analyze the problem with three different due date assignment methods and two different processing time functions. For each combination of these, we provide a polynomial-time algorithm to find the optimal job sequence, due date values and resource allocation minimizing an objective function which includes earliness, tardiness, due date assignment, makespan and total resource consumption costs.     

Minmax scheduling problems with common flow-allowance

In due-date assignment problems with a common flow-allowance, the due-date of a given job is defined as the sum of its processing time and a job-independent constant. We study flow-allowance on a single machine, with an objective function of a minmax type. The total cost of a given job consists of its earliness/tardiness and its flow-allowance cost components. Thus, we seek the job schedule and flow-allowance value that minimize the largest cost among all the jobs. Three extensions are considered: the case of general position-dependent processing times, the model containing an explicit cost for the due-dates, and the setting of due-windows. Properties of optimal schedules are fully analysed in all cases, and all the problems are shown to have polynomial time solutions.     

Single-machine scheduling with time windows and earliness/tardiness penalties

We study the single machine earliness/tardiness problem with arbitrary time windows (STW). We show that STW is NP-hard and then decompose it into the subproblems of finding a good job sequence and optimally inserting idle time into a given sequence. We propose heuristics for the sequencing subproblem by appropriately modifying heuristics originally developed for other single machine scheduling problems. Experimentation with randomly generated problems shows that one of the proposed heuristics is computationally efficient and capable of finding good solutions to problems of arbitrary size. We also propose an algorithm to optimally insert idle time into a given job sequence.     

Single machine common flow allowance scheduling with deteriorating jobs and a rate-modifying activity

We study a single machine slack due date assignment (usually referred to as SLK) scheduling problem with deteriorating jobs and a rate-modifying activity. The deterioration effect manifest such that the job processing time is a function of its starting time in a sequence. The rate-modifying activity is an activity that changes the processing rate of machine, i.e., the machine performs a rate-modifying activity. Hence the actual processing time of a job is a variable, which depends not only on its starting time in a sequence but also on whether it is scheduled before or after a rate-modifying activity. The goal is to schedule the rate-modifying activity, the optimal common flow allowance and the sequence of jobs to minimize the total earliness, the total tardiness and the common flow allowance cost. We show that the problem remains polynomially solvable under the proposed model.     

带强制工期的可中断单机排序问题

工件带强制工期,指工件必须在已给定的工期内完工,不得延迟.这种环境在实际应用中随处可见.如果工件过早提前完工,意味着工件还需要保管,将会产生额外费用.本文讨论了在单机上,加工带准备时间与强制工期的n个可中断工件,在机器可空闲条件下,确定一个工件排序,使得提前完工时间和最小.先考虑了问题的复杂性,通过奇偶划分问题归约,证明了其是NP-complete的.而后,讨论了加工时间相等的特殊情形,由于工件不允许延迟,问题可能会无可行排序,因此提出了—个多项式时间算法,既能判定可行性,又能针对可行问题获得最优排序.     

公共交货期窗口的提前／延期惩罚

本讨论n个独立工件在一台机器上加工,而且加工时间服从正态分布的公共交货期窗口的提前/延期惩罚问题,在确定公共交货期窗口情况下,推导出工件的最优排序具有V型特征。     

具有退化工件和工期窗口安排的排序问题

针对具有退化工件的排序模型,考虑了单机排序和两台机器流水作业的工期窗口安排问题,在这一模型中,工件的加工时间是与其开工时间和退化率有关的一个线性函数。目标是找到一个最优排序和确定工期窗口的开始时间及大小以便最小化所有工件的费用函数,费用函数由四部分组成:提前、延误、工期窗口开始时间和工期窗口大小。对所研究的单机问题,详细地讨论了符合现实情况的几种类型问题,并得到了问题的最优解;对两台机器流水作业问题,给出了多项式算法。     

Single machine common flow allowance scheduling with controllable processing times

In this paper, we consider single machine SLK due date assignment scheduling problem in which job processing times are controllable variables with linear costs. The objective is to determine the optimal sequence, the optimal common flow allowance and the optimal processing time compressions to minimize a total penalty function based on earliness, tardiness, common flow allowance and compressions. We solve the problem by formulating it as an assignment problem which is polynomially solvable. For some special cases, we present an O(n logn) algorithm to obtain the optimal solution respectively.     

Single machine scheduling and due date assignment with positionally dependent processing times

We consider single machine scheduling and due date assignment problems in which the processing time of a job depends on its position in a processing sequence. The objective functions include the cost of changing the due dates, the total cost of discarded jobs that cannot be completed by their due dates and, possibly, the total earliness of the scheduled jobs. We present polynomial-time dynamic programming algorithms in the case of two popular due date assignment methods: CON and SLK. The considered problems are related to mathematical models of cooperation between the manufacturer and the customer in supply chain scheduling.     

Scheduling in bicriteria single machine systems with past-sequence-dependent setup times and learning effects

Scheduling with setup times and learning plays a crucial role in today's manufacturing and service environments where scheduling decisions are made with respect to multiple performance criteria rather than a single criterion. In this paper, we address a bicriteria single machine scheduling problem with job-dependent past-sequence-dependent setup times and job-dependent position-based learning effects. The setup time and actual processing time of a job are respectively unique functions of the actual processing times of the already processed jobs and the position of the job in a schedule. The objective is to derive the schedule that minimizes a linear composite function of a pair of performance criteria consisting of the makespan, the total completion time, the total lateness, the total absolute differences in completion times, and the sum of earliness, tardiness, and common due date penalty. We show that the resulting problems cannot be solved in polynomial time; thus, branch-and-bound (B&B) methods are proposed to obtain the optimal schedules. Our computational results demonstrate that the B&B can solve instances of various size problems with attractive times.     

Parallel machine scheduling with a common due window

In this paper, we consider a machine scheduling problem where jobs should be completed at times as close as possible to their respective due dates, and hence both earliness and tardiness should be penalized. Specifically, we consider the problem with a set of independent jobs to be processed on several identical parallel machines. All the jobs have a given common due window. If a job is completed within the due window, then there is no penalty. Otherwise, there is either a job-dependent earliness penalty or a job-dependent tardiness penalty depending on whether the job is completed before or after the due window. The objective is to find an optimal schedule with minimum total earliness–tardiness penalty. The problem is known to be NP-hard. We propose a branch and bound algorithm for finding an optimal schedule of the problem. The algorithm is based on the column generation approach in which the problem is first formulated as a set partitioning type formulation and then in each branch and bound iteration the linear relaxation of this formulation is solved by the standard column generation procedure. Our computational experiments show that this algorithm is capable of solving problems with up to 40 jobs and any number of machines within a reasonable computational time.     

Scheduling with batch setup times and earliness-tardiness penalties

We consider a scheduling model in which several batches of jobs need to be processed by a single machine. During processing, a setup time is incurred whenever there is a switch from processing a job in one batch to a job in another batch. All the jobs in the same batch have a common due date that is either externally given as an input data or internally determined as a decision variable. Two problems are investigated. One problem is to minimize the total earliness and tardiness penalties provided that each due date is externally given. We show that this problem is NP-hard even when there are only two batches of jobs and the two due dates are unrestrictively large. The other problem is to minimize the total earliness and tardiness penalties plus the total due date penalty provided that each due date is a decision variable. We give some optimality properties for this problem with the general case and propose a polynomial dynamic programming algorithm for solving this problem with two batches of jobs. We also consider a special case for both of the problems when the common due dates for different batches are all equal. Under this special case, we give a dynamic programming algorithm for solving the first problem with an unrestrictively large due date and for solving the second problem. This algorithm has a running time polynomial in the number of jobs but exponential in the number of batches.     

Job sequencing and due date assignment in a single machine shop with uncertain processing times

This paper considers due date assignment and sequencing for multiple jobs in a single machine shop. The processing time of each job is assumed to be uncertain and is characterized by a mean and a variance with no knowledge of the entire distribution. A heuristic procedure is developed to find job sequence and due date assignment to minimize a linear combination of three penalties: penalty on job earliness, penalty on job tardiness, and penalty associated with long due date assignment. Numerical experiments indicate that the performance of the procedure is stable and robust to job processing time distributions. In addition, the performance improves when the means and variances of job processing times are uncorrelated or negatively correlated or when the penalty of a long due date assignment is significant.     

Scheduling problems with random processing times under expected earliness/tardiness costs

This work is concerned with scheduling problems for a single machine. Taking earliness and tardiness of completion time and due–date value into consideration, the objective function with a common due date is considered. The processing time of each job is random. Sufficient conditions guaranteeing an optimal SEPT sequence are derived. Under exponential and normal processing times, further results are obtained