Scheduling stochastic jobs on a single machine subject to breakdowns

A single machine is available to process a collection of stochastic jobs. There may be technological constraints on the job set. The machine sometimes breaks down. Costs are incurred and rewards are earned during processing. We seek strategies for processing the jobs which maximize the total expected reward earned.     

Scheduling stochastic jobs with asymmetric earliness and tardiness penalties

We consider a stochastic counterpart of the well-known earliness-tardiness scheduling problem with a common due date, in which n stochastic jobs are to be processed on a single machine. The processing times of the jobs are independent and normally distributed random variables with known means and known variances that are proportional to the means. The due dates of the jobs are random variables following a common probability distribution. The objective is to minimize the expectation of a weighted combination of the earliness penalty, the tardiness penalty, and the flow-time penalty. One of our main results is that an optimal sequence for the problem must be V-shaped with respect to the mean processing times. Other characterizations of the optimal solution are also established. Two algorithms are proposed, which can generate optimal or near-optimal solutions in pseudopolynomial time. The proposed algorithms are also extended to problems where processing times do not satisfy the assumption in the model above, and are evaluated when processing times follow different probability distributions, including general normal (without the proportional relation between variances and means), uniform, Laplace, and exponential. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44, 531–557, 1997.     

Optimal timing schedules in earliness-tardiness single machine sequencing

The article deals with a single machine earliness-tardiness scheduling model where idle times are permitted in job processing. Based on a cluster concept we develop properties of the model that lead to a very fast algorithm to find an optimal timing schedule for a given sequence of jobs. The performance of this algorithm is tested on 480 randomly generated problems involving 100, 200, 400 and 500 jobs. It takes less than two seconds to solve a 500 job problem on a PC. © 1995 John Wiley & Sons, Inc.     

Single-machine scheduling of stochastic jobs subject to deterioration or delay

N jobs are available for processing by a single machine. Jobs make (stochastic) progress while being processed but deteriorate while awaiting processing. The pioneering work of Browne and Yechiali, who developed scheduling policies for such models, is extended (i) to incorporate a precedence relation on the job set, delimiting the class of admissible policies, and (ii) to preemptive scheduling models. For the latter, we demonstrate that under appropriate conditions there is an optimal policy which is nonpreemptive. This is also achieved for a class of preemptive models in which processing generates delays for waiting jobs. A single class of algorithms is shown to generate optimal policies for many of the problems considered. © 1992 John Wiley & Sons, Inc.     

Discrete stochastic optimization using variants of the stochastic ruler method

We present two random search methods for solving discrete stochastic optimization problems. Both of these methods are variants of the stochastic ruler algorithm. They differ from our earlier modification of the stochastic ruler algorithm in that they use different approaches for estimating the optimal solution. Our new methods are guaranteed to converge almost surely to the set of global optimal solutions under mild conditions. We discuss under what conditions these new methods are expected to converge faster than the modified stochastic ruler algorithm. We also discuss how these methods can be used for solving discrete optimization problems when the values of the objective function are estimated using either transient or steady‐state simulation. Finally, we present numerical results that compare the performance of our new methods with that of the modified stochastic ruler algorithm when applied to solve buffer allocation problems. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

基于蜂群算法的战时毁伤装备维修任务调度研究

现代战争中,装备的战损率大大提高,装备维修任务十分繁重,如何在最短的时间内将损坏的装备修复好,是装备维修决策的重要内容之一[1]。鉴于蜂群算法在任务调度特别是动态随机任务调度中的优势,将蜂群算法引入装备维修任务调度研究,并进行了仿真实验,通过测试案例的仿真结果表明,蜂群算法对于动态随机任务调度具有很强的优势。最后对蜂群算法中的各参数对于仿真结果的影响进行了分析。     

Machine scheduling with an availability constraint and job delivery coordination

In this paper we study the scheduling problem that considers both production and job delivery at the same time with machine availability considerations. Only one vehicle is available to deliver jobs in a fixed transportation time to a distribution center. The vehicle can load at most K jobs as a delivery batch in one shipment due to the vehicle capacity constraint. The objective is to minimize the arrival time of the last delivery batch to the distribution center. Since machines may not always be available over the production period in real life due to preventive maintenance, we incorporate machine availability into the models. Three scenarios of the problem are studied. For the problem in which the jobs are processed on a single machine and the jobs interrupted by the unavailable machine interval are resumable, we provide a polynomial algorithm to solve the problem optimally. For the problem in which the jobs are processed on a single machine and the interrupted jobs are nonresumable, we first show that the problem is NP‐hard. We then propose a heuristic with a worst‐case error bound of 1/2 and show that the bound is tight. For the problem in which the jobs are processed on either one of two parallel machines, where only one machine has an unavailable interval and the interrupted jobs are resumable, we propose a heuristic with a worst‐case error bound of 2/3. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Scheduling jobs with random processing times on a single machine subject to stochastic breakdowns to minimize early-tardy penalties

We examine the problem of scheduling n jobs with a common due date on a single machine. The processing time of each job is a random variable, which follows an arbitrary distribution with a known mean and a known variance. The machine is not reliable; it is subject to stochastic breakdowns. The objective is to minimize the expected sum of squared deviations of job completion times from the due date. Two versions of the problem are addressed. In the first one the due date is a given constant, whereas in the second one the due date is a decision variable. In each case, a general form of the deterministic equivalent of the stochastic scheduling problem is obtained when the counting process related to the machine uptime distribution is a generalized Poisson process. A sufficient condition is derived under which optimal sequences are V-shaped with respect to mean processing times. Other characterizations of optimal solutions are also established. Based on the optimality properties, algorithms with pseudopolynomial time complexity are proposed to solve both versions of the problem. © 1996 John Wiley & Sons, Inc.     

Stochastic scheduling subject to machine breakdowns: The preemptive‐repeat model with discounted reward and other criteria

We consider the problem of scheduling a set of jobs on a single machine subject to random breakdowns. We focus on the preemptive‐repeat model, which addresses the situation where, if a machine breaks down during the processing of a job, the work done on the job prior to the breakdown is lost and the job will have to be started from the beginning again when the machine resumes its work. We allow that (i) the uptimes and downtimes of the machine follow general probability distributions, (ii) the breakdown process of the machine depends upon the job being processed, (iii) the processing times of the jobs are random variables following arbitrary distributions, and (iv) after a breakdown, the processing time of a job may either remain a same but unknown amount, or be resampled according to its probability distribution. We first derive the optimal policy for a class of problems under the criterion to maximize the expected discounted reward earned from completing all jobs. The result is then applied to further obtain the optimal policies for other due date‐related criteria. We also discuss a method to compute the moments and probability distributions of job completion times by using their Laplace transforms, which can convert a general stochastic scheduling problem to its deterministic equivalent. The weighted squared flowtime problem and the maintenance checkup and repair problem are analyzed as applications. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

A generalized Johnson's rule for stochastic assembly systems

Johnson's rule gives the optimal order to schedule a set of jobs through a two-machine flow shop with deterministic processing times. We extend Johnson's rule to fork-join systems with random processing times. Most of the system may be an arbitrary network. We give conditions under which Johnson's rule is optimal in the stochastic sense, in the increasing convex sense, and in expected value. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 211–220, 1997     

随机快速光滑二阶滑模末制导律设计

针对目标随机机动、惯性延迟、参数变化等因素降低导弹末制导精度的问题,提出新型随机快速光滑二阶滑模控制方法。将目标机动简化为零均值高斯白噪声过程,制导系统成为带加性噪声随机不确定非线性系统。考虑到该系统不存在平衡点,提出有限时间二阶均方实用收敛概念,并基于此证明了所设计控制律的收敛特性。根据直接命中条件设计滑模面,得到随机快速光滑二阶滑模制导律。在尾追和迎头两种态势下,将该新型制导律与扩展比例导引、一般滑模制导律及确定性光滑二阶滑模制导律进行仿真比较,验证了该方法的正确性和有效性。     

A note on allocating jobs to two machines

A recent article in this journal by Mehta, Chandrasekaran, and Emmons [1] described a dynamic programming algorithm for assigning jobs to two identical parallel processors in a way that minimizes the average delay of these jobs. Their problem has a constraint on the sequence of the jobs such that any group of jobs assigned to a processor must be processed in the order of the sequence. This note has two purposes. First, we wish to point out a relationship between this work and some prior work [2]. Second, we wish to point out that Mehta, Chandrasekaran, and Emmons formulation, slightly generalized, can be used to find the optimum assignment of jobs to two machines in a more general class of problems than they considered including a subclass in which the jobs are not constrained to be processed in a given sequence.     

An index policy for a stochastic scheduling model with improving/deteriorating jobs

We consider stochastic scheduling models which have the natural character that jobs improve while being processed, but deteriorate (and may possibly leave the system altogether) while processing is diverted elsewhere. Such restless bandit problems are shown to be indexable in the sense of Whittle. A numerical study which elucidates the strong performance of the resulting index policy is complemented by a theoretical study which demonstrates the optimality of the index policy under given conditions and which develops performance guarantees for the index heuristic more generally. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 706–721, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10036     

Scheduling jobs that are subject to failure propagation

A single machine is available to process a collection of stochastic tasks. Processing is interrupted when the machine breaks down. We introduce a new model of breakdowns that more realistically incorporates the effects that job processing may have on the machine. This failure propagation model is equivalent to a Bayesian formulation in which learning about breakdown rates occurs as jobs evolve. Optimal scheduling policies are described in terms of Gittins indices and these indices are characterised in two special cases. For example, we obtain conditions which ensure that an optimal policy will only preempt a job's processing either at its completion or at a machine breakdown. We also bound the value lost by simplistic modelling which ignores the learning phenomenon. © 1996 John Wiley & Sons, Inc.     

Due‐window assignment with unit processing‐time jobs

In due‐window assignment problems, jobs completed within a designated time interval are regarded as being on time, whereas early and tardy jobs are penalized. The objective is to determine the location and size of the due‐window, as well as the job schedule. We address a common due‐window assignment problem on parallel identical machines with unit processing time jobs. We show that the number of candidate values for the optimal due‐window starting time and for the optimal due‐window completion time are bounded by 2. We also prove that the starting time of the first job on each of the machines is either 0 or 1, thus introducing a fairly simple, constant‐time solution for the problem. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

Minimizing makespan on a single batch processing machine with nonidentical job sizes

We deal with the problem of minimizing makespan on a single batch processing machine. In this problem, each job has both processing time and size (capacity requirement). The batch processing machine can process a number of jobs simultaneously as long as the total size of these jobs being processed does not exceed the machine capacity. The processing time of a batch is just the processing time of the longest job in the batch. An approximation algorithm with worst‐case ratio 3/2 is given for the version where the processing times of large jobs (with sizes greater than 1/2) are not less than those of small jobs (with sizes not greater than 1/2). This result is the best possible unless P = NP. For the general case, we propose an approximation algorithm with worst‐case ratio 7/4. A number of heuristics by Uzosy are also analyzed and compared. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 226–240, 2001     

Minimizing flow time on a single machine with job classes and setup times

We examine the static sequencing problem of ordering the processing of jobs on a single machine so as to minimize the average weighted flow time. It is assumed that all jobs have zero ready times, and that the jobs are grouped into classes with the property that setup tasks are only required when processing switches from jobs of one class to jobs of another class. The time required for each setup task is given by the sum of a setdown time from the previous class and a setup time for the new class. We show that an algorithm presented in the literature for solving a special case of this problem gives suboptimal solutions. A number of properties of the optimal solution are derived, and their use in algorithms is evaluated. Computational results are presented for both a branch-and-bound procedure and a simpler depth-first search.     

On‐line algorithms for minimizing makespan on batch processing machines

We consider problem of scheduling jobs on‐line on batch processing machines with dynamic job arrivals to minimize makespan. A batch machine can handle up to B jobs simultaneously. The jobs that are processed together from a batch, and all jobs in a batch start and complete at the same time. The processing time of a batch is given by the longest processing time of any job in the batch. Each job becomes available at its arrival time, which is unknown in advance, and its processing time becomes known upon its arrival. In the first part of this paper, we address the single batch processing machine scheduling problem. First we deal with two variants: the unbounded model where B is sufficiently large and the bounded model where jobs have two distinct arrival times. For both variants, we provide on‐line algorithms with worst‐case ratio (the inverse of the Golden ratio) and prove that these results are the best possible. Furthermore, we generalize our algorithms to the general case and show a worst‐case ratio of 2. We then consider the unbounded case for parallel batch processing machine scheduling. Lower bound are given, and two on‐line algorithms are presented. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 241–258, 2001     

A queuing model for due date control in a multiserver repair shop

This paper deals with a repair shop with multiple parallel servers, which has to carry out planned overhauls. Each overhaul consists of a large number of maintenance jobs. The overhaul process is interrupted by randomly arriving emergency jobs. To control the delivery performance of the overhauls, knowledge about the overhaul makespan distribution should be available. Using a 2‐dimensional Markov model, we derive the first and second moment of the overhaul makespan analytically for the case that the repair times of all overhaul jobs are identically and exponentially distributed. For the case of nonidentical repair time distributions, an approximation is presented. Simulation shows that the makespan distribution fitted on these moments gives an excellent approximation. © John Wiley & Sons, Inc. Naval Research Logistics 48: 281–282, 2001     

随机 Petri 网的瓶颈及其最大处理能力

介绍了随机Ｐｅｔｒｉ网瓶颈的概念,给出了一般随机Ｐｅｔｒｉ网最大处理能力的数学模型,针对一类特殊结构的随机Ｐｅｔｒｉ网,导出了分析其最大处理能力和瓶颈的快速算法,最后给出了一个防空决策组织信息处理瓶颈与处理能力分析的实例。