释放时间和工期同序的单机连续型批调度问题

本文研究的连续型批处理机调度问题, 是在钢铁工业管坯的加热过程中提出来的. 工件带有释放时间和工期, 工件进入和离开机器是按周期依次进行的. 本文针对单机连续型批调度问题中工件释放时间和工期同序的情况, 分析了极小化最大拖期和拖期工件数等问题的计算复杂性, 证明了两类问题都是强NP-难的. 对于工件的释放时间和加工时间、工期都同序的特殊情况, 分别给出了能够获得对应问题的最优解的多项式算法.     

Common due date assignment and scheduling with a rate-modifying activity to minimize the due date,earliness, tardiness,holding, and batch delivery cost

e consider a single-machine batch delivery scheduling and common due date assignment problem. In addition to making decisions on sequencing the jobs, determining the common due date, and scheduling job delivery, we consider the option of performing a rate-modifying activity on the machine. The processing time of a job scheduled after the rate-modifying activity decreases depending on a job-dependent factor. Finished jobs are delivered in batches. There is no capacity limit on each delivery batch, and the cost per batch delivery is fixed and independent of the number of jobs in the batch. The objective is to find a common due date for all the jobs, a location of the rate-modifying activity, and a delivery date for each job to minimize the sum of earliness, tardiness, holding, due date, and delivery cost. We provide some properties of the optimal schedule for the problem and present polynomial algorithms for some special cases.     

Scheduling a Single Server in a Two-machine Flow Shop

We study the problem of scheduling a single server that processes n jobs in a two-machine flow shop environment. A machine dependent setup time is needed whenever the server switches from one machine to the other. The problem with a given job sequence is shown to be reducible to a single machine batching problem. This result enables several cases of the server scheduling problem to be solved in O(n log n) by known algorithms, namely, finding a schedule feasible with respect to a given set of deadlines, minimizing the maximum lateness and, if the job processing times are agreeable, minimizing the total completion time. Minimizing the total weighted completion time is shown to be NP-hard in the strong sense. Two pseudopolynomial dynamic programming algorithms are presented for minimizing the weighted number of late jobs. Minimizing the number of late jobs is proved to be NP-hard even if setup times are equal and there are two distinct due dates. This problem is solved in O(n ³) time when all job processing times on the first machine are equal, and it is solved in O(n ⁴) time when all processing times on the second machine are equal. Received November 20, 2001; revised October 18, 2002 Published online: January 16, 2003     

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

In this paper we consider single machine SLK due date assignment scheduling problem with a rate-modifying activity. In this model, the machine has a rate-modifying activity that can change the processing rate of machine under consideration. Hence the actual processing times of jobs vary depending on whether the job is scheduled before or after the rate-modifying activity. We need to make a decision on when to schedule the rate-modifying activity, the optimal common flow allowance and the sequence of jobs to minimize total earliness, tardiness and common flow allowance cost. We introduce an efficient (polynomial time) solution for this problem.     

Real-Time Scheduling with a Budget

Suppose that we are given a set of jobs, where each job has a processing time, a non-negative weight, and a set of possible time intervals in which it can be processed. In addition, each job has a processing cost. Our goal is to schedule a feasible subset of the jobs on a single machine, such that the total weight is maximized, and the cost of the schedule is within a given budget. We refer to this problem as budgeted real-time scheduling (BRS). Indeed, the special case where the budget is unbounded is the well-known real-time scheduling problem. The second problem that we consider is budgeted real-time scheduling with overlaps (BRSO), in which several jobs may be processed simultaneously, and the goal is to maximize the time in which the machine is utilized. Our two variants of this real-time scheduling problem have important applications, in vehicle scheduling, linear combinatorial auctions, and Quality-of-Service management for Internet connections. These problems are the focus of this paper. Both BRS and BRSO are strongly NP-hard, even with unbounded budget. Our main results are (2 + ε)-approximation algorithms for these problems. This ratio coincides with the best known approximation factor for the (unbudgeted) real-time scheduling problem, and is slightly weaker than the best known approximation factor of e/(e - 1) for the (unbudgeted) real-time scheduling with overlaps, presented in this paper. We show that better ratios (or simpler approximation algorithms) can be derived for some special cases, including instances with unit-costs and the budgeted job interval selection problem (JISP). Budgeted JISP is shown to be APX-hard even when overlaps are allowed and with unbounded budget. Finally, our results can be extended to instances with multiple machines.     

Parallel machine earliness and tardiness scheduling with proportional weights

In this paper we study the problem of scheduling n jobs with a common due date and proportional early and tardy penalties on m identical parallel machines. We show that the problem is NP-hard and propose a dynamic programming algorithm to solve it. We also propose two heuristics to tackle the problem and analyze their worst-case error bounds.Scope and purposeScheduling problems to minimize the total weighted earliness and tardiness (WET) arise in Just-in-time manufacturing systems, where one of the objectives is to complete each job as close to its due date as possible. The earliness and tardiness weights of a job in WET tend to increase with the value of the job. Because processing time is often a good surrogate for the value of a job, it is reasonable to consider weights that are proportional to job processing times. In this paper we study the parallel identical machine WET problem with proportional weights. We propose both exact and approximation algorithms to tackle the problem.     

A Stronger Complexity Result for the Single Machine Multi-Operation Jobs Scheduling Problem to Minimize the Number of Tardy Jobs

We consider the single machine multi-operation jobs scheduling problem to minimize the number of tardy jobs. Each job consists of several operations that belong to different families. In a schedule, each family of job operations may be processed in batches with each batch incurring a setup time. A job completes when all of its operations have been processed. The objective is to minimize the number of tardy jobs. In the literature, this problem has been proved to be strongly NP-hard for arbitrary due-dates. We show in this paper that the problem remains strongly NP-hard even when the due-dates are common and all jobs have the same processing time.     

Single machine due date assignment scheduling problem with customer service level in fuzzy environment

Due date assignment scheduling problems with deterministic and stochastic parameters have been studied extensively in recent years. In this paper, we consider a single machine due date assignment scheduling problem with uncertain processing times and general precedence constraint among the jobs. The processing times of the jobs are assumed to be fuzzy numbers. We first propose an optimal polynomial time algorithm for the problem without precedence constraints among jobs. Then, we show that if general precedence constraint is involved, the problem is NP-hard. Finally, we show that if the precedence constraint is a tree or a collection of trees, the problem is still polynomially solvable.     

Strong NP-hardness of the single machine multi-operation jobs total completion time scheduling problem

We consider the single machine multi-operation jobs total completion time scheduling problem. Each job consists of several operations that belong to different families. In a schedule, each family of job operations may be processed in batches with each batch incurring a set-up time. A job completes when all of its operations have been processed. The objective is to minimize the sum of the job completion times. In the literature, the computational complexity of this problem is posed as open. We show that the problem is strongly NP-hard even when the set-up times are common and the processing time of each operation is 0 or 1.     

Minimizing Mean Completion Time in a Batch Processing System

We consider batch processing jobs to minimize the mean completion time. A batch processing machine can handle up to $B$ jobs simultaneously. Each job is represented by an arrival time and a processing time. Jobs processed in a batch have the same completion time, i.e., their common starting time plus the processing time of their longest job. For batch processing, non-preemptive scheduling is usually required and we discuss this case. The batch processing problem reduces to the ordinary uniprocessor system scheduling problem if $B=1$. We focus on the other extreme case $B=+\infty$. Even for this seemingly simple extreme case, we are able to show that the problem is NP-hard for the weighted version. In addition, we establish a polynomial time algorithm for a special case when there are only a constant number of job processing times. Finally, we give a polynomial time approximation scheme for the general case.     

Scheduling of inventory releasing jobs to minimize a regular objective function of delivery times

In this note we provide new complexity and algorithmic results for scheduling inventory releasing jobs, a new class of single machine scheduling problems proposed recently by Boysen et al. We focus on tardiness related criteria, while known results are concerned with inventory levels between fixed delivery points. Our interest is motivated by the fact that deciding whether a feasible schedule exists is NP-hard in the strong sense, provided that all delivery deadlines are fixed, and there are no restrictions on the amount of products released by the jobs, nor on the job processing times. We will establish NP-hardness results, or provide polynomial or pseudo-polynomial time algorithms for various special cases, and describe a fully polynomial approximation scheme for one of the variants with the maximum tardiness criterion.     

Single machine due-date scheduling of jobs with decreasing start-time dependent processing times

We study the problem of scheduling jobs whose processing times are decreasing functions of their starting times. We consider the case of a single machine and a common decreasing rate for the processing times. The problem is to determine an optimal combination of the due date and schedule so as to minimize the sum of due date, earliness and tardiness penalties. We give an O(n log n) time algorithm to solve this problem.     

Scheduling with due date assignment under special conditions on job processing

We review the results on scheduling with due date assignment under such conditions on job processing as given precedence constraints, maintenance activity or various scenarios of processing time changing. The due date assignment and scheduling problems arise in production planning when the management is faced with setting realistic due dates for a number of jobs. Most research on scheduling with due date assignment is focused on optimal sequencing of independent jobs. However, it is often found in practice that some products are manufactured in a certain order implied, for example, by technological, marketing or assembly requirements and this can be modeled by imposing precedence constraints on the set of jobs. In classical deterministic scheduling models, the processing conditions, including job processing times, are usually viewed as given constants. In many real-life situations, however, the processing conditions may vary over time, thereby affecting actual durations of jobs. In the models with controllable processing times, the scheduler can speed up job execution times by allocating some additional resources to the jobs. In the models with deterioration or learning, the actual processing time can depend either on the position or on the start time of a job in the schedule. In scheduling with deterioration, the later a job starts, the longer it takes to process, while in scheduling with learning, the actual processing time of a job gets shorter, provided that the job is scheduled later. We consider also scheduling models with optional maintenance activity. In manufacturing processing, production scheduling with preventive maintenance planning is one of the most significant methods in preventing the machinery from failure or wear.     

带有线性恶化工件和释放时间的两个代理单机调度问题

研究了带有简单线性恶化工件和释放时间的两个代理单机调度问题. 所有工件在一台机器上加工, 每个代理有各自依赖于自己工件的优化目标. 针对工件释放时间相同与不同两种情况, 研究了有约束的优化模型, 即找到调度最小化一个代理的目标函数而使得另一个代理的目标函数不超过一个给定的上界. 当工件具有相同的释放时间, 我们主要考虑的目标函数有: 总加权完工时间和总加权拖期工件数. 当工件具有不同释放时间, 我们考虑的目标函数有: 最大完工时间、总完工时间以及拖期工件数. 对于每一个问题, 我们分析了问题的计算复杂性. 此外, 对于NP难问题的一些特殊情况本文分析了最优解性质, 基于这些性质给出了最优算法.     

A dominant class of schedules for malleable jobs in the problem to minimize the total weighted completion time

This paper is about scheduling parallel jobs, i.e. which can be executed on more than one machine at the same time. Malleable jobs is a special class of parallel jobs. The number of machines a malleable job is executed on may change during its execution.In this work, we consider the NP-hard problem of scheduling malleable jobs to minimize the total weighted completion time (or mean weighted flow time). For this problem, we introduce the class of “ascending” schedules in which, for each job, the number of machines assigned to it cannot decrease over time while this job is being processed.We prove that, under a natural assumption on the processing time functions of jobs, the set of ascending schedules is dominant for the problem. This result can be used to reduce the search space while looking for an optimal solution.     

Approximation algorithms for minimizing the total weighted number of late jobs with late deliveries in two-level supply chains

We study a supply chain scheduling problem in which n jobs have to be scheduled on a single machine and delivered to m customers in batches. Each job has a due date, a processing time and a lateness penalty (weight). To save batch-delivery costs, several jobs for the same customer can be delivered together in a batch, including late jobs. The completion time of each job in the same batch coincides with the batch completion time. A batch setup time has to be added before processing the first job in each batch. The objective is to find a schedule which minimizes the sum of the weighted number of late jobs and the delivery costs. We present a pseudo-polynomial algorithm for a restricted case, where late jobs are delivered separately, and show that it becomes polynomial for the special cases when jobs have equal weights and equal delivery costs or equal processing times and equal setup times. We convert the algorithm into an FPTAS and prove that the solution produced by it is near-optimal for the original general problem by performing a parametric analysis of its performance ratio.     

Bounded parallel-batching scheduling with two competing agents

We consider a scheduling problem in which two agents, each with a set of non-preemptive jobs, compete to perform their jobs on a common bounded parallel-batching machine. Each of the agents wants to minimize an objective function that depends on the completion times of its own jobs. The goal is to schedule the jobs such that the overall schedule performs well with respect to the objective functions of both agents. We focus on minimizing the makespan or the total completion time of one agent, subject to an upper bound on the makespan of the other agent. We distinguish two categories of batch processing according to the compatibility of the agents. In the case where the agents are incompatible, their jobs cannot be processed in the same batch, whereas all the jobs can be processed in the same batch when the agents are compatible. We show that the makespan problem can be solved in polynomial time for the incompatible case and is NP-hard in the ordinary sense for the compatible case. Furthermore, we show that the latter admits a fully polynomial-time approximation scheme. We prove that the total completion time problem is NP-hard and is polynomially solvable for the incompatible case with a fixed number of job types.     

Search heuristics for a parallel machine scheduling problem with ready times and due dates

We consider a problem of scheduling orders on identical parallel machines. An order can be released after a given ready time and must be completed before its due date. An order is split into multiple jobs (batches) and a job is processed on one of the parallel machines. The objective of the scheduling problem is to minimize the holding costs of orders including work-in-process as well as finished job inventories. We suggest two local search heuristics, simulated annealing and taboo search algorithms, for the problem. Performance of the suggested algorithms is tested through computational experiments on randomly generated test problems.     

Makespan minimization flowshop with position dependent job processing times—computational complexity and solution algorithms

This paper considers flowshop scheduling problems where job processing times are described by position dependent functions, i.e., dependent on the number of processed jobs, that model learning or aging effects. We prove that the two-machine flowshop problem to minimize the maximum completion time (makespan) is NP-hard if job processing times are described by non-decreasing position dependent functions (aging effect) on at least one machine and strongly NP-hard if job processing times are varying on both machines. Furthermore, we construct fast NEH, tabu search with a fast neighborhood search and simulated annealing algorithms that solve the problem with processing times described by arbitrary position dependent functions that model both learning and aging effects. The efficiency of the proposed methods is numerically analyzed.     

A study of the single-machine two-agent scheduling problem with release times

In many management situations, multiple agents compete on the usage of common processing resources. On the other hand, the importance of the ready times can be shown in Wafer fabrication with the presence of unequal ready times. It is sometimes advantageous to form a non-full batch, while in other situations it is a better strategy to wait for future job arrivals in order to increase the fullness of the batch. However, research on scheduling with two-agent and ready time simultaneously is relatively unexplored. This paper addresses a single-machine two-agent scheduling problem with ready times. The aim is to find an optimal schedule to minimize the total completion time of the jobs of the first agent with the restriction that total completion time is allowed an upper bound for the second agent. To the best of our knowledge, the problem under study has not been considered. Firstly, we show that the proposed problem is strongly NP-hard. Following that, we then develop a branch-and-bound, an ant colony, and four genetic algorithms for an optimal and near-optimal solution, respectively. In addition, the extensive computational experiments are also given.