Two‐machine flow shop no‐wait scheduling with a nonavailability interval

We study a two‐machine flow shop scheduling problem with no‐wait in process, in which one of the machines is not available during a specified time interval. We consider three scenarios of handing the operation affected by the nonavailability interval. Its processing may (i) start from scratch after the interval, or (ii) be resumed from the point of interruption, or (iii) be partially restarted after the interval. The objective is to minimize the makespan. We present an approximation algorithm that for all these scenarios delivers a worst‐case ratio of 3/2. For the second scenario, we offer a 4/3‐approximation algorithm. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

Crane scheduling with spatial constraints

In this work, we examine port crane scheduling with spatial and separation constraints. Although common to most port operations, these constraints have not been previously studied. We assume that cranes cannot cross, there is a minimum distance between cranes and jobs cannot be done simultaneously. The objective is to find a crane‐to‐job matching which maximizes throughput under these constraints. We provide dynamic programming algorithms, a probabilistic tabu search, and a squeaky wheel optimization heuristic for solution. Experiments show the heuristics perform well compared with optimal solutions obtained by CPLEX for small scale instances where a squeaky wheel optimization with local search approach gives good results within short times. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Single machine parallel batch scheduling subject to precedence constraints

We consider the single machine parallel batch scheduling problems to minimize makespan and total completion time, respectively, under precedence relations. The complexities of these two problems are reported as open in the literature. In this paper, we settle these open questions by showing that both problems are strongly NP‐hard, even when the precedence relations are chains. When the processing times of jobs are directly agreeable or inversely agreeable with the precedence relations, there is an O(n²) time algorithm to minimize the makespan. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

Production scheduling with history‐dependent setup times

We consider a parallel‐machine scheduling problem with jobs that require setups. The duration of a setup does not depend only on the job just completed but on a number of preceding jobs. These setup times are referred to as history‐dependent. Such a scheduling problem is often encountered in the food processing industry as well as in other process industries. In our model, we consider two types of setup times—a regular setup time and a major setup time that becomes necessary after several “hard‐to‐clean” jobs have been processed on the same machine. We consider multiple objectives, including facility utilization, flexibility, number of major setups, and tardiness. We solve several special cases assuming predetermined job sequences and propose strongly polynomial time algorithms to determine the optimal timing of the major setups for given job sequences. We also extend our analysis to develop pseudopolynomial time algorithms for cases with additional objectives, including the total weighted completion time, the total weighted tardiness, and the weighted number of tardy jobs. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

Balancing perfectly periodic service schedules: An application from recycling and waste management

A national recycling and waste management company provides periodic services to its customers from over 160 service centers. The services are performed periodically in units of weeks over a planning horizon. The number of truck‐hours allocated to this effort is determined by the maximum weekly workload during the planning horizon. Therefore, minimizing the maximum weekly workload results in minimum operating expenses. The perfectly periodic service scheduling (PPSS) problem is defined based on the practices of the company. It is shown that the PPSS problem is strongly NP‐hard. Attempts to solve large instances by using an integer programming formulation are unsuccessful. Therefore, greedy BestFit heuristics with three different sorting schemes are designed and tested for six real‐world PPSS instances and 80 randomly generated data files. The heuristics provide effective solutions that are within 2% of optimality on average. When the best found BestFit schedules are compared with the existing schedules, it is shown that operational costs are reduced by 18% on average. © 2012 Wiley Periodicals, Inc. Naval Research Logistics 59: 160–171, 2012     

Determining crane areas for balancing workload among interfering and noninterfering cranes

This article treats the problem of subdividing an area for storing containers such that the workload is evenly shared among the cranes operating the resulting subareas. We consider two crane sets: while noncrossing constraints between cranes of the same set need to be observed, cranes of different sets do not interfere. Such a problem setting is, for instance, relevant for scheduling the (un‐)loading of vessels by parallel quay cranes operating on opposing berths or in container yards with cross‐over cranes. We formalize the resulting optimization problem, prove computational complexity, and present exact and heuristic solution procedures. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

一种面向复杂地理空间栅格数据处理算法并行化的任务调度方法

随着并行计算技术的成熟,地理空间栅格数据处理算法的并行化研究成为新的热点。聚焦于处理流程包含多个计算步骤的复杂地理空间栅格数据处理算法,基于空间计算域理论,提出了一个随着算法处理流程而动态变化的任务调度方法。实验证明,该方法在算法流程的每一个计算步都会调整任务分组方案,因此相比于传统任务调度方法,任务调度的负载均衡效果更好,并行算法程序的运行时间更短。     

Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks

In a master surgery scheduling (MSS) problem, a hospital's operating room (OR) capacity is assigned to different medical specialties. This task is critical since the risk of assigning too much or too little OR time to a specialty is associated with overtime or deficit hours of the staff, deferral or delay of surgeries, and unsatisfied—or even endangered—patients. Most MSS approaches in the literature focus only on the OR while neglecting the impact on downstream units or reflect a simplified version of the real‐world situation. We present the first prediction model for the integrated OR scheduling problem based on machine learning. Our three‐step approach focuses on the intensive care unit (ICU) and reflects elective and urgent patients, inpatients and outpatients, and all possible paths through the hospital. We provide an empirical evaluation of our method with surgery data for Universitätsklinikum Augsburg, a German tertiary care hospital with 1700 beds. We show that our model outperforms a state‐of‐the‐art model by 43% in number of predicted beds. Our model can be used as supporting tool for hospital managers or incorporated in an optimization model. Eventually, we provide guidance to support hospital managers in scheduling surgeries more efficiently.     

批量独立可修件的备件需求预测仿真算法

利用Monte Carlo随机仿真技术,采用事件调度法的仿真策略对可修件的使用与维修过程进行仿真,给出了在给定备件和维修分队数量下批量独立可修件的备件保障概率仿真算法,并用一系列计算实例证明了算法的可行性和正确性,该算法能有效解决安装在不同装备上,具有不同已工作时间、不同故障间隔时间分布、不同修复时间分布的可修件的备件保障概率计算问题,为备件的科学储备提供了有力的决策依据。     

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