Scheduling patients in an ambulatory surgical center

This paper presents a deterministic approach to schedule patients in an ambulatory surgical center (ASC) such that the number of postanesthesia care unit nurses at the center is minimized. We formulate the patient scheduling problem as new variants of the no‐wait, two‐stage process shop scheduling problem and present computational complexity results for the new scheduling models. Also, we develop a tabu search‐based heuristic algorithm to solve the patient scheduling problem. Our algorithm is shown to be very effective in finding near optimal schedules on a set of real data from a university hospital's ASC. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

Two-agent scheduling with linear resource-dependent processing times

This paper considers a two-agent scheduling problem with linear resource-dependent processing times, in which each agent has a set of jobs that compete with that of the other agent for the use of a common processing machine, and each agent aims to minimize the weighted number of its tardy jobs. To meet the due date requirements of the jobs of the two agents, additional amounts of a common resource, which may be in discrete or continuous quantities, can be allocated to the processing of the jobs to compress their processing durations. The actual processing time of a job is a linear function of the amount of the resource allocated to it. The objective is to determine the optimal job sequence and resource allocation strategy so as to minimize the weighted number of tardy jobs of one agent, while keeping the weighted number of tardy jobs of the other agent, and the total resource consumption cost within their respective predetermined limits. It is shown that the problem is -hard in the ordinary sense, and there does not exist a polynomial-time approximation algorithm with performance ratio unless ; however it admits a relaxed fully polynomial time approximation scheme. A proximal bundle algorithm based on Lagrangian relaxation is also presented to solve the problem approximately. To speed up convergence and produce sharp bounds, enhancement strategies including the design of a Tabu search algorithm and integration of a Lagrangian recovery heuristic into the algorithm are devised. Extensive numerical studies are conducted to assess the effectiveness and efficiency of the proposed algorithms.     

Using real options analysis to value reoptimization options in the shifting bottleneck heuristic

The reoptimization procedure within the shifting bottleneck (SB) involves reevaluation of all previously scheduled toolgroup subproblems at each iteration of the SB heuristic. A real options analysis (ROA) model is developed to value the option to reoptimize in the SB heuristic, such that reoptimization only occurs when it is most likely to lead to a schedule with a lower objective function. To date, all ROA models have sought to value options financially (i.e., in terms of monetary value). The ROA model developed in this paper is completely original in that it has absolutely no monetary basis. The ROA methodologies presented are shown to greatly outperform both full and no reoptimization approaches with respect to both computation time and total weighted tardiness. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

Priority and dynamic scheduling in a make‐to‐stock queue with hyperexponential demand

We consider the scheduling problem in a make‐to‐stock queue with two demand classes that can be differentiated based on their variability. One class experiences Poisson arrivals and the other class experiences hyperexponential renewal arrivals. We provide an exact analysis of the case where the demand class with higher variability is given non‐preemptive priority. The results are then used to compare the inventory cost performance of three scheduling disciplines, first‐come first‐serve and priority to either class. We then build on an existing dynamic scheduling heuristic to propose a modification that works well for our system. Extensions of the heuristic to more than two classes and to the case where demand state is known are also discussed. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.     

A branch‐and‐cut algorithm for the quay crane scheduling problem in a container terminal

The quay crane scheduling problem consists of determining a sequence of unloading and loading movements for cranes assigned to a vessel in order to minimize the vessel completion time as well as the crane idle times. Idle times originate from interferences between cranes since these roll on the same rails and a minimum safety distance must be maintained between them. The productivity of container terminals is often measured in terms of the time necessary to load and unload vessels by quay cranes, which are the most important and expensive equipment used in ports. We formulate the quay crane scheduling problem as a vehicle routing problem with side constraints, including precedence relationships between vertices. For small size instances our formulation can be solved by CPLEX. For larger ones we have developed a branch‐and‐cut algorithm incorporating several families of valid inequalities, which exploit the precedence constraints between vertices. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

高速并行Gardner算法设计与实现

随着空间探测任务逐步增加、空间信道频谱资源日趋紧张,传统Gardner定时同步算法已经无法满足高速数传系统高通量、高可靠性的需求。为了提高Gardner定时同步算法的吞吐率并增大可纠正误差范围,提出一种高速并行Gardner算法。为了保证插值精度同时减少乘法器消耗,设计了一种并行分段抛物线插值滤波器;为了便于并行流水线设计和最佳采样点选取,构建了计数模块和定时缓存调整模块;为了提高等价吞吐率,重构了流水线并行环路滤波器结构和并行数控振荡器结构。结果表明,该算法等价吞吐率可达1 739.13 Msps,数字信号处理器资源消耗可减少44%,可纠正2×10^-3的定时误差。     

A bicriterion approach to common flow allowances due window assignment and scheduling with controllable processing times

We investigate a single‐machine scheduling problem for which both the job processing times and due windows are decision variables to be determined by the decision maker. The job processing times are controllable as a linear or convex function of the amount of a common continuously divisible resource allocated to the jobs, where the resource allocated to the jobs can be used in discrete or continuous quantities. We use the common flow allowances due window assignment method to assign due windows to the jobs. We consider two performance criteria: (i) the total weighted number of early and tardy jobs plus the weighted due window assignment cost, and (ii) the resource consumption cost. For each resource consumption function, the objective is to minimize the first criterion, while keeping the value of the second criterion no greater than a given limit. We analyze the computational complexity, devise pseudo‐polynomial dynamic programming solution algorithms, and provide fully polynomial‐time approximation schemes and an enhanced volume algorithm to find high‐quality solutions quickly for the considered problems. We conduct extensive numerical studies to assess the performance of the algorithms. The computational results show that the proposed algorithms are very efficient in finding optimal or near‐optimal solutions. © 2017 Wiley Periodicals, Inc. Naval Research Logistics, 64: 41–63, 2017     

Determining optimal sizes of bounded batches with rejection via quadratic min‐cost flow

In this article, we consider a single machine scheduling problem, in which identical jobs are split into batches of bounded sizes. For each batch, it is allowed to produce less jobs than a given upper bound, that is, some jobs in a batch can be rejected, in which case a penalty is paid for each rejected job. The objective function is the sum of several components, including the sum of the completion times, total delivery cost, and total rejection cost. We reduce this problem to a min‐cost flow problem with a convex quadratic function and adapt Tamir's algorithm for its solution. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 217–224, 2017     

基于Pareto排序法的战时装备维修任务多目标调度方法

针对战时武器装备维修保障问题,建立了包括最大完工时间、延迟时间和维修单元负荷在内的多目标优化调度模型。为提高解的多样性和收敛性,构建了一种基于Pareto排序法和小生境技术相结合的遗传算法用于模型求解,引入Pareto排序和拥挤距离进行适应度计算,通过混沌系统随机生成权重系数,并使用小生境技术改进选择方式。通过实例验证表明,该方法能够有效地解决装备维修多目标调度问题。     

军事云环境下基于动态博弈的资源调度方法

针对军事云环境下联合指挥资源调度优化问题,综合考虑了多部队任务需求,多目标优化,多部队资源竞争等约束条件,运用动态博弈理论及方法,建立并提出了一种基于完全信息扩展博弈的资源调度模型及方法,提高了资源调度效率,兼顾了多部队利益,增加了联合作战效益。最后通过实验验证了其有效性及准确性。