基于峰值负荷的签派员席位任务分配问题

本文研究的问题来源于航空公司运行控制中心的签派部门,签派员在放行航班时,既要保证航班的正点起飞,又要调节放行航班的工作劳动强度保证放行的质量,使航班在安全状态下运行。这里放行航班的工作劳动强度为单位时间内的工作时间,峰值负荷即劳动强度最大值。峰值负荷过高则工作紧张,进而推断该放行席位的任务分配不合理。文中将问题描述为任务有优先序的单机排序问题,每个任务都有一个到达时间（release time）、截止期限（deadline）和处理时间（procession time）,处理时间因任务的不同而不同,目标是在绝对不准延误完成任务前提下,使单位时间的峰值负荷最小。在使单位时间峰值负荷最小的目标下,本文提出了一个有效算法,并证明该算法下的任务安排是最优安排。     

一种求解时变条件下有宵禁限制最短路的算法

在组合优化过程中,往往需要获得从起点到终点之间的最短路.由于道路、天气、交通条件等因素的影响,使得网络具有很强的时变特性.同时,对于网络中的节点往往有宵禁的限制.对时变条件下有宵禁限制并有到达时间限制的最短路进行了研究,建立了软、硬宵禁限制下的数学模型,给出并证明了时变条件下获得有宵禁限制最短路的最优条件,并设计了求解的多项式算法,通过此算法可以获得时变条件下有宵禁限制的最短路.同时,算法和模型还考虑了不同的起点出发时间,使路径决策者可以根据自身的情况,选择合适的出发时间和路径.最后给出了一个应用算例,分析了宵禁对于获得的最短路的影响.     

第三方物流企业在JIT方式下的多商品分批调度问题

在有多个商品、小批量定货且交货期不同的情况下,如何将这种混合的离散定单任务分批次完成并确定每一批次的调运开始时间,是当前第三方物流企业提高运作效率迫切需要解决的实际问题.给出了一个不允许出现拖期且提前惩罚的分批次优化模型和优化规则及算法,算例的结果表明了方法的有效性.     

一种考虑动态时间窗的满载问题模型及算法

针对时间约束在满载问题中的复杂性,建立了一个考虑装载时间和次序的具有动态时间窗的满载车辆调度模型,并给出了一个基于动态构造原理的启发式算法。该模型和算法改进了以往满载问题中对时间窗的考虑,使得求解更具有实际派车意义,并且该算法通过参数调整,经过少量迭代即可快速求得最小化总成本的满意解。     

一种费用与时间相关的GERT模型的解析求解研究

本文探讨了项目活动时间与费用之间的函数关系,在假定活动费用与时间线性相关的条件下,依据矩母函数和梅森公式,推导出互斥型GERT模型的解析算法,并给出了该算法的一个应用实例。     

一种求解时变网络下多式联运最短路的算法

在运输过程中,往往不止有一种运输方式,可能同时有多种运输方式交叉,即可能多式联运的方式存在,不同的运输方式之间需要通过转运才可实现.同时,在运输过程中,成本、运输时间、风险等因素会随着时间的不同而变化.首先,将运输网络进行变形,然后给出了在时变网络条件下多式联运的最短路模型,设计了求解时变条件下多式联运的最短路的算法,利用此算法可以获得从起点到终点之间的最短路,并对算法的计算复杂性进行了分析.最后给出一个应用算例.     

供应链产品物流过程模型及时间瓶颈的识别

供应链时间瓶颈的识别是对供应链进行时间压缩的首要环节.本文首先根据供应链网状模型和无边界集成的供应链合作思想,将供应链中产品生产过程看作是一个物在供应链各环节的流动过程.在对这一物流过程进行分析的基础上建立了供应链产品的物流过程模型,并使用产品物流在供应链各环节流动的转移概率,结合价值工程的思想,提出了基于产品物流过程转移概率的供应链时间瓶颈识别方法,给出了供应链时间瓶颈识别与压缩的方法和流程,最后使用算例进行了仿真研究.     

自动化立体仓库中出入库任务顺序与出库位置选择集成优化研究

具有多个出口的自动化立体仓库系统是一种将存储和分拣相结合的新型仓储技术,其最典型的特征是在货架底层有很多个出库位置以供取货人员分拣。研究此系统中出入库任务排序与出口选择的集成优化问题,以最小化堆垛机完成所有任务的移动距离为目标,将此问题转化为一个混合整数规划模型。根据问题的特点设计了两阶段启发式算法求解此问题,数值结果表明设计的算法能在较短时间内给出近似最优解,同时与企业常用的先到先服务方法相比,该算法可以缩短超过20%的移动距离。     

目标函数为∑和max的双目标最短路问题:算法和复杂性

本文研究了一个双目标最短路问题。在该问题中,一个目标函数是∑形式,另一个目标函数是max形式。首先给出了一个时间复杂性为O(m²logn)的算法产生代表有效解集合。然后研究了∑和max的组合目标函数最短路问题,对动态问题和静态问题,分别给出了一个时间复杂性都为O(m²logn)的算法。最后在字典序最优解的意义下,本文给出了两个时间复杂性都为O(mlogn)的算法。     

时变随机网络下基于成功和风险的应急路径选择研究

现实生活中,当发生紧急事件时,应急中心需要对某地需要服务的紧急事件出车.由于交通管理、交通流量、天气变化等因素的影响,导致了路网中各个路段上的行驶时间可能是一个与出发时间相关的随机变量.通常,对于所发生紧急时间需要在一定的应急限制期内到达.由于路网的时变随机特性,使得所选择路径可能不能完全满足应急限制期的需求.首先,定义了时变随机网络下可行应急路径中不满足应急限制的风险和满足应急限制的成功.然后,分别考虑了成功和风险两个目标,建立了时变随机网络下多目标应急路径选择模型,并设计了求解时变随机网络下应急路径选择算法,讨论了算法的计算复杂性.最后,给出了一个应用算例,并与单独考虑成功所获得的应急路径进行了对比.     

Simulated annealing procedure for scheduling competing tasks in flexible manufacturing

This paper presents a simulated annealing SA procedure heuristic for the problem of scheduling N tasks on a machine equipped with an automatic tool changer to minimize the makespan time. The problem is first formulated as a symmetric travelling salesman problem TSP . A local search heuristic procedure is developed, then embedded into SA algorithm to enhance its performance. The implemented SA heuristic has the following features: an exponential acceptance function with non-monotonic cooling schedule, heuristic pre-processing, and a neighbourhood of changing the sequence of a small number of tasks and named the k-interchange procedure. The algorithm is compared with an exact solution method on a set of practical-sized problems. The proposed algorithm performed very well in terms of solution quality and computation time.     

Priority Rules for Multi‐Task Due‐Date Scheduling under Varying Processing Costs

We study the scheduling of multiple tasks under varying processing costs and derive a priority rule for optimal scheduling policies. Each task has a due date, and a non‐completion penalty cost is incurred if the task is not completely processed before its due date. We assume that the task arrival process is stochastic and the processing rate is capacitated. Our work is motivated by both traditional and emerging application domains, such as construction industry and freelance consulting industry. We establish the optimality of Shorter Slack time and Longer remaining Processing time (SSLP) principle that determines the priority among active tasks. Based on the derived structural properties, we also propose an effective cost‐balancing heuristic policy and demonstrate the efficacy of the proposed policy through extensive numerical experiments. We believe our results provide operators/managers valuable insights on how to devise effective service scheduling policies under varying costs.     

A nuclear medicine patient/instrument scheduling model

The problem of scheduling patients and clinical instruments for each physician-requested Nuclear Medicine study, in a clinical environment, is resolved through the development of a computerized heuristic model. The study schedule was determined by minimizing such factors as elapsed instrument time, instrument idle time, and maximizing instrument utilization. The heuristic scheduling procedure was developed and evaluated for scheduling thirteen different Nuclear Medicine study types on a daily basis. The analysis showed that this heuristic can be utilized to provide a good basic schedule for use in Clinical Nuclear Medicine.     

A new hybrid heuristic algorithm for the Precedence Constrained Production Scheduling Problem: A mining application

In this work we address the Precedence Constrained Production Scheduling Problem (PCPSP), the problem of scheduling tasks in such a way that total profit is maximized, while satisfying conditions such as precedence constraints among tasks and side constraints. A motivation for addressing this problem comes from open-pit mining industry, where the PCPSP seeks to maximize the net present value of an ore deposit by selecting the blocks (tasks) to extract, their extraction periods and their processing options, while satisfying constraints as precedences among blocks, limited availability of operational resources and maximum and/or minimum allowable concentrations of ore-grade or pollutants. Since real-world models have millions of blocks and constraints, the monolithic problem is computationally intractable. This article presents a hybrid heuristic algorithm that combines a rolling horizon decomposition with a block preselection procedure, allowing near-optimal solutions to be quickly determined. The proposed heuristic was tested on all the PCPSP instances of the MineLib library and has shown a significant improvement over the previous reported results. Moreover, a good feasible solution has been found for the instance W23, for which no solution has been previously reported.     

An Efficient Algorithm for Delay Buffer Minimization

A data flow machine is said to be synchronized if for any vertex u in the underlying data flow graph, all inputs to vertex u arrive at the same time. An unsynchronized data flow machine with an acyclic underlying data flow graph can be transformed into a synchronized system by adding unit delay buffers to the system. This synchronization process can increase pipelining and throughout. Since the addition of delay buffers introduces hardware and area costs, it is desirable to insert the minimum number of delay buffers to synchronize a given data flow machine. Due to important applications in computer design, various delay buffer minimization problems have been studied by many researchers. Several optimal algorithms and heuristic algorithms have been proposed for slightly different models. In this paper, we introduce the concept of extensions of a directed acyclic graph to generalize and formalize several delay buffer minimization problems studied in the literature and present a polynomial time algorithm for computing the minimum delay buffer synchronization of a given data flow machine. Examples are provided to illustrate our algorithm and to show that our algorithm requires fewer delay buffers than previously published optimal algorithms for various models.     

考虑恢复依赖的基础设施网络应急恢复决策

在大规模突发事件发生后,基础设施恢复任务之间会形成复杂的依赖关系,本文考虑了在此条件下基础设施系统的恢复设计与调度决策问题。基于网络流理论,以累积恢复效能最大化与成本最小化为目标,构建了涵盖阻塞依赖、选项依赖和效率依赖三类恢复依赖关系的集成恢复设计与调度决策混合整数规划模型,并且设计了求解模型的启发式算法。最后,以长沙市真实基础设施数据集为基础,构造了突发事件后的损毁场景实例,利用模型求解得出了受损基础设施的恢复设计与调度决策方案,并且分析了决策周期长度与工作组数量对累积恢复效能和成本的影响。结果表明：(1)该模型在突发事件后的基础设施恢复决策中具有应用可行性;(2)决策周期长度显著影响累积恢复效能,随着决策周期长度与恢复过程中各组件恢复耗时契合度的提升,累积恢复效能获得有效增长,并且,在决策周期长度的取值范围内,总成本存在最小值;(3)随工作组数量的增加,累积恢复效能呈增长趋势,增长率逐渐减小,同时,总成本呈减少趋势,减少率也逐渐减小。     

Sequencing and Scheduling Appointments with Potential Call‐In Patients

This paper studies appointment scheduling for a combination of routine patients who book well in advance and last‐minute patients who call for an appointment later that same day. We determine when these same‐day patients should be scheduled throughout the day, and how the prospect of their arrivals affects the appointment times of the routine patients. By formulating the problem as a stochastic linear program, we are able to incorporate random and heterogeneous service times and no‐show rates, ancillary physician tasks, and appointment delay costs for same‐day patients who prefer to see the doctor as early as possible. We find that the optimal patient sequence is quite sensitive to the no‐show probabilities and the expected number of same‐day patients. We also develop two simple heuristic solutions to this combinatorial sequencing problem.     

Analysis of the List Scheduling Algorithm for Precedence Constrained Parallel Tasks

Given P processors, and a set of precedence constrained parallel tasks with their processor requirements and execution times, the problem of scheduling precedence constrained parallel tasks on multiprocessors is to find a nonpreemptive schedule of the tasks on a multiprocessor with P processors, such that the schedule length is minimized. We show that for many heuristic choices of the initial priority list, the list scheduling algorithm has worst-case performance ratio P, which is unbounded as P gets large. However, it is also shown that when task sizes are bounded from above by a fraction of P, the list scheduling algorithm has finite worst-case performance ratio. In particular, we prove that if all tasks request for no more than qP processors, where 0 < q < 1, then the worst-case performance ratio of the list scheduling algorithm is no larger than

An exchange heuristic for resource constrained scheduling with consideration given to opportunities for parallel processing

This paper describes a heuristic which produces efficient makespans for resource-constrained scheduling problems with parallel processing capabilities. This heuristic was initially developed for the scheduling of army battalion training exercises. The original heuristic has also been successfully applied to solve problems in project scheduling with limited resources, generalized job shop scheduling, and resource-constrained scheduling. The exchange heuristic requires an initial feasible solution upon which it improves the makespan by efficiently and systematically shuffling activities while maintaining feasibility. The method has recently been modified twice, termed the intelligent version and naive version, respectively, such that its ability to reduce the initial makespan is enhanced. In this study     

Single facility scheduling with dual criteria: minimizing maximum tardiness subject to minimum number of tardy jobs

This paper considers the static single machine scheduling problem with the objective of minimizing the maximum tardiness of any job subject to the constraint that the total number of lardy jobs is minimum. Based on simple dominance conditions an o(n²) heuristic algorithm is proposed to find an approximate solution to this problem. The effectiveness of the proposed heuristic algorithm is empirically evaluated by solving a large number of problems and comparing them to the optimal solutions obtained through the branch and bound algorithm.