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。     

一类新型批处理机调度问题的理论分析

钢卷在冷轧生产中,为了改进其性能,需要在罩式炉进行退火,退火过程由加热、保温和降温三段组成,而这三段处理时间由于工艺上的要求不能归结为一个时间,这与传统批处理机调度有明显的差别.对新型批处理机的总加权完成时间最小化问题建立了非线性整数规划模型,开发了基于动态规划的启发式算法.通过理论分析,获得该算法的误差性能比为3.对于三段中的某一段板卷的处理时间相同的情况,证明了启发式算法的误差性能比是2,而且证明是紧界.对于三段中的某二段板卷的处理时间相同的情况,证明了启发式算法是最优算法.对启发式算法扩展到带有任意段的加工时间的一般情况进行了性能分析.     

救援物资跨区域调度双层规划模型——考虑幸存者感知满意度和风险可接受度

为减少幸存者的痛苦、提高灾害响应效率和降低各种损失,聚焦于跨区域救援物资反应性调度主从优化问题.综合考虑救援物资调度与分配网络中不确定供应-需求-行程时间、公平性分配原则、幸存者多层次异质性与风险可接受度限制、利益相关者的层级或纵向"府际"关系、需求可拆分、多灾点、多供应点和多运输模式等特征;构建了上层最小化加权行程时间总和,下层最大化加权感知满意度的双层整数规划模型.通过分析模型的特征,设计了以互补松弛条件为基础的原始-对偶算法对其进行求解.最后,以汶川地震为算例,验证了所提出模型与求解策略的可行性与有效性.     

平行机作业环境下的订单分配与排序

针对一类平行机作业环境下的订单分配与排序问题,从整体的角度协同优化供应链中的订单分配、生产调度和分批运输调度。以完成所有订单的总订货提前期与生产运输总成本的加权和最小化为目标,构建了问题的数学模型。将基于向量组编码结构的禁忌搜索算法与基于动态规划方法的启发式算法相结合,设计了一种混合优化算法以求解问题。对不同策略和不同算法进行比较,数据实验结果显示了订单分配与排序策略的优越性及所提算法的有效性。     

顾客时间窗变化的多车场车辆调度干扰管理模型研究

为解决物流配送过程中某顾客时间窗变化而导致的物流配送难以按照原计划实现的难题,运用干扰管理方法,针对多车场带时间窗的物流配送车辆路径问题,提出包含顾客配送时间总偏离度、配送总成本、新路径方案与初始路径计划偏差量、最长行驶时间违反总量4个因素的扰动程度度量方法;以顾客时间窗变化这类干扰事件发生时的问题状态为基础,以系统整体扰动最小化为目标,建立问题的目标规划数学模型;针对这一复杂模型,提出基于特定邻域结构及其寻优概率的变邻域搜索算法;最后在标准算例上使用变邻域搜索算法与其他文献算法进行比较分析,结果表明该变邻域搜索算法对于干扰事件发生后的车辆路线重排结果在系统整体扰动程度方面明显优于其他文献的算法.     

求解带时间窗的装-卸载问题的概率式禁忌搜索算法

本文介绍一个求解有时间窗的装载—卸载问题的概率式禁忌搜索算法。论文首先回顾了求解VRP、PDP问题的主要方法,介绍了搜索算法的基本概念,给出了基于概率式禁忌搜索的详细算法。初步实验表明新算法优于传统禁忌搜索算法。     

基于韧性城市视角的突发事件下分布式应急手术调度研究

针对城市突发事件下的应急手术问题,进行了基于韧性城市视角的分布式应急手术调度研究。首先,提出了韧性城市视角下同时考虑救援时间和患者恶化成本的调度目标;其次,结合应急手术中的疲劳阈值效应,截断学习效应和患者恶化成本等典型特点,以及救援医院的可重入层流手术特点,构建了分布式应急手术调度模型;然后,设计两阶段算法求解患者在医院间的分配,以及在医院内的手术排序与资源安排;最后,通过数值实验测试了四种启发式算法下带深度搜索策略的混合教与学优化算法（HTLBO-LS）的寻优性能,并通过仿真案例进一步探讨了不同算法的应用效果,以及在城市韧性视角下的医疗资源配置方案。研究结果为城市突发事件下分布式应急手术调度提供方法借鉴和决策参考。     

带工期指派的产品服务系统订单随机调度问题研究

针对由一个制造工厂和多个区域服务中心组成的服务型制造企业，研究了考虑生产时间和服务时间均具有随机性且工期可指派的产品服务系统（PSS）订单调度问题。首先以最小化订单提前、误工和工期指派费用的期望总额为目标构建问题的优化模型，然后分析目标函数近似值的最优性条件，据此提出加权最短平均生产时间排序规则，并结合该规则与插入邻域局部搜索设计了启发式算法对问题进行求解，最后通过数值仿真验证算法的可行性和有效性。研究表明，提前费用偏差对PSS订单调度与工期指派决策的影响很小，因此企业管理者无需准确估计库存费用也能制定出比较有效的PSS订单调度策略；而工期指派费用偏差对决策结果的影响非常大，因此企业管理者在决策时必须谨慎估计该项费用。     

电子商务下基于改进两阶段算法的有时间窗车辆调度优化

为满足电子商务下的物流配送需求,将传统车辆调度模型进行修改,将目标函数改为基于费用最小,在约束条件中增加时间约束、货物容积约束、车辆最大工作时间、多种车型、载重量限制和最大行驶距离等,以提高模型的适用性和通用性。由于有时间窗的车辆调度问题是NP难问题,采用改进两阶段算法进行求解。即第一阶段用模糊分层聚类法将客户群分成若干区域,在每个区域又用扫描算法分解成若干符合约束条件的小规模子集;第二个阶段对各个分组内客户点,就是一个个单独TSPTW模型的线路优化问题,因此,采用改进混合遗传算法进行优化求解,最后的算例仿真表明了算法的有效性和可行性。     

时间依赖型多配送中心带时间窗的开放式车辆路径问题研究

本文研究了时间依赖型多配送中心带时间窗的开放式车辆路径问题,基于道路通行情况,建立车辆行驶速度时间函数;考虑车辆载重、里程限制与客户点服务时间窗的约束,建立混合整数规划模型,目标函数为最小化车辆总成本,具体包括车辆行驶时间成本和车辆固定发车成本;提出了一种二维编码方式的混合遗传算法,针对混合遗传算法设计了多分区段单点交叉策略、动态插入算子及局部搜索策略;最后,基于Solomon VRPTW基准数据集生成27个测试算例,使用混合遗传算法进行求解。数值实验结果表明,考虑道路通行情况,选择合理的出发时间,避免拥堵时段进行配送服务,能明显减少车辆的总行驶时间,且验证了混合遗传算法能够获得问题的满意解。     

Multi-objective scheduling of two-job families on a single machine

This paper presents a preliminary analysis of the typical scheduling environment in semiconductor manufacturing involving multiple job families, and where more than one objective such as cycle time, machine utilization and the due-date accuracy needs to be simultaneously considered. In this study, the NP-hard problem of scheduling N independent jobs on a single testing machine with due dates and sequence-dependent setup times is addressed, where the multiple objectives are to minimize average cycle time, to minimize average tardiness, and to maximize machine utilization. A Pareto optimal solution, which is not inferior to any other feasible solutions in terms of all objectives, is generated combining the analytically optimal and conjunctive simulated scheduling approach. First, the machine-scheduling problem is modeled using the discrete event simulation approach and the problem is divided into simulation clock based lot selection sub-problems. Then, a Pareto optimal lot is selected using the compromise programming technique for multiobjective optimization at each decision instant in simulated time. With the help of a broad experimental design, this developed solution is then compared with common heuristic-dispatching rules such as SPT and EDD, which show better results for all the objectives over a wide range of problems. The developed scheduling method shows approximately 16.7% reduction in average cycle time, 25.6% reduction in average tardiness, and 21.6% improvement in machine utilization over the common dispatching rules, SPT and EDD.     

多渠道医疗门诊挂号的时间策略研究

现阶段多种门诊挂号方式并存时，根据患者特性制定不同的等待时间策略尤为重要。本文考虑门诊挂号的三种渠道：直接排队挂号渠道、电话预约挂号渠道与O2O预约挂号渠道。在价格外生前提下，综合考虑患者的渠道偏好程度、时间敏感性与提前支付敏感性，基于效用理论构建患者的渠道选择模型。根据该模型推导得出不同市场条件下医院的需求，医院再据此制定患者的等待时间策略以达到利润最大化的目的。研究发现：当医院仅开通单一挂号渠道时，若患者的时间敏感度越大、渠道偏好程度越小，等待时间均越小；开通某两种挂号渠道时，还需考虑患者对两种渠道的偏好比例，偏好某种渠道的患者比例增加，选择该渠道的患者等待时间增加，选择另一渠道的患者等待时间减小；同时开通三种渠道时，最偏好某渠道的患者比例越大，选择该渠道的患者的等待时间也越大；在所有情况下，患者对提前支付的敏感度均不影响最优等待时间的制定。     

Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem

In this work three genetic algorithms are presented for the permutation flowshop scheduling problem with total tardiness minimisation criterion. The algorithms include advanced techniques like path relinking, local search and a procedure to control the diversity of the population. We also include a speed up procedure in order to reduce the computational effort needed for the local search technique, which results in large CPU time savings. A complete calibration of the different parameters and operators of the proposed algorithms by means of a design of experiments approach is also given. We carry out a comparative evaluation with the best methods that can be found in the literature for the total tardiness objective, and with adaptations of other state-of-the-art methods originally proposed for other objectives, mainly makespan. All the methods have been implemented with and without the speed up procedure in order to test its effect. The results show that the proposed algorithms are very effective, outperforming the remaining methods of the comparison by a considerable margin.     

A heuristic for scheduling in a flowshop with the bicriteria of makespan and maximum tardiness minimization

This article deals with the development of a heuristic for scheduling in a flowshop with the objective of minimizing the makespan and maximum tardiness of a job. The heuristic makes use of the simulated annealing technique. The proposed heuristic is relatively evaluated against the existing heuristic for scheduling to minimize the weighted sum of the makespan and maximum tardiness of a job. The results of the computational evaluation reveal that the proposed heuristic performs better than the existing one.     

Some heuristic algorithms for total tardiness minimization in a flowshop with blocking

The flowshop scheduling problem with blocking in-process is addressed in this paper. In this environment, there are no buffers between successive machines; therefore intermediate queues of jobs waiting in the system for their next operations are not allowed. Heuristic approaches are proposed to minimize the total tardiness criterion. A constructive heuristic that explores specific characteristics of the problem is presented. Moreover, a GRASP-based heuristic is proposed and coupled with a path relinking strategy to search for better outcomes. Computational tests are presented and the comparisons made with an adaptation of the NEH algorithm and with a branch-and-bound algorithm indicate that the new approaches are promising.     

单资源调度中误工问题的作业时间压缩算法

本文采用作业时间可压缩的方法来解决单资源调度中的误工问题。在安排任务处理顺序的过程中,当某个任务发生误工时,我们基于关键路径反向搜索的方法,给出了一个启发式算法,求得需要压缩的任务集,使这个误工任务的延误时间尽可能的减少,并使需要压缩的任务数目最少,最后证明了算法的有效性,并给出了一个算例。     

Heuristic algorithms for the two-machine flowshop with limited machine availability

The paper studies a flowshop scheduling problem where machines are not available in given time intervals. The objective is to minimize the makespan. The problem is known to be NP-hard for two machines. We analyze constructive and local search based heuristic algorithms for the two-machine case. The algorithms are tested on easy and difficult test problems with up to 100 jobs and 10 intervals of non-availability. Computational results show that the algorithms perform well. For many problems an optimum solution is found.     

Beyond Patient Classification: Using Individual Patient Characteristics in Appointment Scheduling

Scheduling patients involves a trade‐off between the productivity of the service provider and customer service. This study considers how outpatient medical facilities can improve their appointment scheduling by incorporating individual patient information in the scheduling process. Specifically, we obtain data on patient characteristics and examination durations from a health clinic, describe how that data can be used to predict patient examination durations in the clinic's appointment scheduling system, and evaluate the benefit of using individual patient characteristics over a conventional classification method. Computational results illustrate this method of patient scheduling reduces an overall cost function comprised of patient wait time, physician idle time, and over time by up to 24.2%, particularly when patients are sequenced with short duration patients being scheduled first. Several environmental characteristics are found to play critical roles in determining the magnitude of the benefit, including patient punctuality, no‐show probability, the clinic duration, the appointment rule used for scheduling, and the ratio of the physician's idle time cost to the patient wait cost. We also detail and evaluate a practical procedure for using heterogeneous scheduling under a fixed schedule.     

Benefits of Surgical Smoothing and Spare Capacity: An Econometric Analysis of Patient Flow

In this study, we examine the hospital's ability to admit patients from its emergency department. From a medical perspective, the number of patients being admitted should depend solely on the patients’ clinical conditions. Using a large‐scale econometric study that includes detailed operational and clinical data on all cardiac patient encounters from a set of 128 hospitals over a period of four years, we show that this is not the case. In particular, we find that independent of their medical condition, many emergency patients are denied hospital admission because of a lack of inpatient beds. Our analysis suggests that having one more inpatient bed at the start of a day can increase the likelihood of an emergency room patient admission by around 3% on average. We examine two policies – active discharge and demand smoothing – that can help hospitals improve patient access. We find that some hospitals actively discharge inpatients when beds become scarce; hospitals that follow such an active discharge protocol are, on average, able to admit more patients. We also investigate to what extent the hospital's ability to smooth its surgical schedule impacts hospital admissions. Hospitals tend to schedule their elective patients early in the week (Mondays and Tuesdays), and discharge them by the weekend in order to minimize weekend staffing, effectively maximizing bed occupancy during the middle of the week. This “weekend effect” artificially induces variability, and reduces effective system capacity. We find that by scheduling patients more uniformly over the week, hospitals can dramatically increase patient access, obviating the need for active discharges or additional capacity investment. Our analysis quantifies these effects, and can help hospitals make effective capacity management decisions in order to improve patient flow.