基于优先权的多应急点资源调度模型研究

大型突发事件由于影响规模广、破坏程度大,需要多个资源供应点向多个灾害点调度各类急需资源,研究多灾害点的情况具有很强的现实意义,研究主要解决多应急点资源调度问题。通过确定各个应急点的优先权建立多应急点资源调度模型,并用算例进行了分析。     

装备保障站点维修工作动态优化调度算法研究

对于典型的装备多级保障体系,一个上级站点通常承担了多个下级站点的故障件维修工作,因此装备保障站点维修工作的调度,对站点的保障效率有着明显的影响。在分析了故障件维修工作的优先级、故障件维修时间、装备系统故障程度、站点需求状况以及维修资源等各方面的影响后,提出了一种维修工作动态优化调度算法;建立了保障体系的仿真模型用于实现算法,然后对算法进行了实例分析,结果表明动态优化调度算法对于提高站点的保障效率、降低保障系统维修资源需求有较好的作用。     

改进现有废品回收反向物流体系的初步思路——以厦门市思明区废品回收为例

废品回收在社会经济发展中占据越来越重要的地位,厦门市思明区现有的废品回收体系是一个四级反向物流体系,存在着个体废品收购者无人管理,＂以偷为卖＂;废品回收点布局杂乱和管理失控等诸多问题。运用重心法和VSP节约里程法重建一套新型的废品回收优化方案,降低废品回收的成本,增加利润,以其规范思明区的废品回收产业,并且能改善和美化环境。如果增加的收入能合理地分配给个体废品收购者,就能改善他们的工作环境和收入,辅之以必要的监管措施,就能让整个行业良性规范地运行。     

物流配送中心车辆优化安排的研究

针对物流配送中心如何根据货物运单来调度运输车辆的问题,在研究了各种货物配装优化模型的基础上,建立了车辆安排的多目标优化模型。然后,利用线性加权法和主要目标法将多目标优化问题转化为单目标优化问题,采用精确算法思想进行模型求解。最后,编程实现了车辆优化安排功能,将该功能应用到了物流配送中心管理系统中,通过实例验证了该模型的优越性和实用性。     

基于eM-Plant的柔性生产调度仿真研究

车间生产调度是流水制造企业运行管理的核心,针对流水车间工件多样化、多机调度平行化的作业特点,在分析系统逻辑流程和运行机理的基础上,用em-plant软件建立仿真模型,通过SimTalk语言程序触发仿真事件和监控仿真状态。分析工件加工时间、到达率及加工工序对调度结果的影响,以零件平均等待时间、资源利用率和置信度为系统评价指标。仿真结果表明,在确定加工工序和加工时间条件下,生产调度方案是合理的。     

基于事件调度的客运专线能力仿真系统

通过事件调度的原理构建仿真系统,并给出了路网状态更新矩阵、事件调度模型和仿真评价模型。通过仿真系统能准确的统计列车的延误和路网的能力利用率,并通过仿真结果的分析识别路网的瓶颈,给出瓶颈疏导建议,有利于运行图系统下一步的优化调整。最后,对武广客运专线建立了仿真系统,并通过仿真实验对武广客运专线的实际能力进行评价。     

基于JIT采购的3PL运输调度问题研究

研究Just in Time(JIT)背景下制造商主导的第三方物流(Third Party Logistics,3PL)运输调度问题。制造商根据其生产计划的要求进行采购。使用C-W路线优化算法,在线路规划中考虑了车辆载重量、容积以及车辆到达时间的影响,实现以最小的成本达到JIT采购的目的,并用一个实例验证了修正的C-W算法对解决采购物流中运输调度问题的适用性。     

Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm

This paper addresses the problem of finding robust and stable solutions for the flexible job shop scheduling problem with random machine breakdowns. A number of bi-objective measures combining the robustness and stability of the predicted schedule are defined and compared while using the same rescheduling method. Consequently, a two-stage Hybrid Genetic Algorithm (HGA) is proposed to generate the predictive schedule. The first stage optimizes the primary objective, minimizing makespan in this work, where all the data is considered to be deterministic with no expected disruptions. The second stage optimizes the bi-objective function and integrates machines assignments and operations sequencing with the expected machine breakdown in the decoding space. An experimental study and Analysis of Variance (ANOVA) is conducted to study the effect of different proposed measures on the performance of the obtained results. Results indicate that different measures have different significant effects on the relative performance of the proposed method. Furthermore, the effectiveness of the current proposed method is compared against three other methods; two are taken from literature and the third is a combination of the former two methods.     

A column generation-based approach to solve the preference scheduling problem for nurses with downgrading

The demand for nurses in virtually all western countries has been outpacing the supply for more than a decade. The situation is now at the point where the rules for good practice are being stretched to the limit and patient care is in jeopardy. The purpose of this paper is to present several ideas for maximizing the use of the available staff and to quantify the resultant benefits. Two approaches are investigated for substituting nurses with higher level skills for those with lower level skills when there is sufficient idle time to do so. Idle time is usually due to scheduling constraints and contractual agreements that prevent a hospital from arbitrarily assigning nurses to shifts over the week.When the substitution is skill-related, as it is here, it is often called downgrading. The models that we develop are for preference scheduling, which means that individual preferences are taken into account when constructing monthly rosters. There are several reasons for doing this in today's environment, the most important is the need to boost staff morale and increase retention. The problem is modeled as an integer program and solved with a column generation technique that relies on intelligent heuristics for identifying good candidate schedules. The computations show that high quality solutions, as measured by the reduction in the need for non-unit nurses as well as the degree to which preferences are satisfied, can usually be obtained in a matter of minutes.     

Proportional scheduling, split-proofness, and merge-proofness

If shortest (respectively longest) jobs are served first, splitting a job into smaller jobs (respectively merging several jobs) can reduce the actual wait. Any deterministic protocol is vulnerable to strategic splitting and/or merging. This is not true if scheduling is random, and users care only about expected wait.The Proportional rule draws the job served last with probabilities proportional to size, then repeats among the remaining jobs. It is immune to splitting and merging. Among split-proof protocols constructed in this recursive way, it is characterized by either one of three properties: job sizes and delays are co-monotonic; total delay is at most twice optimal delay; the worst (expected) delay of any job is at most twice the smallest feasible worst delay. A similar result holds within the family of separable rules,