大型水电工程物资调运建模及算法

根据小湾水电工程物资供应管理的实际情况,提出了物资调运的三级节点概念模型,论述了物资采购与调运的数学模型,并给出了具体算法。模型已应用于该工程物资调运决策支持系统中,运行实践表明,该模型能够辅助调度人员制定合理的物资采购与调运方案。     

沿海港口集装箱空箱调运策略优化模型

我国沿海港口进出口箱分布非常不均衡,由此产生了集装箱空箱调运现象,合理的空箱调运策略能够有效地降低航运成本。本文研究了沿海港口间的空箱调运问题。在传统确定目的港策略的基础上,提出了一种新的空箱调运策略,不确定目的港策略。针对两种不同调运策略分别建立了以总调运费用最小为目标的规划模型。并以中国沿海港口空箱调运为例,运用遗传算法,在不同的空箱供需情况下,对两种调运策略费用进行了优化求解。结果表明,供需平衡模式下,固定目的港策略略有优势;在供需不平衡模式中,不确定目的港策略优势更加明显。此研究能够为港口间空箱调运提供辅助决策。     

一类具有非线性目标函数的运输问题

在作物资调运计划时,有时会遇到这样的问题,在完成调运计划的条件下,允许某些收、发点在一定范围内增加收、发量,目标是这个调运计划的平均运输费用最低,这类问题可以归结为下列具有非线性目标函数的运输问题。     

海运服务链中的运力定价和空箱调运责任研究

本文研究了单个承运商和两个货运代理在双向港口间提供往返货运服务的航运服务链。由于港口间货运需求的内在不平衡,货运公司在多港口间的空箱调运会产生巨大的空箱调运费用。分别构建了承运商承担和承运商与货运代理共同承担空箱调运的数学模型,通过数学模型和数值算例分析了不同市场条件下的空箱调运责任和运力定价策略。研究发现承运商和货运代理是否采用定价策略来平衡需求取决于双向港口间货运市场的潜在需求差异。同时,承运商与货运代理的空箱调运分摊为非此即彼策略,当空箱调运成本大于某阈值时,承运商独自承担空箱调运责任;反之,货运代理承担空箱调运责任。而且货运代理承担模式增加整个海运链的利润,但进一步加剧空箱的不平衡状况。     

应急物资费用定价水平影响因子敏感性问题研究

分析影响因子对应急物资费用定价的敏感度有利于寻找定价管理的薄弱环节,降低整体运作费用.定性分析并绘制应急管理过程中物资定价水平因果关系图,确定影响应急物资定价的关键因子,利用多项式回归模型拟合费用定价水平,通过综合考虑影响因子的波动区间和变化概率,结合应用敏感性分析与统计分析测算各影响因子的敏感性系数,针对敏感度大的因子实施重点控制,进而提高费用控制效率,降低应急物资运营成本,为进一步制定出精细化、动态化的应急物资定价标准提供基础.通过算例分析验证了方法的科学性和可行性,能够为应急管理物资定价问题提供借鉴意义.     

多类型公共自行车调运问题

公共自行车是我国正大力发展的低碳交通出行模式,加强公共自行车调运优化是提升自行车出行吸引力的关键要素。通过对公共自行车调运背景分析,提出了一类多类型公共自行车的调运优化问题。针对现实生活中租赁站点内公共自行车不均衡的情况,建立了以总成本最小为目标的混合整数线性规划模型,并提出一种改进的混合禁忌搜索对问题进行求解。通过数值实验分析了问题特性并验证了算法性能。实验结果表明非均衡惩罚系数决定了租赁站点各类自行车的装卸载数量,并影响了调配车辆的运行路线,是实现多类型公共自行车均衡优化的关键因素。不同类型自行车的替代策略使得调运决策更加灵活。混合禁忌搜索可以求解更大规模的问题,并能在短时间内求得较好质量的解。     

基于MSPC的物资采购风险控制研究

本文首先对大量物资采购危机案例进行归纳分析,然后对采购风险诱因进行分类总结。物资采购风险被划分为三类:企业物资采购市场风险、企业物资缺货与备货风险和供应链管理的物资采购风险。针对企业物资采购风险的整体性,本文提出了基于多变量统计过程控制(MSPC)的物资采购风险综合分析模型,该模型可从全局角度控制企业物资采购风险。最后结合一家典型铝业公司监测数据,论证多变量统计过程控制模型在控制企业物资采购风险方面的有效性。     

区际救援物资中转调度的动态决策模型与算法

考虑灾害救援中灾区对应急物资的持续消耗,研究了区际多品种救援物资的动态中转调度问题.综合考虑各阶段调度费用、运输费用和库存费用总和最小化的救援物资中转调度安排和库存规划,建立了一个区际救援物资中转调度动态决策模型,并设计了一种矩阵编码的协进化遗传算法.最后通过一个算例验证了模型和算法的有效性.     

存在空箱调运的非对称运输市场定价策略研究

考虑空箱调运成本,本文对垄断和双寡头市场分别研究运输企业在两条相向路径上的定价问题。对于垄断市场,建立了运输企业最优定价策略,并刻画出无空箱调运的潜在需求不平衡区间。对于双寡头市场,考虑同一路径上不同企业潜在运输需求不等的现实情境,求解了非对称企业的伯川德纳什均衡,给出最优定价策略。研究发现,无空箱调运并不意味着较高利润,运输企业没有必要刻意消除空箱调运现象。另外,增加单位载货运输成本和竞争强度会降低企业利润,而提升单位空箱重置成本、价格敏感度和市场不对称程度都会增大企业利润。     

突发事件应急医疗物资调度的随机算法

传统的车辆路径问题(VRP)是为车辆设计将物资从仓库运送到各个需求客户的路线,使得总的运输费用(或时间)最小。在本文中,我们更关心的是使得未满足的需求量和总的物资延误时间最小。这个模型的一个非常重要的应用就是当大规模突发事件发生以后如何有效的将应急医疗物资运送到各个医疗单位,例如自然灾难,恐怖袭击之后,各个医院的医疗物资有限,需要从应急中心调集所需物资,在这种情况下,从应急中心分发应急物资过程中的运输费用就不再是最主要的考查因素,而更重要的是考虑物资到达医院的时间以及到达量,因为这两个因素直接与病人生命息息相关。本文的主要工作是改进了已有的局部搜索算法,通过引入随机算法的思想设计了求解模型的改进随机算法,可以得到模型更优的解,并通过计算机模拟案例说明了算法是行之有效的。     

基于区间数的应急物资调度决策模型及算法研究

应急物资调度决策是应急物流管理的重要研究问题之一,是影响应急救援效果的关键环节,加强应急物资调度方法研究,对提升我国应急物流管理水平,提高突发事件的应急保障能力和应对效果具有重要现实意义和理论研究价值.为了提高应急物资调度决策的有效性,基于应急物资调度问题的特点,引入区间数来描述应急物资调度决策中的不确定因素,建立了区间数信息环境下应急物资调度问题的时间最小化决策模型,提出了基于满意度水平的应急物资调度问题有效解的具体算法,最后通过算例分析验证了该方法的有效性.     

上市公司控制权转移的演化博弈分析

在演化博弈理论的框架下建立了控制权转移的接管机制模型.在具有不完全信息的控制权市场,将控制权争夺的参与者分为两个群体:接管者(进入者)群体和管理者(在位者)群体,他们随机配对进行不对称两人博弈.接管者选择进入和不进入且其特性有强、弱之分,在位者选择合作和抵制.通过建立复制者动态模型并对其均衡点进行分析,得到了控制权转移的路径及接管者与管理者的最优策略.     

Priority dispatching strategies for EMS systems

Emergency medical service (EMS) systems provide urgent medical care and transport. In this study we implement dispatching policies for EMS systems that incorporate the severity of the call in order to increase the survival probability of patients. A simulation model is developed to evaluate the performance of EMS systems. Performance is measured in terms of patients’ survival probability, since survival probability more directly mirrors patient outcomes. Different response strategies are evaluated utilizing several examples to study the nature of the optimal dispatching policy. The results show that dispatching the closest vehicle is not always optimal and dispatching vehicles considering priority of the call leads to an increase in the average survival probability of patients. A heuristic algorithm, that is easy to implement, is developed to dispatch ambulances for large-scale EMS systems. Computational examples show that the dispatching algorithm is valuable in increasing the patients’ survival probability.     

A model for the periodic optimization of bus dispatching times

We model the problem of dispatching time control in rolling horizons following a periodic optimization approach reactionary to travel time and passenger demand disturbances. This model provides more flexibility to transport planners allowing them to adjust the bus schedules during the daily operations. We prove that our periodic optimization model is a convex quadratic program, guaranteeing the global optimality of its solution. To reduce the computational burden, we introduce an iterative algorithm that uses gradient approximations to obtain an approximate dispatching solution. The proposed solution method is found to be significantly faster than exact optimization approaches for quadratic programming and maintains an (almost) negligible optimality gap in realistic bus operation scenarios. Finally, we show that our periodic optimization method outperforms myopic methods that adjust the dispatching time of each bus trip in isolation using operational data from bus line 302 in Singapore.     

Examining military medical evacuation dispatching policies utilizing a Markov decision process model of a controlled queueing system

Military medical planners must develop dispatching policies that dictate how aerial medical evacuation (MEDEVAC) units are utilized during major combat operations. The objective of this research is to determine how to optimally dispatch MEDEVAC units in response to 9-line MEDEVAC requests to maximize MEDEVAC system performance. A discounted, infinite horizon Markov decision process (MDP) model is developed to examine the MEDEVAC dispatching problem. The MDP model allows the dispatching authority to accept, reject, or queue incoming requests based on a request’s classification (i.e., zone and precedence level) and the state of the MEDEVAC system. A representative planning scenario based on contingency operations in southern Afghanistan is utilized to investigate the differences between the optimal dispatching policy and three practitioner-friendly myopic policies. Two computational experiments are conducted to examine the impact of selected MEDEVAC problem features on the optimal policy and the system performance measure. Several excursions are examined to identify how the 9-line MEDEVAC request arrival rate and the MEDEVAC flight speeds impact the optimal dispatching policy. Results indicate that dispatching MEDEVAC units considering the precedence level of requests and the locations of busy MEDEVAC units increases the performance of the MEDEVAC system. These results inform the development and implementation of MEDEVAC tactics, techniques, and procedures by military medical planners. Moreover, an analysis of solution approaches for the MEDEVAC dispatching problem reveals that the policy iteration algorithm substantially outperforms the linear programming algorithms executed by CPLEX 12.6 with regard to computational effort. This result supports the claim that policy iteration remains the superlative solution algorithm for exactly solving computationally tractable Markov decision problems.     

应急物资联合配送决策研究

救援物资的高效投放是应急物流的关键活动之一。考虑到灾后救援车辆短缺,分配时受灾群众产生非理性心理,以及灾后道路状况对军、地救灾车辆速度产生影响等因素,本文从军民融合的角度出发,根据军地运输车辆特点,设计科学合理的车辆分配方案与运输路线。论文研究构建了基于车辆分配公平性和应急配送及时性的“分配-运输”集成决策模型,设计改进后的NSGA-Ⅱ算法进行求解,最后通过算例验证本算法的有效性和合理性,并通过调节军民运输车辆数,找到应急救援过程中的最佳军、地车辆比例,从而为灾后各灾备中心的车辆调度决策提供参考。     

A comparison of different dispatching policies in two-warehouse inventory systems for deteriorating items over a finite time horizon

This paper considers a two-warehouse inventory problem for deteriorating items with a constant demand rate over a finite time horizon. A modified first-in-first-out dispatching policy is first proposed, and a new two-warehouse inventory model based on this dispatching policy is developed. The results of this model are then compared with those of other models based on classical dispatching policies, such as the last-in-first-out, modified last-in-first-out and first-in-first-out dispatching policies. We also prove the existence and uniqueness of the optimal solutions for the models considered. Finally, a numerical example is presented to illustrate the results, and several key conditions are derived for comparing the general cases of these four models.     

Solving the AGV Problem via a Self-Organizing Neural Network

Automated Guided Vehicle (AGV)-based material handling systems (MHS) are used widely in Flexible Manufacturing Systems (FMS). The problem of AGV consists of the decisions and the operational control strategies of dispatching, routeing and scheduling of a set of AGVs under given system environments and operational objectives. One remaining challenge is to develop effective methods of AGV decisions for improved system productivity. This paper describes a prototype neural network approach for the AGV problem in an FMS environment. A group of neural network models are proposed to perform dispatching and routeing tasks for the AGV under conditions of single or multiple vehicles, and with or without time windows. The goal is to satisfy the transport requests in the shortest time and in a non-conflicting manner, subject to the global manufacturing objectives. Based on Kohonen's self-organizing feature maps, we have developed efficient algorithms for the AGVs decisions, and simulation results have been very encouraging.     

The time-optimal transfer of a perturbed dynamical object to a given position

The problem of transferring a dynamical object (a point mass) of arbitrary dimensional to a required position in a time-optimal manner by means of a force of limited modulus is solved. The velocity of the object at the final instant is not specified. It is assumed that an arbitrary known perturbation, with a magnitude strictly less than that of the control, acts on the controlled system. For clarity when analysing the optimal controlled motion, considerable attention is paid to the case of a steady perturbation. A constructive procedure for finding the optimal response time and the control is developed for arbitrary permissible values of the governing parameters. The Bellman function and the feedback control are constructed over the whole of the phase space. The structural properties of the solution are established and an asymptotic analysis is carried out by small-parameter methods. The extremal directions of the perturbation vector and the corresponding response time and optimal control are found. A modification of the time-optimal problem to the case of a non-stationary perturbation is presented and the basic properties of the optimal solution are investigated.