防洪物资调运的数学模型

首先构建防洪物资调运的交通网络矩阵模型,并利用Dijkstra算法寻找各调运节点之间的最优路线,然后在平时以追求最小总调运费用、紧急情况下以追求最快调运速度为目标建立了防洪物资调运的优化模型,在此过程中引入偏离控制量以便充分考虑各调运节点的需求,讨论了调运期、偏离控制量对调运费用的影响,为科学制定调运方案提供依据,较好地解决了防洪物资的调运问题.     

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。     

大庆油田物资采购及库存优化机制设计与模型研究

本文结合大庆油田物资采购中的实际问题, 考虑物资市场、需求、库存三方之间的不确定性和复杂性,分别讨论了物资采购价格时变、物资需求时变、以及不同仓储容量限制下的库存优化模型的研究进展。进一步,设计了针对大庆油田物资的采购及库存优化机制,并选取大庆油田实际采购中的4种A类物资,基于时间序列方法和0-1混合整数规划,分别对机制中的价格预测部分和策略优化部分进行了数值试算,结果表明,基于准确度较高的预测价格,运用混合0-1整数规划模型制定的多品种物资的最优联合采购策略,可以实现采购成本的节省,相比于4种物资2009年的实际采购成本,节约比率高达7.66%,同时价格预测的精度也得到了用户的认可。该机制为油田物资采购和库存优化管理项目中的辅助决策支持系统原型设计提供了参照。但考虑到大庆油田实际采购中的各种复杂因素的影响,还需进一步完善该优化机制,并对相关模型进行改进。     

三种物资同时段需求的EOQ模型

本文研究了三种物资同时段需求的EOQ模型,从理论以及实例均说明了该模型相对单一物资的EOQ模型,能够缩小这三种物资对仓库的占用空间,该模型在实际问题中要求需求和采购价格均随着时间的变化而变化,通过算法计算得到了满足三种物资同时段需求的最佳采购次数,最后得出了所要建立满足需求的最小仓库容量．     

考虑突发事件状态转移的政府应急物资采购定价模型

突发事件发生后,应急物资需求量呈现爆发式增长,政府首先将常规物资调拨至受灾区,并根据阶段性救灾成果作出应急物资采购决策。突发事件状态总是不断发生转移,使得政府已采购的物资在状态好转时容易造成浪费,在状态持续恶化时又不足以满足突发需求,因此考虑突发事件状态转移情形的应急物资采购定价策略对提升政府应急物资保障能力尤为关键。为此,本研究引入数量柔性契约到政府与应急物资供应商组成的两级应急物资采购供应链,构建了基于突发事件状态转移的应急物资采购定价模型,推导得出政企达成合作的条件与双方最优决策策略,并对比分析了契约合作与分散非合作下的供应商利润与政府成本。进一步采用数值计算与敏感性分析验证该模型的有效性,讨论了若干外生变量对政企最优决策与双方成本收益的影响,提出重要的管理启示。研究表明,考虑突发事件状态转移的基于数量柔性契约的政府应急物资采购定价模型既可以有效提高应急物资储备水平,又能保障供应商的合理收益以及控制政府成本,实现了政企双赢。     

面向集团供应链的大宗物资采购计划研究

针对集团大宗物资采购的特性,研究集团采购计划的编制与调整,将集中采购计划方法应用于集团供应链中,并重点研究了需求计划变动以及集团内物资平衡引发的采购计划调整模型。模型的构建依据三个权衡准则:获取数量折扣、准时采购、降低库存,以达到计划期内采购总成本最低的目的,所建模型经转化为一般非线性模型,应用优化软件求解。最后通过算例说明所建模型的有效性。     

油田A类物资采购决策支持系统研究

结合大庆油田物资公司在重要供应链管理环节,即采购、需求和库存中所面临的实际问题(物资需求增加、仓储压力增大、采购成本增多)及三者之间的相互作用,基于预测和优化理论,构建了针对油田A类物资的采购优化和库存管理决策支持系统原型,包括：预测模块、优化模块和方案调整评估模块,为相关部门制定合理物资采购方案提供决策支持.进一步,以银浪仓库中的4种A类物资为例,运用该原型系统进行数值模拟. 结果表明,2009年和2010年4种物资的总成本节省率分别为10.35%和8.07%,效益可观. 考虑到油田物资数据结构不完备及优化模型的复杂性,该原型系统在大庆油田大规模推广方面仍需进一步完善.     

基于信息化技术的物资采购创新研究

物资采购是任何组织发展的中心环节,有效及时的物资采购直接影响着组织内部各种活动的正常开展。现代社会信息技术在物资采购领域的应用缩短了采购时间,提高了采购效率,推动了物资采购管理的创新。本文通过分析信息技术对物资采购的影响,为如何有效利用信息技术提出建议。     

采购方主导的基于能力期权契约的应急物资采购模型

设计了一个基于能力期权契约的应急物资采购模型,政府作为唯一的采购方期初向一个合约供应商采购实物储备量,并向供应商购买一定量的生产能力期权,一旦在期权执行期内有应急物资需求发生,可立即将供应商储备的生产能力转化为实物产品.通过推导和算例模拟证明,这样的采购机制在大量减少政府应急物资实物储备量的同时,降低了政府的缺货风险.同时,也合理补偿了灾后供应商被安排突击生产应急物资发生的成本,应急物资供应链实现了协调,供和需双方实现了双赢.     

整批间隔进货的存储量控制模型与随机局部弹性

利用作者于文[1，2]引入的随机弹性理论，研究了整批间隔进货并不允许缺货的随机存储模型中，一个采购周期内的总费用对最高存储量的弹性．给出了总费用弹性概率密度函数的一般表达式．进一步给出了存在多种需求物资的随机存储模型中总费用对单个物资采购量的弹性分布函数和概率密度函数，讨论了随着采购量的随机变化，总费用的弹性变化规律和变化范围．通过实例研究了当最高库存量的分布特性已知且采购量服从某一分布时，总费用的弹性变化范围及在该变化范围的可信度．     

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.     

突发事件下应急物资多目标优化调度模型与算法研究：以新冠肺炎疫情为例

突发事件下应急物资调度具有高度不确定性与动态性,应急物资跨区域调度计划能否科学合理制定对应急救援的有效开展具有重要影响。以武汉市新冠肺炎疫情为例,考虑应急物资跨区域调配的时效差异、地域分散以及资金消耗等要素,建立多周期下包含软硬需求时间窗约束的多物资品种、运输方式的跨区域三级动态物资调度网络模型,并设计一种带变异操作的动态变惯性权重自适应粒子群算法(VDCWPSO),利用疫情算例验证该算法的有效性和可行性。研究结果表明,在保障应急物资供应时效性、公平性、经济性原则下,物资跨区域调度可以缓解资源严重短缺状况、提高医用物资利用率,研究可为突发事件下应急物资调度计划的制定与动态调整提供决策支持。     

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.     

存货论模型的建立

商家进货要有计划 ,进货过多 ,卖不出去 ,造成货物积压 ,库存费增加 ;进货过少 ,供不应求 ,使销量减少 ,影响收益 .如何合理地定期进货 ,使商家获取收益最大 ,本文就存货论模型 ,利用转移概率 ,寻找最佳进货方案 ,为合理进货提供依据 .     

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.     

生命线网络系统多节点失效的应急抢修队伍派遣模型研究

针对生命线网络系统中多个节点失效的应急抢修问题,给出了抢修队伍派遣优化模型以及模型的求解方法。在本文中,首先考虑应急修复所需时间、各节点失效所造成的损失和应急修复所需成本等因素,构建了以节点失效损失最小和应急修复成本最低为目标的抢修队伍派遣优化模型;然后针对优化模型的特点,给出了模型的求解方法。最后,通过一个算例分析说明了构建的模型的可行性和有效性。     

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.     

Dynamics behaviors of a delayed stage-structured predator-prey model with impulsive effect

In this paper, we analyzed a delayed stage-structured predator-prey model with impulsive stocking on prey and continuous harvesting on predator. Sufficient conditions for the global attractivity of ‘predator-eradication’ periodic solution and permanence of the system are obtained. The results show that the behavior of impulsive stocking on prey plays an important role for the permanence of the system.     

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.