局内动态配送车调度管理及其竞争策略

提出了动态物流配送车辆调度优化问题——配送车在一度量空间中进行服务,度量空间中的任何一节点可能在任何时间提出服务请求,要求配送车将该点处的货物运送到另一点,每一个服务请求都有一个服务期限,若在规定的时间内某一服务请求不能被满足则将被取消,在考虑装/卸货所用时间的情况下,决策者如何以局内方式确定调度策略,使配送车完成的服务请求数最多.针对该不确定性条件下的管理决策问题,给出了两种局内管理策略,并利用局内问题及竞争分析理论,给出了不同载重量下(Q=1和Q=∞)的两种策略的竞争比.     

基于链与链竞争的延保服务渠道策略选择

构建由两个制造商和两个专有零售商组成的链与链竞争模型，考虑三种延保服务渠道策略选择情形——两条链均由制造商提供延保服务(MM),两条链均由零售商提供延保服务(RR)和一条链由制造商提供延保服务，另一条链由零售商提供延保服务(H),以探究链与链竞争环境下的延保服务渠道策略选择问题.结果表明，从供应链视角，MM和RR都可能成为均衡策略，且这两种均衡策略可能单独存在或同时存在.有趣的是，从制造商和零售商的视角，研究发现双方并非会一直偏好于接受同一种延保服务渠道策略，存在特定的区域使得制造商和零售商在策略选择上有冲突.此外，在链与链竞争环境下，分散化渠道情形下的系统总利润可能会优于集中化渠道情形下的系统总利润.     

电子商务环境下企业服务联盟策略研究

本文提出了企业适应电子商务应用的三种服务联盟策略,利用两阶段博弈理论,从投入水平、获利水平和投入产出比三方面分析和比较了这三种服务联盟策略,为企业在电子商务环境下通过服务联盟迅速提高其服务能力提供了理论依据.     

考虑商品三重竞争的均衡与协调策略研究北大核心CSSCI

研究了制造商向顾客提供两种产品,且两种产品在价格、质量和服务三个方面进行竞争的均衡与协调策略问题.通过构建一条包括制造商和顾客的供应链模型,分析了协调情形和无协调情形下两种产品竞争的均衡解.运用数值仿真讨论了各种参数变化带来的影响,结果表明:协调策略是制造商的占优策略,且两种产品存在越大的价格差或存在明显的质量成本优势或存在明显的服务成本优势时,采取协调策略获益幅度越大;协调策略可以弥补产品在质量成本或服务成本方面的劣势,但从整个市场的发展来说,无协调策略却能加强市场竞争激烈程度,提高产品平均质量和平均服务,使顾客受益,市场中顾客总量增加.     

寡头市场信息产品与服务的最优定价策略

在信息产品与服务竞争性市场中,信息产品及附加服务的定价策略日渐成为信息产品提供商关心的首要问题.而以往的研究多集中于单一信息产品的定价策略,没有统一考虑产品的后续服务,且假设消费者的网络外部性强度相同.分析了异质消费者和双寡头垄断市场,针对基础信息产品和附加服务的定价策略选择问题,建立了双寡头垄断市场中两个企业的捆绑销售和分别销售策略的博弈模型,使得双寡头企业通过4种局势下的策略选择实现收益最大化的定价策略.同时,将差异化网络外部性强度系数引入Hotelling模型,计算企业的需求函数和收益函数,并采用两阶段Bertrand博弈分析两个企业的定价策略和竞争行为.通过模型求解,得到4种局势下两家企业的最优定价解析解.通过数值模拟分析了潜在消费者的构成比例及市场规模对企业定价、市场占有率和最优收益产生的影响,以及不同成本和市场规模组合下企业的最优定价策略.     

大规模邻域搜索算法求解时变车辆调度问题

对时变网络车辆调度问题提出一种满足先入先出准则的时变处理方法,并建立相应的数学模型,提出一种基于大规模邻域搜索技术的智能优化算法进行求解,算法顶层采用动态规划算法搜索环状交换邻域以得到每辆车的最佳服务顾客集合;底层设计动态搜索算法用以安排每辆车的最佳服务路线.在此基础上提出顶层加入虚拟顾客和底层嵌入insert两类改进策略.通过实验仿真比较,验证了所提算法的有效性.     

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。     

考虑差异化服务时间的多车型电动汽车路径优化与充电策略研究

在政府政策大力支持以及社会环境意识不断增长的背景下,电动汽车在物流配送行业快速普及。电动汽车参与的物流配送服务需要物流专员、电动汽车和顾客三方协作完成。因此,在传统车辆配送路径优化的基础上,车辆的多样性、充电策略、人车的匹配以及服务时间差异化等因素都会影响物流运营成本。本文提出了考虑差异化服务成本的多车型电动汽车路径优化与充电策略问题并建立了该问题的整数规划数学模型。其次,提出了混合启发式算法MCWGATS,并通过多组算例验证了算法的有效性。最后,采用多组算例分析了多车型和差异化服务时间对运营成本的影响。实验结果表明,该模型有助于物流企业提高人员、物流车辆、服务时间等资源的利用效率,降低运营成本。     

动态需求下车辆路径问题的周期性优化模型及求解

针对客户点不断更新的动态需求车辆路径问题,依据滚动时域对配送中心工作时间进行划分,提出基于延迟服务的周期性客户点实时重置策略,策略中延迟服务机制能结合车辆启动延迟系数对照当前时域的时间进行检验,满足所有客户点的服务需求,保证车辆满足中心时间窗约束。设计多阶段求解的混合变邻域人工蜂群算法对各时间片内子问题进行连续迭代优化,算法中子路径动态转变的设计能较好平衡原有客户点和新客户点对路径更新和车辆实时信息匹配的要求。算例验证及对比分析表明本文策略和算法在求解动态问题时的有效性和可行性。     

随机动态装卸混合问题的分区求解策略

针对需求服从一般分布的随机动态装卸混合问题,提出一种求解该问题的分区求解策略,分析了需求稀少和需求密集情况下该策略的渐近性.仿真比较了需求服从一般分布情形下分区求解策略、随机队列中位策略、多车场随机队列中位策略和堆栈策略的求解效果,以及需求服从一般分布和需求服从均匀分布情形下分区求解策略的求解效果.结果表明,对于需求服从一般分布的随机动态装卸混合问题,分区求解策略是一种有效的求解策略.     

A note on the facility location problem with stochastic demands

In this research note that the single source capacitated facility location problem with general stochastic identically distributed demands is studied. The demands considered are independent and identically distributed random variables with arbitrary distribution. The unified a priori solution for the locations of facilities and for the allocation of customers to the operating facilities is found. This solution minimizes the objective function which is the sum of the fixed costs and the value of one of two different recourse functions. For each case the recourse function is given in closed form and a deterministic equivalent formulation of the model is presented. Some numerical examples are also given.     

Self-organizing maps in population based metaheuristic to the dynamic vehicle routing problem

We consider the dynamic vehicle routing problem (dynamic VRP). In this problem, new customer demands are received along the day. Hence, they must be serviced at their locations by a set of vehicles in real time. The approach to address the problem is a hybrid method which manipulates the self-organizing map (SOM) neural network into a population based evolutionary algorithm. The method, called memetic SOM, illustrates how the concept of intermediate structure, also called elastic net or adaptive mesh concept, provided by the original SOM can naturally be applied into a dynamic setting. The experiments show that the heuristic outperforms the approaches that were applied to the Kilby et al. 22 problems with up to 385 customers. It performs better with respect to solution quality than the ant colony algorithm MACS-VRPTW, a genetic algorithm, and a multi-agent oriented approach, with a computation time used roughly 100 times lesser.     

The Dynamic Newsvendor Model with Correlated Demand

The classic newsvendor model was developed under the assumption that period‐to‐period demand is independent over time. In real‐life applications, the notion of independent demand is often challenged. In this article, we examine the newsvendor model in the presence of correlated demands. Specifically under a stationary AR(1) demand, we study the performance of the traditional newsvendor implementation versus a dynamic forecast‐based implementation. We demonstrate theoretically that implementing a minimum mean square error (MSE) forecast model will always have improved performance relative to the traditional implementation in terms of cost savings. In light of the widespread usage of all‐purpose models like the moving‐average method and exponential smoothing method, we compare the performance of these popular alternative forecasting methods against both the MSE‐optimal implementation and the traditional newsvendor implementation. If only alternative forecasting methods are being considered, we find that under certain conditions it is best to ignore the correlation and opt out of forecasting and to simply implement the traditional newsvendor model.      

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。     

Temporary Hubs for the Global Vehicle Supply Chain in Humanitarian Operations

We model the global vehicle supply chain of an International Humanitarian Organization (IHO) with a dynamic hub location model across monthly periods. We use actual vehicle data from the International Federation of the Red Cross to feed our model and provide insights into IHO secondary support demand. We find that secondary support demand for items such as vehicles is different from primary beneficiary demand for items such as water and food. When considering disaster response and development program demand simultaneously (disaster cycle management), our results illustrate that keeping a lean centralized hub configuration with an option for temporary hubs in mega disaster locations can reduce overall supply chain costs over a long time horizon. We also show that it is possible to structure a supply chain to take operational advantage of earmarked funding. This research lays the groundwork for using optimization models to analyze disaster cycle management.     

非奇次空间动态极值估计的模型与应用

传统EVT方法是从静态的角度,研究超额数据的性质。然而,它没有同时考虑极端数据发生的时间所隐含的充分信息。本文首次在国内提出了非奇次空间动态极值理论(ITD-EVT)的概念,克服了EVT的上述缺陷,在极端数据的基础上考虑了时间因素,并引入多个解释变量,使极值分布的是三个参数为时变的,用二维泊松分布过程建立动态空间模型,是文中一大特色。把TD-EVT运用于极端情况下风险值的估计中,对金融风险管理、资产定价等问题有较大的理论和现实意义。     

A multi-objective vehicle routing and scheduling problem with uncertainty in customers’ request and priority

In this paper, a multi-objective vehicle routing and scheduling problem with uncertainty in priority and request of customers is presented. In the proposed model, a set of dynamic requests is received over time, and the planner does not have any information regarding their location and size until they arrive. Moreover, the routing model aims to satisfy different customers according to their specific time windows which were predefined by an expert as (being very important, important, casual or unimportant). This paper uses the proposed model as a multi-objective problem where the total required number of vehicles, the total distance travelled and the waiting time imposed on vehicles are minimized, and the total customers’ satisfaction for service is maximized. An efficient framework for solving this model is designed and its performance is evaluated in different steps for various test problems generalized from Solomon’s VRPTW benchmark problems. The various heuristics and improvement concepts incorporate local exploitation in the evolutionary search, and the concept of Pareto optimality for the multi-objective optimization is used in the proposed procedure. The computational experiments on data sets illustrate the efficiency and effectiveness of the proposed approach.     

最优交易策略对交易持续期的影响

构建一个纯流动市场交易动态策略模型。假设交易者按Poisson过程到达市场,交易者根据其私人估值及市场状态对限价指令的收益做预期,通过最大化其收益确定所提交指令的类型(限价指令或市价指令)。模型发现,虽然交易者到达市场的时间间隔相互独立,但交易持续期却受前一期的交易策略影响:买(卖)指令的提交将增加下一期卖(买)交易持续期的期望值,减小下一期买(卖)交易的持续期的期望值。因而,交易间的自相关性是依据最优交易策略所内生的性质,与知情交易无关。     

The impact of stochastic lead times on the bullwhip effect under correlated demand and moving average forecasts

We quantify the bullwhip effect (which measures how the variance of replenishment orders is amplified as the orders move up the supply chain) when both random demands and random lead times are estimated using the industrially popular moving average forecasting method. We assume that the lead times constitute a sequence of independent identically distributed random variables and the correlated demands are described by a first-order autoregressive process. We obtain an expression that reveals the impact of demand and lead time forecasting on the bullwhip effect. We draw a number of conclusions on the bullwhip behaviour with respect to the demand auto-correlation and the number of past lead times and demands used in the forecasts. We find maxima and minima in the bullwhip measure as a function of the demand auto-correlation.