网络零售商配送业务差别定价策略

电子商务的快速发展使网络销售的配送业务激增,包含大量的二次/重复配送业务.有效提高配送成功率,减少配送次数是企业低碳运营重要方面.采取差别定价诱导部分网络消费者放弃传统上门配送,选择自提方式.在分析消费者选择偏好基础上,建立企业利润模型,对送货上门与自提两种配送服务的差别定价问题进行研究.给出了在差别定价模式下企业最优定价决策,分析了相关因素对市场需求、企业决策和利润的影响,对企业低碳运营策略提出建议.     

带时间窗的三维装载物流配送优化方法研究

针对城市物流配送优化研究在客户服务时间窗和货物装载方式合理结合方面存在的不足,考虑物流配送车厢货物装载方式与客户访问序列相关的特征对车厢空间进行合理的区域划分。首先,构建了包含配送中心的固定成本、配送车辆的运输成本、维修成本、租赁成本和违反时间窗惩罚成本的物流运营成本最小化和配送车辆空间利用率最大化的双目标优化模型;然后,提出一种结合遗传算法(GA)全局搜索能力和禁忌搜索算法(TS)局部搜索能力的GA-TS混合算法求解模型;最后,结合重庆市某配送中心的三维装载物流配送实例数据进行了优化计算,实验结果给出了带时间窗的三维装载物流配送路径优化方案,并进行了不同车厢空间分区模式下平均装载率、物流运营成本和车辆使用数的比较分析。研究表明,当客户需求货物种类数与车辆的空间区域划分数相等且按货物类型进行区域划分时,物流运营成本最小,配送车辆使用数最少和车辆平均装载率最高。     

基于分支定价算法的电动汽车车辆路径问题

目前,随着电动汽车的普及,物流企业逐渐重视电动汽车的应用。本文考虑到电动汽车在实际应用中的行驶里程、充电耗时以及配送时间等因素,研究含时间窗的电动汽车车辆路径问题,建立了相应的混合整数规划模型,然后改进分支定价算法以求得其最优解。改进的分支定价算法首先根据Dantzig-Wolfe分解原理将原问题分解为基于路径的主问题(MP)和求最短路径的子问题,然后用列生成和动态规划算法在主问题和子问题之间进行迭代以求得主问题线性松弛后的最优解,最后采用基于弧的分支策略求得其整数解。通过用改进的Solomon算例的实验数据,与CPLEX比较验证了模型和算法结果的准确性,并对该问题进行了灵敏度分析,证明了本文提出的算法具有一定的应用价值。     

考虑交叉网络外部性的网约车平台市场定价研究

随着移动互联网的发展,具有双边市场特征的以网约车平台为载体的多种新型出行方式相继出现,但同时也带来了新的交通管理问题。基于双边市场理论,考虑了交叉网络外部性,从用户、司机均为单归属以及用户多归属、司机单归属两个方面研究了网约车平台的市场定价问题。同时,引入了时间敏感系数和司机提成比例表征网约车行业特征。最终得到了网约车平台对用户和司机的定价结构公式、用户和司机规模及平台收益。研究显示平台定价及收益与交叉网络外部性及时间敏感系数的关系与用户归属结构相关;平台定价及收益与差异化程度正相关,且在用户多归属情况下提高差异化程度会导致多归属方用户规模降低;在双边均为单归属情况下,提高司机提成比例会降低平台对出行用户的定价且增加对司机方的定价,最终导致平台均衡收益降低;但多归属下平台对司机的定价与司机提成比例的关系还与双边交叉网络外部性的相对大小有关。     

加权平均指数跳—扩散模型下的首次通过时间问题

本文将双指数跳扩散模型进行推广,提出加权平均指数跳-扩散模型.在该模型下,要研究期权的定价,需要研究首次通过时间.本文给出首次通过时间,并通过求解方程再结合鞅方法给出了首次通过时间与过冲、首次通过时间与收益过程的联合分布.     

面向电商终端物流配送的电动车配置与路径集成优化

在电子商务终端物流配送方面,存在能力与需求的矛盾。一方面,电动车存在货物容量约束和电池电量约束,配送能力有限;另一方面,一个物流配送点需要为众多的消费者进行门到门的配送,配送任务繁重。针对电子商务环境下终端物流配送规模大、电动车货物容量和行驶里程有限的问题,建立电商终端物流配送的电动车配置与路径规划集成优化模型,并提出一种基于临近城市列表的双策略蚁群算法,实现物流配送电动车辆配置与配送路径集成优化。该模型以电动车辆数最少和总路径最短为目标,以电动车货物容量和电池续航里程为约束,是带容量的车辆路径问题的进一步扩展,属于双容量约束路径规划问题。双策略蚁群算法在货物容量和续航里程的约束下,将蚁群搜索策略分为两类,即基于临近城市列表的局部搜索策略和全局搜索策略,在提高搜索效率的同时防止陷入局部优化。最后,通过阿里巴巴旗下菜鸟网络科技有限公司在上海的30组真实配送数据进行了测试,验证双策略蚁群算法显著优于一般蚁群算法。     

智能制造环境下的备件生产与运输协同调度问题研究

智能制造和即时配送环境下的备件生产与运输协同调度问题是目前国内研究的一大热点,这是因为备件供应链响应速度已成为当前备件制造企业赢得客户的关键因素。为了提高客户满意度,尽可能缩短从客户下达定制化生产订单到订单配送完成的时间,本文建立了以所有客户总等待时间最短为目标的混合整数规划模型和集合覆盖模型,推导了最优解性质,并设计改进的分支定价算法求得最优解。通过将小规模算例结果与CPLEX进行对比,验证了模型和算法的有效性。多组算例测试结果表明,所提出的模型和算法可以有效提升智能制造环境下的备件供应链运作效率。     

运单体积不确定对货运共享平台定价决策的影响研究

货运共享平台订单分析显示顾客对运输货物申报的体积往往比实际体积偏大,平台则会按照客户申报的体积进行收费。本文站在平台利润视角,构建了考虑运单体积不确定的装载与配送路径联合优化模型来研究运单体积不确定对定价决策的影响,研究发现可以提升平台利润达24%。进一步,为了实现平台、顾客与司机的收益共享,并提高平台市场占有率,平台考虑在收费体积存在偏差的情况下,采用降价的策略与平台实际定价策略进行对比。通过数值分析发现,在存在体积偏差率的情况下,价格下降18%以内时,平台的利润呈现增长的趋势,最高可以达到23.26%;当价格下降超过18%时,平台利润出现负增长,因此不适合采取降价的措施。在降价过程中,顾客需要支付的成本最多可减少19.17%,司机的收益最多可增加13.82%,结果表明,采用新型分段定价策略可以更好的实现三方共赢,对平台的实际运营有一定的借鉴意义。     

市场需求信息不对称下的保兑仓融资风险控制策略

信息不对称风险广泛存在于保兑仓融资过程当中,本文运用Stackelberg博弈模型刻画融资系统成员关系,运用动态规划优化分析方法求解对应博弈均衡策略。总结出需求信息不对称的三种表现形式:信息造假,信息优势及信息隐匿,分析各类信息不对称情形对融资系统所造成影响,并相应提出实现信息显示功能的契约甄别机制。研究表明:零售商可从信息不对称中获取巨大信息优势,但对其他成员造成损害,其中信息隐匿对生产商损害程度更高;二部定价机制可实现信息甄别,但生产商须为之付出信息租金,造成效率损失;而合理参数设定下的二部定价加回购机制有助于进一步改进融资系统及各成员收益,甚至达到次协调状态,最终实现融资成员收益的帕累托改进。本研究对于控制供应链融资中的信息风险、改善融资效率提供了理论依据及决策参考。     

收益管理中单产品动态定价的稳健模型研究

在收益管理的动态定价模型的研究中,由传统的确定性模型和随机模型所得到的定价策略常常受限制于需求估计的准确性,当对需求的估计出现偏差时定价策略可能达不到最大化收益的目的,因此定价策略即最优解的稳健性越来越受到研究者的重视。针对需求函数系数的不确定性,在未知需求分布的条件下,应用稳健最优化思想,提出了一种稳健的动态定价模型,并对模型的最优解和最大收益进行了数值模拟分析。     

Dynamic pricing of airline tickets with competition

Competition has a huge influence on customer buying behaviour and will impact on the optimal price that companies should charge for goods or services. To date, many dynamic pricing models have not modelled competition explicitly. In this paper, we introduce pricing strategies that maximize revenue when selling an inventory of identical items by a fixed time and where there is a competing seller. The model used incorporates a probabilistic formulation of customer demand, which is influenced by the prices offered by the company and the competitor, and the time remaining until the end of the selling period. Calculus of variations is used to solve the problem and simple conditions are given that ensure the uniqueness of a solution. Illustrative examples are included. A practical implementation that uses dynamic updating is proposed and tested using simulated data, showing the effectiveness of the method.     

A compromised large-scale neighborhood search heuristic for capacitated air cargo loading planning

Cost effectiveness is central to the air freight forwarders. In this work, we study how an air freight forwarder should plan its cargo loading in order to minimize the total freight cost given a limited number of rented containers. To solve the problem efficiently for practical implementation, we propose a new large-scale neighborhood search heuristic. The proposed large-scale neighborhood relaxes the subset-disjoint restriction made in the existing literature; the relaxation risks a possibility of infeasible exchanges while at the same time it avoids the potentially large amount of checking effort required to enforce the subset-disjoint restriction. An efficient procedure is then used to search for improvement in the neighborhood. We have also proposed a subproblem to address the difficulties caused by the fixed charges. The compromised large-scale neighborhood (CLSN) search heuristic has shown stably superior performance when compared with the traditional large-scale neighborhood search and the mixed integer programming model.     

同城货运众包平台的车货匹配模式对比研究

针对目前同城货运车货匹配平台采用抢单模式造成客户等待时间较长、客户满意度不高的问题,提出将派单模式应用于同城货运车货匹配过程,构建以客户平均等待时长最短为目标的动态车货匹配模型。根据抢单模式实际情况设计了就近随机配对算法,针对派单模式设计了滚动时域完美匹配算法,运用模拟仿真研究方法,对比研究了两种算法的有效性和适用性,发现订单饱和度大时宜采用派单模式,且滚动时域越短客户平均等待时长越短。研究结果可为同城货运车货匹配平台的订单分配提供决策支持,提高客户满意度。     

A continuous-time dynamic pricing model knowing the competitor’s pricing strategy

In this paper we consider a dynamic pricing model for a firm knowing that a competitor adopts a static pricing strategy. We establish a continuous time model to analyze the effect of dynamic pricing on the improvement in expected revenue in the duopoly. We assume that customers arrive to purchase tickets in accordance with a geometric Brownian motion. We derive an explicit closed-form expression for an optimal pricing policy to maximize the expected revenue. It is shown that when the competitor adopts a static pricing policy, dynamic pricing is not always effective in terms of maximizing expected revenue compared to a fixed pricing strategy. Moreover, we show that the size of the reduction in the expected revenue depends on the competitor’s pricing strategy. Numerical results are presented to illustrate the dynamic pricing policy.     

Model and algorithms for multi-period sea cargo mix problem

In this paper, we consider the sea cargo mix problem in international ocean container shipping industry. We describe the characteristics of the cargo mix problem for the carrier in a multi-period planning horizon, and formulate it as a multi-dimensional multiple knapsack problem (MDMKP). In particular, the MDMKP is an optimization model that maximizes the total profit generated by all freight bookings accepted in a multi-period planning horizon subject to the limited shipping capacities. We propose two heuristic algorithms that can solve large scale problems with tens of thousands of decision variables in a short time. Finally, numerical experiments on a wide range of randomly generated problem instances are conducted to demonstrate the efficiency of the algorithms.     

Symmetry helps: Bounded bi-directional dynamic programming for the elementary shortest path problem with resource constraints

When vehicle routing problems with additional constraints, such as capacity or time windows, are solved via column generation and branch-and-price, it is common that the pricing subproblem requires the computation of a minimum cost constrained path on a graph with costs on the arcs and prizes on the vertices. A common solution technique for this problem is dynamic programming. In this paper we illustrate how the basic dynamic programming algorithm can be improved by bounded bi-directional search and we experimentally evaluate the effectiveness of the enhancement proposed. We consider as benchmark problems the elementary shortest path problems arising as pricing subproblems in branch-and-price algorithms for the capacitated vehicle routing problem, the vehicle routing problem with distribution and collection and the capacitated vehicle routing problem with time windows.     

Routing and scheduling in project shipping

Tramp shipping companies are committed to transport a set of contracted cargoes and try to derive additional revenue from carrying optional spot cargoes. Here, we present a real life ship routing and scheduling problem for a shipping company operating in project shipping, a special segment of tramp shipping. This segment differs from more traditional tramp segments, as the cargoes are usually transported on a one-time basis. Because of the special nature of the cargoes, complicating requirements regarding stowage onboard the ships and cargo coupling must be considered while determining routes and schedules for the ships in the fleet. A mathematical model is presented and a tabu search heuristic is proposed to solve the problem. Computational results show that the tabu search heuristic provides optimal or near-optimal solutions in a reasonable amount of time, and that it can give significant improvements to manual planning for the shipping company.     

Revenue management model for on-demand IT services

This paper presents a model for applying revenue management to on-demand IT services. The multinomial logit model is used to describe customer choice over multiple classes with different service-level agreements (SLAs). A nonlinear programming model is provided to determine the optimal price or service level for each class. Through a numerical analysis, we examine the impacts of system capacity and customer waiting incentives on the service provider’s profit and pricing strategies.     

Short-term booking of air cargo space

This paper proposes a stochastic dynamic programming model for a short-term capacity planning model for air cargo space. Long-term cargo space is usually acquired by freight forwarders or shippers many months ahead on a contract basis, and usually the forecasted demand is unreliable. A re-planning of cargo space is needed when the date draws nearer to the flight departure time. Hence, for a given amount of long-term contract space, the decision for each stage is the quantity of additional space required for the next stage and the decision planning model evaluates the optimal cost policy based on the economic trade-off between the cost of backlogged shipment and the cost of acquiring additional cargo space. Under certain conditions, we show that the return function is convex with respect to the additional space acquired for a given state and the optimal expected cost for the remaining stages is an increasing convex function with respect to the state variables. These two properties can be carried backward recursively and therefore the optimal cost policy can be determined efficiently.