回收竞争下考虑CSR行为的闭环供应链定价决策及协调

研究考虑企业社会责任(CSR)行为的闭环供应链定价决策及协调问题,分别构建了单个第三方回收及两个第三方回收竞争下的闭环供应链博弈模型,分别从企业与消费者两个视角分析了CSR投入对供应链成员的定价策略及系统总收益的影响,并建立了基于“收益共享”与“回收成本共担”相结合的契约协调模型。研究表明,两个第三方回收商间的适度竞争不仅可以降低产品价格、提升废旧产品回收效率,同时对提高企业的CSR投入水平以及收益水平也起到积极的促进作用。最后,在契约参数的可行性范围内,“收益共享-成本共担”契约在使供应链总收益达到最大的同时,还成功改善了每个成员企业的收益。     

消费者展厅行为和参考质量效应对O2O供应链动态运营策略的影响

本文研究消费者展厅行为和参考质量效应对O2O供应链动态运营策略的影响问题。分析了集中决策、分散决策和制造商分担实体店服务成本决策3种模式下的制造商产品质量策略、实体店和电商平台的服务竞争策略,以及品牌商誉变化和企业利润等问题。并进行了价格、实体店访问比例和展厅行为强度对企业绩效的影响。研究发现：展厅行为会挫伤实体店提供服务的积极性,降低品牌商誉,从而损害制造商和实体店的利润水平;而消费者的这种搭便车行为会提高电商平台利润,但由于参考质量效应的存在,其提升空间有限。综合而言,展厅行为对供应链总体绩效不利。应对展厅行为的长期战略有：1)加强上下游的纵向合作,制造商分担实体店服务成本契约,能实现供应链总体绩效的帕累托改进。2)从受展厅行为影响最大的实体店角度,保持线下产品高定价、高服务水平,提升消费者支付意愿,全面提升实体店的品牌溢价能力,获取长久生存之道。     

Binary probability maps using a hidden conditional autoregressive Gaussian process with an application to Finnish common toad data

The Finnish common toad data of Heikkinen and Hogmander are reanalysed using an alternative fully Bayesian model that does not require a pseudolikelihood approximation and an alternative prior distribution for the true presence or absence status of toads in each 10 km×10 km square. Markov chain Monte Carlo methods are used to obtain posterior probability estimates of the square-specific presences of the common toad and these are presented as a map. The results are different from those of Heikkinen and Hogmander and we offer an explanation in terms of the prior used for square-specific presence of the toads. We suggest that our approach is more faithful to the data and avoids unnecessary confounding of effects. We demonstrate how to extend our model efficiently with square-specific covariates and illustrate this by introducing deterministic spatial changes.     

Analysis of Competing Risks by Using Bayesian Smoothing

We consider the competing risks set-up. In many practical situations, the conditional probability of the cause of failure given the failure time is of direct interest. We propose to model the competing risks by the overall hazard rate and the conditional probabilities rather than the cause-specific hazards. We adopt a Bayesian smoothing approach for both quantities of interest. Illustrations are given at the end.     

Bayesian inference for stable Lévy–driven stochastic differential equations with high‐frequency data

In this paper, we consider parametric Bayesian inference for stochastic differential equations driven by a pure‐jump stable Lévy process, which is observed at high frequency. In most cases of practical interest, the likelihood function is not available; hence, we use a quasi‐likelihood and place an associated prior on the unknown parameters. It is shown under regularity conditions that there is a Bernstein–von Mises theorem associated to the posterior. We then develop a Markov chain Monte Carlo algorithm for Bayesian inference, and assisted with theoretical results, we show how to scale Metropolis–Hastings proposals when the frequency of the data grows, in order to prevent the acceptance ratio from going to zero in the large data limit. Our algorithm is presented on numerical examples that help verify our theoretical findings.     

On hysteretic vector autoregressive model with applications

This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility in the degrees of freedom for each time series. We use the proposed model to test for a causal relationship between any two target time series. Using posterior odds ratios, we overcome the limitations of the classical approach to multiple testing. Both simulated and real examples herein help illustrate the suggested methods. We apply the proposed HVAR model to investigate the causal relationship between the quarterly growth rates of gross domestic product of United Kingdom and United States. Moreover, we check the pairwise lagged dependence of daily PM2.5 levels in three districts of Taipei.     

信息不对称下价格随机的应急数量折扣契约研究

以市场需求和市场价格随机波动的二级供应链为研究对象,寻找其在信息不对称条件下绩效优化的路径。通过显示原理分别构建生产成本和销售成本信息不对称时应急供应链的数量折扣契约模型,得到最优订货量与最佳批发价策略。通过具体的算例加以验证,分析了信息不对称程度对供应链上成员及整个供应链绩效的影响。研究结果表明：当突发事件造成市场价格随机波动和市场需求变化时,供应链上的跟随者能从隐瞒私人信息中获利。但当市场需求增大时,整个供应链绩效受损,反之则反是。这与以往研究在市场需求随机、市场价格固定和信息不对称条件下的结论迥异。     

三级装备制造业供应链质量管理行为研究

作为我国工业化建设和国民经济发展的根本保证,供应链环境下装备制造企业的产品质量问题受到了广泛关注。本文重点考虑产品质量对供应链收益的影响,运用微分对策理论研究由单一制造商、单一部件供应商和单一零件供应商构成的三级装备制造业供应链质量管理行为的协调问题。通过对比Nash非合作博弈和Stackelberg主从博弈两种分散式决策模式及集中式决策模式下协同合作博弈的均衡结果,得到相关结论,并利用算例进行验证。研究表明,①分散式决策下,制造商的质量管理行为相同,但相比于Nash非合作博弈,Stackelberg主从博弈能够改善部件供应商和零件供应商的质量管理行为,提升供应链成员的收益水平;②集中式决策下,供应链成员的质量管理行为达到最优,系统整体收益实现最大。研究结论为装备制造业供应链成员间质量管理战略联盟的构建提供了理论依据。     

线上线下融合模式下考虑战略顾客行为的供应链协调研究

在移动电商高速发展和移动支付日益便捷的时代,线上线下融合商务模式正日益成为传统零售、电子商务甚至快递物流企业转型升级的重要方向,影响着供应链运营管理及其优化决策。线上线下融合模式下的战略顾客行为不同于传统模式,其对于供应链运营管理及其优化决策的影响也不同于传统模式。基于O2O运作模式特征,运用战略顾客行为理论、供应链优化与协调理论,本文分别构建了"线上下单+线下体验+线下支付+自行提货"O2O模式1、"线上浏览+线下体验+线上购买+线下配送"O2O模式2、纯线下模式和纯线上模式下考虑战略顾客行为的供应链集中决策模型和契约协调模型,并进行了相应的数值分析,在此基础上,提出了相应的管理启示。研究结果表明：（1）无论是"线上下单+线下体验+线下支付+自行提货"O2O模式1还是"线上浏览+线下体验+线上购买+线下配送"O2O模式2,收益分享契约机制可以有效地实现战略顾客行为下的供应链协调。（2）对比两种O2O模式和两种非O2O模式,O2O模式1和O2O模式2能给供应链及其成员带来更多的利润,而纯线上模式和纯线下模式能给战略顾客带来更高的效用。（3）O2O模式下,制定较高的自提补贴和较低的折扣价格,有利于提高供应链及其成员的运营绩效。（4）O2O模式下,增强产品及其相关服务的实际体验效用,有利于提高供应链整体及其成员的运营绩效,有利于提高战略顾客的净效用。     

基于混合CVaR的供应链绿色研发-广告决策与协调机制研究

针对消费者绿色偏好异质性而引发产品需求不确定性的情形,构建了基于混合CVaR准则的绿色供应链双层风险决策模型,研究了一个具有不同风险态度制造商与一个具有不同风险态度零售商所组成供应链的最优绿色研发投入及广告宣传水平等决策问题,分析了成员不同风险态度对供应链相关决策的影响。在此基础上,提出一个基于风险补偿的双向成本分担契约协调机制,并给出实现供应链协调的合理转移支付区间,最后通过数值分析验证该协调机制的有效性。结果表明,无论供应链成员表现为何种风险态度,该契约均能实现供应链协调,并且对由风险规避制造商与风险追逐零售商所组成的供应链协调效果最为显著。