不确定环境下回收网络选址-库存最优策略研究

基于中国废旧产品回收网络的特点,分析了由制造商、综合回收中心、当地回收点、消费者构成的回收网络,并对其选址-库存策略进行了研究.提出了一个随机规划模型,针对不同的情景进行了分析,并通过敏感性分析,得出回收周期、当地回收点和消费者之间的距离对总的废旧产品回收成本的影响,为回收商制定回收决策提供了一定的理论依据.     

政府主导型废旧产品处理中心的建立——一个政府决策模型

随着对环保问题的日益重视,废旧产品回收处理问题引起了政府、制造商和消费者的广泛关注。分析了欧洲、日本和中国台湾废旧产品回收处理的现状,指出我国应建立政府主导型的专业的废旧产品处理中心;然后分析了我国目前废旧产品处理中心存在的问题及原因,建立了一个政府决策模型;通过实证分析对废旧产品处理中心的财政补贴效果和额度等问题进行了探讨。     

基于家电回收的逆向物流网络模型研究

随着家电产品的增加,废旧家电产品的回收成为一个迫切需要解决的问题。在分析逆向物流网络模型的基础上,根据家电产品的特征,提出了家电回收的逆向物流网络模型;它包括了收集点、检测点、再制造工厂、最终处理点、产品分销中心和销售点。并运用遗传算法,以运输成本最小为目标,对家电产品回收的逆向物流网络的设施进行选址优化。最后,给出一个应用实例。     

家电产品回收物流网络模型优化研究

通过分析我国家电回收所存在的问题,对废旧家电产品回收网络进行了探讨和分析,并通过对废旧家电回收网络模型的构建建立了以利润最大化为目标函数的混合整数线性规划模型,进而对废旧家电产品回收物流的网络模型进行了优化.     

政府补贴下企业社会责任对闭环供应链的影响

分别对制造商回收和零售商回收建立模型并对其进行敏感性分析和数值仿真,探讨了履行企业社会责任对再制造闭环供应链的影响,研究表明:无论是哪个企业进行废旧产品的回收,承担企业社会责任都会对自身的利润产生影响,同时在零售商回收废旧产品的过程中会产生更多的消费者剩余。但是企业履行社会责任时,努力程度越高对于整个闭环供应链的利润越有利,还会对整个社会福利有促进作用。     

基于消费者回收行为的废旧家电回收影响因素及实证分析

文章在文献研究和问卷调查的基础上,对消费者废旧家电回收行为进行了具体测度,并总结了消费者参与废旧家电回收的主要影响因素,然后借助SPSS软件对调查数据进行了实证检验,实证研究结果表明:服务动机、行为态度、主观规范、经济动机、公众宣传对消费者的废旧家电回收行为影响最为显著。最后指出了研究存在的局限性。     

模糊环境下生产商自营逆向物流网络的优化设计

随着人们环保意识的增强、环保法律法规的健全,越来越多的企业关注废旧产品的回收及利用。废旧产品的回收利用及再制造活动不仅有利于企业节约成本、保护环境而且有助于建立企业信誉、赢得客户。然而,由于回收产品数量、质量等不确定性,逆向物流网络构建具有很大的困难。本文运用模糊规划的方法,通过建立模糊期望值模型来设计模糊环境下废旧产品回收利用及再制造的逆向物流网络,并通过模糊模拟和遗传算法相结合的混合智能算法对模型进行了求解,并进行了实例验证。     

政府规制下的双渠道逆向供应链的治理机制

非正规回收渠道的存在已经成为中国逆向供应链管理的一个严重问题,非正规回收商打破了废旧产品回收现有的正规模式。为了研究这个问题,本文引入和分析了政府和正规回收商制定的治理机制,利用Stackelberg博弈模型来描述和分析考虑消费者对回收渠道的偏好的双渠道收集供应链以获得最优决策和某些参数属性。结果表明:政府对正规回收商进行回收补贴在某些方面是有利于消费者选择正规回收商进行回收,同时消费者对正规回收渠道的偏好程度很大程度上影响着回收情况。     

废旧轮胎逆向物流网络选址优化研究

本文根据废旧轮胎逆向物流网络的相关分析,提出多级逆向物流网络的整数模型使逆向物流网络的利润最大化,并根据现实的轮胎翻新企业的案例来验算该模型,解决多级逆向物流网络的初始收集点数量、选址以及产品流向的问题。此外,本文通过灵敏度分析研究客户和初始收集点的最大距离,进一步优化轮胎回收再制造行业的逆向物流网络。     

低碳经济视角下闭环供应链网络优化研究

在低碳经济背景下,研究具有多个供应商、多个制造商、多个消费者市场以及多个回收中心的闭环供应链网络。综合考虑碳减排和废旧产品回收因素,以总运营成本最小化为优化目标,建立混合整数规划模型,从而解决网络节点的建设、节点间运量、运输方式选择、减排技术投资、回收中心选址以及回收中心环保水平等供应链网络资源配置问题。通过lingo12软件对低碳闭环供应链网络进行优化模拟,得到对生产实践有指导作用的结论。     

产品生命周期对配送网络设计的影响

为了在当今竞争激烈的市场环境中持续保有较大竞争优势,供应链中企业的配送网络设计必须考虑产品特征与顾客需求的关系.当产品经历整个生命周期不同阶段时,其市场需求的变化往往具有较大不确定性,因此,企业的配送网络必须动态地与产品需求特征进行匹配,才能具有最大的竞争力.本文讨论了配送网络的不同选择以及这些配送网络适合的产品类型,分析了在产品生命周期的不同阶段,如何设计合适的配送网络.最后,以制药业为例来具体分析产品生命周期不同阶段对配送网络设计的影响.     

Estimating surplus food supply for food rescue and delivery operations

Hunger remains a largely hidden social problem in many developed nations. The not-for-profit food rescue organizations, aid in alleviating hunger, by rescuing the surplus food from different food providers and re-distributing to people in need. However, surplus food donation is a random process which varies with regard to quantity, time and place. Understanding the dynamics of food recovery and forecasting food donations using historical information has significant importance in inventory management and redistribution, particularly in reducing operational costs and achieving a sustainable and equitable distribution of inventory incorporating uncertainties in supply. This paper uses different modelling techniques including multiple linear regression, structural equation modelling and neural networks to explore the patterns and dynamics of food donation and distribution process for one of the largest food rescue organization in Australia. A set of significant indicators has been identified to describe the current food donation process, to predict daily average food donated by different food providers and also to anticipate the potential donation from a new donor which may appear in the network in the future. Results suggest that structural equation modelling and neural networks provide improved demand estimation when compared to conventional multiple linear regression. We also discuss the usefulness of these models in sustainable and equitable management of food recovery and redistribution.     

基于逆向物流的拆卸和恢复策略优化算法

针对逆向物流中产品拆卸回收和恢复的难题,从图论的角度采用最短路径算法探讨了产品拆卸的路径选择问题。随后结合回收产品的恢复策略、不同种类的拆卸方法以及不同的拆卸修复质量,采用随机动态规划算法解决产品回收后的恢复策略的优化问题。     

废旧电子产品回收网络研究

废旧电子产品的污染问题已经越来越被重视，孕育在废旧电子产品的价值也已经被企业逐渐认识．而现行的回收网络过于简单，不利于产品的回收处理和价值恢复。本文从讨论废旧电子产品的污染和回收价值谈起．就电子产品回收的璁状提出了一个以制造企业为中心、全社会广泛参与的回收网络模型。     

Dynamic credit contagion and aggregate loss in networks

We develop a dynamic model to illustrate the credit risk contagion mechanism caused by interaction between firms. Specifically, we formulate the sources of risk into idiosyncratic risk and contagion risk, and introduce recovery ability to model the scenario of a firm changing from default into normal status. Our result shows that there always exists a steady state in a network under some trivial conditions. For quasi-regular networks and bipartite networks, the expected aggregate loss remains unchanged as long as the product of the contagion probability and the partner number is fixed.     

The importance of diverse collaborative networks for the novelty of product innovation

Competition today is driving firms to introduce products with a higher degree of novelty. Consequently, there is a growing need to understand the critical success factors behind more novel product innovations. This paper theoretically and empirically analyzes the role of different types of collaborative networks in achieving product innovations and their degree of novelty. Using data from a longitudinal sample of Spanish manufacturing firms, our results show that technological collaborative networks are of crucial importance in achieving a higher degree of novelty in product innovation. Continuity of collaboration and the composition of the collaborative network are highly significant dimensions. Collaboration with suppliers, clients and research organizations—in this order—have a positive impact on the novelty of innovation, while collaboration with competitors has a negative impact. The greatest positive impact on the degree of innovation novelty comes from collaborative networks comprising different types of partners.     

Computing systemic risk using multiple behavioral and keystone networks: The emergence of a crisis in primate societies and banks

What do the behavior of monkeys in captivity and the financial system have in common? The nodes in such social systems relate to each other through multiple and keystone networks, not just one network. Each network in the system has its own topology, and the interactions among the system’s networks change over time. In such systems, the lead into a crisis appears to be characterized by a decoupling of the networks from the keystone network. This decoupling can also be seen in the crumbling of the keystone’s power structure toward a more horizontal hierarchy. This paper develops nonparametric methods for describing the joint model of the latent architecture of interconnected networks in order to describe this process of decoupling, and hence provide an early warning system of an impending crisis.     

An agent-based model of innovation diffusion: network structure and coexistence under different information regimes

The paper analyzes how the structure of social networks affects innovation diffusion and competition under different information regimes. Diffusion is modeled as the result of idiosyncratic adoption thresholds, local network effects and information diffusion (broadcasting and demonstration effect from previous adopters). A high social cohesion decreases the probability of one innovation cornering the market. Nonetheless, with imperfect information, in small-world networks the higher speed of diffusion produced by the low average distance increases this probability. A low social cohesion also increases the probability of falling into traps of under-adoption. However, such probability is significantly lower with imperfect information, because such regime is characterized by higher levels of market concentrations and this reduces the frictions due to the coexistence of non-compatible product innovations.     

Multinomial modeling methods: Predicting four decades of international banking crises

This paper examines banking crises in a large sample of countries over a forty-year period. A multinomial modeling approach is applied to panel data in order to track and capture end-to-end cyclical crisis formations, which enhances the binary focus of previous research studies. Several macroeconomic and banking sector variables are shown to be emblematic of leading indicators across the idiosyncratic stages of a banking crisis. Gross domestic product is an early warning signal across all phases, and a concomitant deterioration in consumption spending and fixed capital formation, preceded by a credit boom, signal a banking crisis to come. Currency depreciation exemplifies ensuing financial distress, reinforced by developmental constructs and regional integration. Lower real interest rates, increasing imports, and rising deposits are frequently harbingers of a recovery. Period effects underscore the dynamic evolution of common contemporaneous precursors over time. Premised on pursuing cyclical movements through multiple outcomes, our findings on forecasting performance suggest enhanced predictive power. Several multinomial logistic models generate higher predictive accuracy in contrast to probit models. Compared to machine learning methods (which encompass artificial neural networks, gradient boost, k-nearest neighbors, and random forests methods), a multinomial logistic approach outperforms during pre-crisis periods and when crisis severity is modeled, whereas gradient boost has the highest predictive accuracy across numerous versions of the multinomial model. As investors and policy makers continue to confront banking crises, leading to high economic and social costs, enhanced multinomial modeling methods make a valuable contribution to improved forecasting performance.