再制造逆向物流网络设计的多目标优化模型

逆向物流是物流领域的新视野,它不仅强调对废旧品的回收利用,更强调实现节约资源、保护环境和增强竞争力等目标.针对再制造逆向物流网络设计问题,在考虑废旧产品回收数量不确定的情况下,基于混合整数规划方法,建立了一个多目标的再制造逆向物流网络优化设计模型.模型以最小化网络设施总建设费用和最小化所建设施对居民产生的负效用为目标,...     

多服务对象下的第三方逆向物流网络模型设计

一、引言 第三方逆向物流企业作为专门提供第三方逆向物流的单位需要建立合理的网络结构。从而为多类服务对象服务，实现自己规模化效益。在整个逆向物流系统中，由于可以用联节点和运输路线构成的逆向物流网络来表示．所以在逆向物流系统中．最重要的是从产品回收出发，针对需求，以尽可能小的物流费用，实现产品回收网络结构、布局的合理化。网络设计是一种资源最优配置的建设过程，因此如何做到多类服务对象下的网络最优化是验证该第三方逆向物流网络设计成功与否的关键指标。在本文中我们将以最优化理论为基础对多服务对象下的第三方逆向网络模型进行设计与模型验证。     

多服务对象下的第三方逆向物流网络模型设计

一、引言 第三方逆向物流企业作为专门提供第三方逆向物流的单位需要建立合理的网络结构,从而为多类服务对象服务,实现自己规模化效益.在整个逆向物流系统中,由于可以用联节点和运输路线构成的逆向物流网络来表示,所以在逆向物流系统中,最重要的是从产品回收出发,针对需求,以尽可能小的物流费用,实现产品回收网络结构、布局的合理化.     

浅析第三方逆向物流决策与网络构建

对第三方逆向物流决策与网络构建进行研究,能够有效提高第三方逆向物流的运行质量。基于此,本文首先对第三方逆向物流的结构特征进行简单介绍。其次,对第三方逆向物流决策进行分析,其中主要包括建立专业化的回收中心、逆向物流导向技术的应用两方面内容。最后,对第三方逆向物流的网络构建进行研究,其中主要包括可直接再利用的物流网络、再制造加工的逆向网络、再循环逆向网络以及商业退货逆向网络四方面内容。     

物流节点项目的长期静态选址模型

物流节点是物流网络的关键组成部分,物流节点项目的选址是一个至关重要的战略问题,选址的好坏直接影响物流节点项目功能和效益的实现,因此,物流节点项目的选址在物流发展的进程中受到了广泛的关注.本文从静态角度出发,以0-1整数规划模型为基础,建立起物流节点项目的长期选址的数学模型,并成功的将物流节点项目的长期运营状况纳入到模型当中.     

基于逆向物流中再制造产品回收价格研究

逆向物流和再制造系统最重要的特性就是回收的不确定性,包括回收产品数量和质量的不确定性。而回收量的大小和质量都受到回收价格的直接影响,因此解决好逆向物流中回收物的定价问题,将是解决好逆向物流回收量的重要方法。本文通过对零售商和消费者之间回收价格模型的研究,找出合作博弈和非合作博弈下回收价格的均衡解,并对其进行比较,提出相应的解决方法。     

基于顾客感知的物流干扰管理研究

物流配送过程中经常有各种不确定的扰动因素产生，对物流配送造成干扰。如何在照顾顾客感知，保持顾客满意度，维系顾客忠诚度的情况下．构建干扰管理模型使系统扰动最小，是物流干扰管理研究的重要方向．极具应用价值。本文通过对物流干扰管理及物流顾客感知的研究及总结．建立了基于顾客感知的物流干扰管理模型。     

基于电子废旧产品回收的逆向物流问题研究

随着我国可持续发展战略政策的实施,建立循环经济、节约友好型社会等政策应运而生,与此同时,在政策的感召和媒体的宣传下,电子产品逆向物流回收逐渐引起专家学者的注意。本文分析了我国电子废旧产品循环利用问题亟待解决,提出了电子废旧产品逆向物流的新模式。     

浅析绿色物流在我国煤炭行业中的应用

煤炭是我国国民经济的主要能源,同时也是全社会实现可持续发展的动力和保障,但煤炭行业的黑色经济又造成了较大的环境污染问题,这主要归根于煤炭物流的粗放型运作方式。本文从绿色物流的概念入手,提出建立煤炭绿色物流体系的重要性及现实意义,主要分析了煤炭的开采、包装、运输、仓储,以及废弃物的回收这几个方面的绿色化,从而将煤炭物流由"黑色物流"转变为"绿色物流"。     

基于第三方物流的秸杆回收物流系统构建研究

由于秸杆回收物流系统的不完善，导致秸杆资源回收效率低、成本过高，回收率低，制约着秸杆资源综合利用的经济性、商品化和产业化发展。本文在分析我国秸杆回收存在的问题的基础上，对如何结合第三方物流来构建秸杆回收物流系统进行研究。     

Grey Relational Analysis and Neural Network Forecasting of REIT returns

This study employs the Grey Relational Analysis (GRA) and Artificial Neural Network (ANN) to measure the impact of key elements on the forecasting performance of real estate investment trust (REIT) returns. To manage risks from a real estate price bubble, the findings of GRA suggest that the REIT is best influenced by industrial production index, lending rate, dividend yield, stock index and its own lagged performance. Consequently, this paper adjusts the parameters from GRA and inserts the key elements into the fitted ANN model by comparing the learning effect of the Back-propagation Neural Network (BPN). This study found that the ranking provided by the GRA is significant in correcting prediction errors using the learning outcome of the BPN. The neural network model proved to minimize error function and was able to adjust weighted values in order to enhance prediction accuracy.     

Diversification and systemic risk

Portfolio diversification makes investors individually safer but creates connections between them through common asset holdings. Such connections create “endogenous covariances” between assets and investors, and enhance systemic risk by propagating shocks swiftly through the system. We provide a theoretical model in which shocks spread through constrained selling from N diversified portfolio investors in a network of asset holdings with home bias, and study the desirability of diversification by comparing the multivariate distribution of implied losses for every level of diversification. There may be a region on the parameter set for which the propagation effect dominates the individually safer one. We derive analytically the general element of the covariance between two assets i and j. We find agents may minimize their exposure to endogenous risk by spreading their wealth across more and more distant assets. The resulting network enhances systemic stability.     

A multi-agent-based negotiation support system for distributed transmission cost allocation

We have developed a multi-agent system (MAS), based on the network flow model and KQML, called MASCAN, to support negotiations in the cost allocation of network transmission. This is very important to industries that have different entities connected with lines or pipes, such as the Internet and telecommunications. Such an approach is especially useful to the utility industries, such as electricity and gas, and the transportation industry. In the system, each agent represents a node in a network, for example supplier or consumer. Agents do not receive any centralized controls or information from centralized sources to guarantee autonomy–a key requirement for the agent. In this all decisions are made locally based on the rules or knowledge that each agent has or captured to communicate or coordinate with other agents for the cheapest path under fair-play requirements. We also assume that each agent is rational, that is, one of the goals or objectives of agent decisions or movements is to minimize costs or increase profits. The solution to cost allocation is to search for the equilibrium point of a non-cooperative game subject to the given constraints, for example network capacity. We applied MASCAN to model and support the negotiation of cost allocation in power transmission, and the results and how this approach supported the process of negotiation are perceived to be closer to the real-world negotiation and the outcomes were accepted more easily by the participants. Copyright © 2001 John Wiley & Sons, Ltd.     

Bank Reserves and Financial Stability

A stochastic financial model is developed which derives the reserve levels on financial assets which minimize price level fluctuations. It is shown that these levels of reserves are a function of the structure of unanticipated shocks to asset demands and are, in general, quite different from the levels which minimize the fluctuations of either the nominal or real value of these assets. Application of the model to currency and demand deposits in the U.S.A. suggest that the price-Stablizing reserve ratio on demand deposits is approximately one-half of the 12% currently mandated by the Monetary Control Act of 1980.     

A neural‐network‐based decision‐making model in the peer‐to‐peer lending market

This study proposes an investment recommendation model for peer‐to‐peer (P2P) lending. P2P lenders usually are inexpert, so helping them to make the best decision for their investments is vital. In this study, while we aim to compare the performance of different artificial neural network (ANN) models, we evaluate loans from two perspectives: risk and return. The net present value (NPV) is considered as the return variable. To the best of our knowledge, NPV has been used in few studies in the P2P lending context. Considering the advantages of using NPV, we aim to improve decision‐making models in this market by the use of NPV and the integration of supervised learning and optimization algorithms that can be considered as one of our contributions. In order to predict NPV, three ANN models are compared concerning mean square error, mean absolute error, and root‐mean‐square error to find the optimal ANN model. Furthermore, for the risk evaluation, the probability of default of loans is computed using logistic regression. Investors in the P2P lending market can share their assets between different loans, so the procedure of P2P investment is similar to portfolio optimization. In this context, we minimize the risk of a portfolio for a minimum acceptable level of return. To analyse the effectiveness of our proposed model, we compare our decision‐making algorithm with the output of a traditional model. The experimental results on a real‐world data set show that our model leads to a better investment concerning both risk and return.     

网络嵌入性、商业模式创新与企业竞争优势关系研究

基于商业模式创新中介效应,依据全国314家企业问卷调查的样本数据,运用多元线性回归方法,考量网络嵌入性、商业模式创新和企业竞争优势之间关系,结果发现:关系嵌入性、结构嵌入性与竞争优势均有显著的正向关系;商业模式创新在关系嵌入性、结构嵌入性与企业竞争优势的关系中起着中介作用.鉴此,企业应注重构建不同形式的网络嵌入,推动商业模式的调整与变革,以提高企业竞争优势.     

Un enfoque multicriterio para el diseño de una red para el transporte de embarques internacionales

One of the important challenges for manufacturers and distributors of autoparts is the coordination of multiple sourcing areas where their international suppliers are located. The objective of this research is the design of a service transportation network that takes into account the time shipments are held at customs and transshipment terminals, as well as the possible delays due to the use of different transportation modes. To assure the best performance of the whole transportation network, a bi-criteria optimization model was formulated, the two criteria considered were cost and time. The solution to the model was the structure of the transportation network to be used by a manufacturer of auto parts located in the Toluca industrial zone; this firm imports auto parts produced by suppliers located in the United States (E.E.U.U.) and commercializes them among multiple automakers. The structure of the optimization problem was analyzed to obtain a convenient separable arrangement and then the problem was solved by using Lagrangian relaxation. A set of possible efficient solutions for the structure of the transportation network was generated by using the weighting method and the ε-constraint method; the resulting solutions represent the set of best alternatives from which the logistics managers may choose the service network that best attends the needs and logistics performance goals of the firm.     

Forecasting and Arbitrage of the Nikkei Stock Index Futures: An Application of Backpropagation Networks

A neural network model was used in forecasting the basis in SIMEX Nikkei Stock Index futures. Results for out of sample show that the neural network forecast performance was better than that of the ARIMA model. Also, a two-way ANOVA confirms that the employed neural network was able to provide the trader with more arbitrage profits than the traditional cost-of-carry model even though it observed relative less profitable arbitrage timing. The results can be attributed to the network';s higher ability to capture nonlinear market patterns.