季节性产品零售商的网络订单履行策略研究

以一个销售季节性产品的零售商为研究对象,该零售商拥有两个实体店,分别位于两个独立的地区,通过Drop-shipping方式接收网上的订单,以扩大销量。初始库存量是确定的,产品卖给网上顾客为零售商带来的收益要比卖给店内顾客的收益少（履行网上订单获得的收益是零售商与订单提供者共享）;因此,每当网上订单到达时,零售商需要在接收该订单、还是把产品留给能带来更高收益的未来的店内顾客间做决策;如果接收该订单,还要决策将由哪个实体店来履行订单效益更好;以最大化未来期望收益为目标,建立动态规划模型,并从实体店对单个产品期望收益的角度为这两个问题的决策提供依据;分别在两个实体店处于平等地位和其中一个实体店被确定为优先供应商两种不同的情况下,研究零售商决策结果的差异。     

基于FPGA和ARM的语音采集终端的实现

介绍一款基于FPGA和ARM方式实现的模拟话音采集终端,通过FPGA完成模数、数模转换芯片与主控MCU之间的接口转换,降低硬件的复杂程度。采集到的音频信息通过软件实现编码和解码,并以MP3的格式储存到通用存储介质。该设备的软件功能丰富,硬件的接口灵活,能够方便维护和工程化生产。通过对样机的实际测试,其性能、指标、功能和人机接口等方面与同类产品相比在均有相当的改观。     

NXD研究与应用

NXD(NativeXMLDatabase)是以XML格式存储信息的数据库.NXD的特性能很好地满足电子商务等应用对XML格式的嵌套和半结构化数据进行信息管理的特殊需要,它是这类应用的一个好的选择.本文根据NXD的研究现状,介绍了NXD的基本概念和相关技术,并对其实现和应用进行了分析和研究.     

Soft time-windows for a bi-objective vendor selection problem under a multi-sourcing strategy: Binary-continuous differential evolution

This paper introduces a novel and practical integration of the inventory control and vendor selection problems for a manufacturing system that provides multiple products for several stores located in different places. The replenishment policy of each store is the economic order quantity under a multi-sourcing strategy in which the demand rate decreases as the selling price increases. In this strategy, the ordered quantity of each store for each product can be replenished by a set of selected vendors among all. In addition, the selected vendors can deliver the required products within a certain time window based on a soft time-window mechanism. The aim is to minimize the total system cost and delivery schedule violations, simultaneously. A trade-off between the two objectives is generated using the min–max approach to obtain near fair non-dominated solutions. As the problem is known to be NP-hard, a novel meta-heuristic algorithm called binary-continuous differential evolution (BCDE) is developed to make the original differential evolution capable of solving both binary and continuous optimization problems. Moreover, an improved genetic algorithm with a multi-parent crossover operator is designed to solve the problem. While the applicability of the proposed approach and the solution methodologies are demonstrated, the solution algorithms are tuned and their performances are analyzed and compared statistically. Finally, sensitivity analyses on the size of the soft time-window and the bandwidth factor of the BCDE algorithm are conducted.     

Innovators and Imitators in Novelty‐intensive Markets: A Research Agenda

The phenomenon of demand for novelty is defined and explored as a unique and under‐examined aspect of certain markets. Demand for novelty is the portion of demand not explained by practical utility or marketing effects – it is the demand for the new and unique. We explore markets characterized by high demand for novelty and how they differ from typical markets. Primarily, this involves the central role of novelty in the product or service value proposition as well as rapid growth rates and product or service obsolescence. Within this context, we consider the dynamics of innovating and imitating and suggest several ways that first mover competition is unique in markets with high demand for novelty. From the perspective of the knowledge‐based theory of the firm, we consider the implications of organizational learning and knowledge and decision making as they relate to new product development routines, improvisation, and top management team decision making. We conclude by considering several avenues for future empirical research.     

Distributed cooperative industrial demand response

Industrial demand response, whereby energy-intensive industrial processes shift their production to take advantage of time-varying electricity prices, is a popular topic of recent research. In a traditional demand response problem, the industrial process will have customers which set their own schedule for purchases of the industrial product, which the industrial process must meet. However, by introducing cooperation between an industrial process and its customers, additional cost savings are possible. In this work, we present an optimization framework for cooperative demand response, whereby the industrial process provides economic incentives to customers to shift their schedules to better align with the utility costs seen by the industrial process. As the industrial process and its customers are likely to be different entities which do not share their operational models, we propose a distributed algorithm for solving the problem using the alternate direction method of multipliers, which enables solving the problem with minimal information sharing. The ability of this algorithm to obtain solutions which improve upon the status quo is showcased through multiple case studies. We further demonstrate that cooperation provides the most benefit when the industrial process has limited ability to store its product and when the customer has a high degree of flexibility. We also show that this idea is practically applicable through a case study where an industrial process supplies hydrogen and nitrogen to an ammonia producer.     

Teleshopping or going shopping? An information acquisition perspective

Shopping is the acquisition of information that precedes the purchase of goods and services for household or individual consumption. New telecommunications technologies enable individuals to shop and purchase without leaving the home. Teleshopping options put the individual in a choice situation between home-based shopping and store shopping. This involves a trade-off between the costs (in terms of time and money) and benefits of making a trip or of communicating with vendors. Teleshopping serves as a useful case study for examining the demand for videotex-based services and some psychological factors involved in their adoption. Two major factors associated with the benefits of shopping appear to affect that choice. First, the direct experience of multi-sensory stimulation of store or shopping-mall environments is superior in terms of information quality and quantity to that obtained through teleshopping for many products. Thus, teleshopping may not reduce the uncertainty involved in purchasing decision to the extent possible in store shopping and, therefore, may not be a satisfactory substitute. Second, the recreational and psychological gratification (or costs) that many people experience in store shopping activity but which do not exist in teleshopping may affect the adoption of teleshopping. The relative importance of these factors varies across product or service type and individual shopper's preferences.     

销售的大数据分析及对生产计划的影响的研究

文章主要研究销售的大数据分析,以及客户需求对往后生产计划的影响。以VB.net和SQL还有Excel为平台,通过程序实现对销售数据的统计,销售数据的保存,销售数据的预测、产品生产过程中的故障与分析、成本优化等。销售数据可以通过SQL保存,可以通过Excel产出折线图从而对产品数据分析,销售数据也可以通过Excel进行周期性预测,生产过程的故障也可以通过检索相关信息达到检测目的;对上季度客户需求多的产品可以在下季度实现生产。     

Teleshopping or going shopping? An information acquisition perspective

Abstract

Shopping is the acquisition of information that precedes the purchase of goods and services for household or individual consumption. New telecommunications technologies enable individuals to shop and purchase without leaving the home. Teleshopping options put the individual in a choice situation between home-based shopping and store shopping. This involves a trade-off between the costs (in terms of time and money) and benefits of making a trip or of communicating with vendors. Teleshopping serves as a useful case study for examining the demand for videotex-based services and some psychological factors involved in their adoption. Two major factors associated with the benefits of shopping appear to affect that choice. First, the direct experience of multi-sensory stimulation of store or shopping-mall environments is superior in terms of information quality and quantity to that obtained through teleshopping for many products. Thus, teleshopping may not reduce the uncertainty involved in purchasing decision to the extent possible in store shopping and, therefore, may not be a satisfactory substitute. Second, the recreational and psychological gratification (or costs) that many people experience in store shopping activity but which do not exist in teleshopping may affect the adoption of teleshopping. The relative importance of these factors varies across product or service type and individual shopper's preferences.     

海洋灾害遥感元数据及数据系统设计

提出一个海洋灾害遥感元数据框架,用来对海洋灾害遥感数据进行描述、组织、存储和管理;在此基础上,建立了海洋灾害遥感数据系统,实现了卫星数据及灾害专题产品的网络发布、查询检索和数据获取,为国家灾害应急监测与灾后恢复重建等重大应急任务提供数据交换与数据共享服务。     

Lot sizing for a recoverable product with inspection and sorting

This paper studies an inventory system where demand is satisfied by recovered and new purchased items. Used units of a product, returned by (or collected from) customers, are kept in recoverable inventory until the start of a combined process of inspection and recovery. Recovered (remanufactured) items are assumed to be as-good-as new. However, some recovered items do not qualify to be classified as “remanufactured” and are perceived by customers to be of secondary quality. These refurbished items are sold to a secondary market at a reduced price. A simple formula that determines the optimal inventory level of recoverable (used) items is developed to be able to start the inspection and recovery processes and the economic order quantity for procurement. According to the relationships among the parameters, this paper proposes and analyzes models that describe the system of interest, which are considered extensions of the work of Koh, Hwang and Sohn [Computers and Industrial Engineering 43(1–2), 59–73, 2002].     

The development of an adapted Markov chain modelling heuristic and simulation framework in the context of transportation research

This paper has developed and evaluated the implementation of an adapted Markov Chain modelling heuristic and simulation framework in the context of transportation research. In order to gain insight into the travel patterns of individuals and in the decision making that people use to make transport mode decisions, a new methodology is presented in this paper to extract knowledge from data. The presented approach shows two ways to store the sequential information (sequences of activities and travel) that is typically incorporated in activity diary data. The approach is novel, especially with respect to store information in ‘codebooks’, a term which is introduced to reflect that the information which is kept, represents the combinations of activities that typically sequentially occur in a persons' diary.In order to test the validity of the heuristic, new data is simulated and compared with the original observed data. The new data is generated by means of Monte Carlo simulation and the empirically derived information from the codebooks is used as a constraint in the simulations. In order to make a mature evaluation of the simulated diaries, different performance indicators were considered by using pattern-, trip- and activity-level measures. It is shown in the paper that the results are satisfactory and that the framework that was developed holds out considerable promise; both for gaining behavioural decision making insights and for simulating activity diary data that can assist practitioners and researchers in the calibration of travel demand models.     

Analyzing a lost-sale stochastic inventory model with Markov-modulated demands: A simulation-based optimization study

A consumer demand that presents auto-correlated components is a class of demand commonly found in competitive markets in which consumers may develop preferences for certain products which influence their willingness to purchase them again. This behavior may be observed in inventory systems whose products are subject to promotion plans in which mechanisms that incentivize the demand are implemented. Inventory systems that ignore these dependency components may severely impair their performance. This paper analyzes a stochastic inventory model where the control review system is periodic, is categorized as a lost-sale case, and is exposed to this class of auto-correlated demand pattern. The demand for products is characterized as a discrete Markov-modulated demand in which product quantities of the same item may relate to one another according to an empirical probability distribution. A simulation-based optimization that combines simulated annealing, pattern search, and ranking and selection (SAPS&RS) methods to approximate near-optimal solutions to this problem is employed. Lower and upper bounds for a range of near-optimal solutions are determined by the pattern search step enhanced by ranking and selection—indifferent zone. Results indicate that inventory performance significantly declines as the autocorrelation increases and is disregarded.     

Active Store Window: Enabling Far Store-Load Forwarding with Scalability and Complexity-Efficiency

Conventional dynamically scheduled processors often use fully associative structures named load/store queue (LSQ) to implement the value communication between loads and the older in-flight stores and to detect the store-load order violation.But this in-flight forwarding only occupies about 15% of all store-load communications,which makes the CAM-based micro-architecture the major bottleneck to scale store-load communication further.This paper presents a new micro-architecture named ASW (short for active store window).It provides a new structure named speculative active store window to implement more aggressively speculative store-load forwarding than conventional LSQ.This structure could forward the data of committed stores to the executing loads without accessing to L1 data cache,which is referred to as far forwarding in this paper.At the back-end of the pipeline,it uses in-order load re-execution filtered by the tagged SSBF (short for store sequence bloom filter) to verify the correctness of the store-load forwarding.The speculative active store window and tagged store sequence bloom filter are all set-associate structures that are more efficient and scalable than fully associative structures.Experiments show that this simpler and faster design outperforms a conventional load/store queue based design and the NoSQ design on most benchmarks by 10.22% and 8.71% respectively.     

A distribution-free newsvendor problem with nonlinear holding cost

In this paper, we analyse a single-period newsvendor model to determine the optimal order quantity where the customers’ balking occurs.This scenario occurs when the customers are opposed to buy a product for various reasons, such as decreasing quality of product, product is not as good as fresh when it reaches under a threshold level, etc. The model is investigated by assuming that the holding cost function depends on order quantity and the inventory level at which customer balking occurs depends on holding cost. The model allows partial backlogging and permits part of the backlogged shortages to turn into lost sales. We develop the model without taking any specific distributional form of demand, only assuming the mean and the variance of the distribution of demand. Finally, we illustrate the model by numerical examples and compare our distribution-free model with the specific distributional form of demand.     

多方数据加密和共享传输技术在合同能源管理互联网数据交易的应用研究

本文对合同能源管理中涉及多个用户之间的数据交易设计了一种能够满足行业特殊需求的数据加密和传输方案,能源数据的供数方可以按照需要配置哪些数据元可以卖给哪些类型的客户,数据交易平台可以为供需双方提供数据元级别的多类加密算法、多重签名算法、数据脱敏算法,支持高并发数据实时传输,并确保接受方能够以安全的方式按需索取数据、处理数据、存储数据,该方案在上海市科委支持的项目中应用,并获得了预期的效果。     

Modelling and Forecasting Customer Navigation in Intelligent Retail Environments

Understanding shopper behaviour is one of the keys to success for retailers. In particular, it is necessary that managers know which retail attributes are important to which shoppers and their main goal is to improve the consumer shopping experience. In this work, we present sCREEN (Consumer REtail ExperieNce), an intelligent mechatronic system for indoor navigation assistance in retail environments that minimizes the need for active tagging and does not require metrics maps. The tracking system is based on Ultra-wideband technology. The digital devices are installed in the shopping carts and baskets and sCREEN allows modelling and forecasting customer navigation in retail environments. This paper contributes the design of an intelligent mechatronic system with the use of a novel Hidden Markov Models (HMMs) for the representation of shoppers’ shelf/category attraction and usual retail scenarios such as product out of stock or changes on store layout. Observations are viewed as a perceived intelligent system performance. By forecasting consumers next shelf/category attraction, the system can present the item location information to the consumer, including a walking route map to a location of the product in the retail store, and/or the number of an aisle in which the product is located. Effective and efficient design processes for mechatronic systems are a prerequisite for competitiveness in an intelligent retail environment. Experiments are performed in a real retail environment that is a German supermarket, during business hours. A dataset, with consumers trajectories, timestamps and the corresponding ground truth for training as well as evaluating the HMM, have been built and made publicly available. The results in terms of Precision, Recall and F1-score demonstrate the effectiveness and suitability of our approach, with a precision value that exceeds the 76% in all test cases.     

Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising

Clickstreams are visitors' paths through a Web site. Analysis of clickstreams shows how a Web site is navigated and used by its visitors. Clickstream data of online stores contains information useful for understanding the effectiveness of marketing and merchandising efforts, such as how customers find the store, what products they see, and what products they purchase. In this paper, we present an interactive visualization system that provides users with greater abilities to interpret and explore clickstream data of online stores. This system visualizes the effectiveness of Web merchandising from two different points of view by using two different visualization techniques: visualization of sessions by using parallel coordinates and visualization of product performance by using starfield graphs. Furthermore, this system provides facilities for zooming, filtering, color-coding, dynamic querying and data sampling. It also provides summary information along with visualizations, and by maintaining a connection between visualizations and the source database, it dynamically updates the summary information. To demonstrate how the presented visualization system provides capabilities for examining online store clickstreams, we present a series of parallel coordinates and starfield visualizations that display clickstream data from an operating online retail store. A framework for understanding Web merchandising is briefly explained. A set of metrics referred to as micro-conversion rates, which are defined for Web merchandising analysis in our previous work (Lee et al., Electronic Markets, 2000), is also explained and used for the visualizations of online store effectiveness.     

Product mix optimization for a semiconductor fab: Modeling approaches and decomposition techniques

To optimize the product mix of a semiconductor fab, the production capabilities and capacities are matched with the demand in the most profitable way. In this paper we address a linear programming model of the product mix problem considering product dependent demand limits (e.g. obligations and demand forecast) and profits while respecting the capacity bounds of the production facility. Since the capacity consumption is highly dependent on choosing from different production alternatives we are implicitly solving a static capacity planning problem for each product mix. This kind of planning approach is supported by the fluid flow concept and complete resource pooling in high traffic. We propose a general model that considers a wide range of objectives, and we introduce a heuristic that is based on the decomposition of the static capacity planning problem. A computational study reports on the quality of the decomposition approaches, and examples from practice demonstrate the versatility of the model.     

“电脑”型产品需求预测的Gompertz模型与随机模拟

“电脑”型产品包括手机、电视机、电脑等无形性变质产品,这种产品需求波动性大,随机性强、历史数据失效或根本不存在历史数据,其需求量的预测往往比常规品更为困难。介绍了“电脑”型产品需求预测的Gompertz模型,并应用这个模型对2010年长沙市电脑需求情况进行了预测,通过计算机随机模拟,对2010年长沙市手机产品的需求量也进行了预测,取得了较好的效果。为进一步研究“电脑”型产品库存控制问题提供了较好的基础。