考虑损失厌恶型顾客的产品推荐方法研究

随着电子商务和社交网络的快速发展,在线商品和服务的数量日益增长,顾客越来越难以依据在线信息制定其购买决策。本文基于数据体量较大的在线数值评分,构建产品推荐模型,帮助顾客成功定位所需商品。首先,引入直觉梯形模糊数进行数值评分转换,以处理历史用户给定评分数据的不确定性特征;其次,引入经典前景理论来刻画现实决策过程中顾客的损失厌恶心理。最后,通过汽车之家网站的汽车购买案例及与完全理性顾客推荐结果的对比分析,验证了该模型的实用性和有效性。     

模糊需求下订购批量问题的研究

库存管理模型在现实生活中有着广泛的运用,它为管理决策者有效地确定最佳订购批量提供帮助.然而,由于历史数据的缺乏,需求量在很多情况下往往被主观地确定,因而带有一定的模糊性.本文针对两种不同类型的模糊需求:离散型与连续型,运用模糊理论分别建立了相应的模糊库存模型.该模型不同于已有的模糊库存模型如下:在现有的模糊库存的文献中,大多采用的是利用模糊集的知识对确定EOQ模型加以研究,而本文从模糊理论的角度对报童问题进行研究.     

模糊数在投资组合优化中的应用研究及实证分析

采用模糊数处理不确定性信息.以模糊期望收益率最大为目标函数,使总的风险不高于给定的模糊数,建立了一种新的模型.在给定的截集下,期望收益率转化为区间数,目标函数转化为对该区间数的下限求最大值.基于模糊数大小的概率比较,从而将模糊优化模型转化为不等式约束下的线性规划模型.利用Matlab编程可解得其最优解.最后通过实例分析,验证该模型的可行性.     

基于模糊系数的投资组合优化模型及其实证分析

采用模糊数处理不确定性信息．以模糊期望收益率最大为目标函数,使总的风险不高于给定的模糊数,建立了一种新的模型．在给定的截集下,期望收益率转化为区间数,目标函数转化为对该区间数的下限求最大值．基于模糊数大小的概率比较,从而将模糊优化模型转化为不等式约束下的线性规划模型．利用Matlab编程可解得其最优解．最后通过实例分析...     

证券组合的模糊规划模型

通过结构元方法定义了一种模糊数排序准则,利用模糊约束将Markowitz投资组舍模型转化为模糊线性规划模型,并利用模糊数来描述证券的期望收益率和风险损失率,建立模糊数模糊证券投资组合模型.最后,利用定义的模糊数排序准则把模糊数规划问题转化为经典的线性规划问题,然后再对该模型进行求解,并通过算例阐述了该方法的有效性.     

基于在险价值的物流网络规划模糊两阶段模型与精确求解方法

针对物流网络规划问题中顾客需求和运输成本的不确定性,使用在险价值量化投资风险,建立了以投资损失的在险价值最小化为目标的模糊两阶段物流网络规划模型。对于模型中不确定参数均为规则模糊数的这一类模糊两阶段规划模型,本文通过理论分析和证明将其转化为等价的确定一阶段规划模型进行求解,从而将无穷维的优化问题转化为有限维的经典优化问题,降低了计算难度且得到了模型的精确解。不同规模的数值实验证实了所提出模型及其求解方法的有效性。     

不确定情况下逆向供应链的协作与定价策略研究

基于单一制造商和单一零售商构成的逆向供应链系统,应用博弈理论和模糊理论对逆向供应链的协作与定价策略进行了研究.针对回收产品价格的不确定性,将回收价格看作是三角模糊数,采用Chen与H sieh提出的梯级平均综合表示法确定模糊回收价格的解模糊值,由此,建立非合作博弈模型和合作博弈模型,并给出均衡条件下制造商和零售商各自的回收价格策略.最后,举例说明了该方法的有效性.     

金融区间序列分析及其预测的初步评价

金融市场中的实际观测数据,除随机性外往往还带有模糊性,这样的观测数据通常以观测区间的形式给出,例如,当我们谈及某日的上证指数时,其观测值总是在最低点与最高点之间波动。观测值的这种不确定性来自于多重隶属现象,而非随机现象,我们称这种不确定性为模糊性。不确定性问题通常含有两种意义上的分类:一类是随机不确定性,人们依靠概率统计方法进行处理;另一类是非随机性的不确定性,即为模糊性问题,通常利用模糊集合理论来进行研究。本文将在模糊数学理论基础上,利用回归分析方法,构建模糊金融时间序列模型,并利用FLP(模糊线性规划)方法来估计模型的未知参数。为了合理评价拟合效果,我们将根据测量模糊集合间的择近原则,给出利用样本平均贴近度来评价模型拟合效果的一个准则。实证研究将讨论金融模糊时间观测序列的建模、参数估计,并对模型的实际拟合效果和预测效果做初步评价。     

带有模糊约束的最优路

到目前为止,最短路问题已有很多好的算法,但这些算法都是假定所有弧的长度均为确定的数值;而在实际中,常会遇到一些网络,其所有弧的长度具有不确定性.本文将这种不确定性以模糊数的形式给定,然后提出了模糊最优路的概念,并针对L-R型模糊数给出了具体的算法.     

含模糊缺陷率且允许缺货的EOQ模型研究

生产系统不完善、自然灾害、运输破损等因素对传统EOQ模型中产品质量100%合格的假设提出了巨大的挑战.本文研究了模糊环境中产品存在质量缺陷情况下允许缺货的库存模型,运用更为一般的LR型模糊数表示缺陷率、缺货成本、订货成本、需求等可能出现不确定情况的参数,建立经济订货批量模型,并运用符号距离法对模型进行反模糊化,确定了使总利润最大化的最优订货批量.最后利用实验仿真验证了模型,分析并揭示了模糊缺陷率等因素对经济订货批量的影响.     

Incorporating one-way substitution policy into the newsboy problem with imprecise customer demand

This paper presents an approach for solving an inventory model for single-period products with maximizing its expected profit in a fuzzy environment, in which the retailer has the opportunity for substitution. Though various structures of substitution arise in real life, in this study we consider the fuzzy model for two-item with one-way substitution policy. This one-way substitutability is reasonable when the products can be stored according to certain attribute levels such as quality, brand or package size. Again, to describe uncertainty usually probability density functions are being used. However, there are many situations in real world that utilize knowledge-based information to describe the uncertainty. The objective of this study is to provide an analysis of single-period inventory model in a fuzzy environment that enables us to compute the expected resultant profit under substitution. An efficient numerical search procedure is provided to identify the optimal order quantities, in which the utilization of imprecise demand and the use of one-way substitution policy increase the average expected profit. The benefit of product substitution is illustrated through numerical example.     

Supply chain coordination for fuzzy random newsboy problem with imperfect quality

Facing to imperfect quality and fuzzy random market demand in the real-life inventory management, a two-echelon supply chain system with one retailer and one manufacturer for perishable products is considered. Two fuzzy random models for the newsboy problem with imperfect quality in the decentralized and centralized systems are presented. The expectation theory and signed distance are employed to transform the fuzzy random model into crisp model. The optimal policies in the two decision-making systems are derived and analyzed contrastively. The theoretical analysis shows that manufacturer’s repurchase strategy can achieve the increase in the whole supply chain profit. The influence of the fuzzy randomness of the demand and the defective rate on the optimal order quantity, the whole supply chain profit and the repurchasing price is analyzed via numerical examples.     

Optimal price and lot size determination for a perishable product under conditions of finite production, partial backordering and lost sale

The paper describes an EOQ model of a perishable product for the case of price dependent demand, partial backordering which depends on the length of the waiting time for the next replenishment, and lost sale. The model is solved analytically to obtain the optimal price and size of the replenishment. In the model, the customers are viewed to be impatient and a fraction of the demand is backlogged. This fraction is a function of the waiting time of the customers. In most of the inventory models developed so far, researchers considered that inventory accumulates at the early stage of the inventory and then shortage occurs. This type of inventory is called IFS (inventory followed by shortage) policy. In the present model we consider that shortage occurs before the starting of inventory. We have proved numerically that instead of taking IFS, if we consider SFI (shortage followed by inventory) policy, we would get better result, i.e., a higher profit. The model is extended to the case of non-perishable product also. The optimal solution of the model is illustrated with the help of a numerical example.     

Fuzzy pricing,marketing and service planning in a fuzzy inventory model: A geometric programming approach

In the real world markets, demand is influenced by different parameters. Recently, many researchers have been interested in integrated production and marketing planning strategies in inventory models where demand depends on different parameters such as price and/or marketing expenditure. The quality of services that are offered to customers of a product is one of the most important parameters that affects demand in the real markets and has not been considered in development of inventory models. On the other hand, the cost parameters in real inventory systems and other parameters such as price, marketing and service elasticity to demand are imprecise and uncertain in nature. So, the notion of fuzziness can be applied to cope with this uncertainty. In this paper, a new fuzzy profit maximization inventory model with shortages is proposed. The demand is considered as a power function of price, marketing expenditure and service expenditure. Furthermore, unit cost is determined as a power function of order quantity. Since the proposed model is in a fuzzy environment, a fuzzy decision should be made to meet the decision criteria, and the results should be fuzzy. Therefore, the proposed model is formulated and solved using geometric programming and fuzzy optimization techniques to derive an approximation of the results’ membership functions. The model is illustrated with a numerical example and finally a case study is provided for evaluation and validation of the results of model.     

Dynamic pricing with real-time demand learning

In many service industries, the firm adjusts the product price dynamically by taking into account the current product inventory and the future demand distribution. Because the firm can easily monitor the product inventory, the success of dynamic pricing relies on an accurate demand forecast. In this paper, we consider a situation where the firm does not have an accurate demand forecast, but can only roughly estimate the customer arrival rate before the sale begins. As the sale moves forward, the firm uses real-time sales data to fine-tune this arrival rate estimation. We show how the firm can first use this modified arrival rate estimation to forecast the future demand distribution with better precision, and then use the new information to dynamically adjust the product price in order to maximize the expected total revenue. Numerical study shows that this strategy not only is nearly optimal, but also is robust when the true customer arrival rate is much different from the original forecast. Finally, we extend the results to four situations commonly encountered in practice: unobservable lost customers, time dependent arrival rate, batch demand, and discrete set of allowable prices.     

Fuzzy profit measures for a fuzzy economic order quantity model

Changing economic conditions make the selling price and demand quantity more and more uncertain in the market. The conventional inventory models determine the selling price and order quantity for a retailer’s maximal profit with exactly known parameters. This paper develops a solution method to derive the fuzzy profit of the inventory model when the demand quantity and unit cost are fuzzy numbers. Since the parameters contained in the inventory model are fuzzy, the profit value calculated from the model should be fuzzy as well. Based on the extension principle, the fuzzy inventory problem is transformed into a pair of two-level mathematical programs to derive the upper bound and lower bound of the fuzzy profit at possibility level α. According to the duality theorem of geometric programming, the pair of two-level mathematical programs is transformed into a pair of conventional geometric programs to solve. By enumerating different α values, the upper bound and lower bound of the fuzzy profit are collected to approximate the membership function. Since the profit of the inventory problem is expressed by the membership function rather than by a crisp value, more information is provided for making decisions.     

一个新的最优模糊存储模型

提出一个新的具有积压定单的关于模糊订购量的模糊存储模型.在模糊函数原理下,给出了模糊总存储成本.为了寻找最优解,把最优模糊存储模型转化为双目标最优化模型,利用L ingo8.0求解不等式约束问题,我们发现最优解都是确定的实数.此外,当模糊订购量和模糊总需求都是三角形(或权重均为1/2梯形)模糊数时,我们提出模型的最优解与经典的具有积压定单存储模型具有相同的结果.     

备货型商品在三种销售价格下的利润函数（I）

本文考虑的是备货型商品的生产与优化管理。商品的生产过程是随机可控的。需求过程是一个根据销售价格高低来控制的随机过程。本文主要研究是这样一个系统的利润函数。     

Impact of RFID technology on inventory control policy

RFID application can improve operation performance in a supply chain by reducing or eliminating inventory misplacement and shrinkage. In this paper, we present a periodic review inventory model to investigate and characterize the multiperiod inventory control policies in both non-RFID and RFID cases when the firm encounters misplacement and shrinkage. The optimal inventory control policy is proved to be a two-control limit policy. The control limits in both the non-RFID case and the RFID case are analyzed and examined, while considering the impact of shrinkage and misplacement on inventory policies. A critical inventory level is determined to identify the relationship of higher inventory level control limits between the RFID case and the non-RFID case. An intensive numerical study with sensitivity analysis of selling price, misplacement rate, shrinkage rate, inventory recovery rate, and tag price is conducted. We find that when RFID technology is adopted, the inventory control policy in the RFID case is much more stable than that of the non-RFID case, as the misplaced inventory can be recovered perfectly and instantly for sale and the inventory shrinkage can be reduced by RFID technology. In addition, one of our intriguing findings is that when the shrinkage rate is below a threshold value which is independent of parameters, RFID application has no effect on inventory control policy if the misplaced inventory can be recovered in a timely manner by physical audit, which has not been revealed in previous studies.