工业园区配售电公司分时电价双层优化模型

随着增量配电网业务的放开,配售电公司在激烈的竞争中通过峰谷分时电价套餐吸引电力用户成为其运营的重要方式,而电价套餐各参与方利益的合理分配是配售电公司顺利推进此项服务的关键.考虑增量配电工业园区内配售电公司拥有配电业务的特殊性以及大工业用户调整生产的不适应成本,首先构建了工业用户与配售电公司的成本收益模型;并在此基础上提出了工业园区配售电公司峰谷分时电价双层优化模型,上层优化模型以配售电公司收益最大化为目标、下层优化模型以大工业用户用电成本最小化为目标;最后对某一试点园区数据进行仿真测算,结果表明,由该模型得到的分时电价套餐在起到削峰填谷效果的同时,能够保证配售电公司和工业用户的利益,有利于引导双方参与峰谷分时电价套餐的积极性.     

计及需求响应的大用户购电决策优化模型

为制定对大用户最合适的购电决策方案,首先对用户因转移电量产生的不适应成本进行分析,建立了大用户的需求响应模型.在此基础上,考虑分时购电、双边合同购电、自备电厂发电三种购电方式,对大用户购电成本进行分析,构建了以购电成本最小为目标的购电优化决策模型.最后采用遗传算法对所提模型进行求解,得到用户的购电策略.算例分析表明,利用所建模型得到的购电策略,一方面可以大幅减少用户的购电成本,另一方面可以产生削峰填谷的效果,提升资源结构性配置效率.     

基于灾变灰预测的电力峰谷差异常的预测模型研究

电力负荷预测过程中,峰谷值波动是一种正常现象,当高峰期负荷急速攀升时,可能出现峰谷差异常问题(如异常值、随机误差项的异方差等),则会给电力企业带来麻烦和损失.为了对负荷峰谷差异常进行有效的预测,构建了一个基于灾变灰预测的电力峰谷差异常的预测模型,并结合实例说明了此模型的可行性和有效性.     

基于峰谷分时电价机制的节能发电调度优化模型

为减少能源消耗,电力工业在发电侧推行节能发电调度,而在用电侧则施行需求侧管理。事实上,发电侧与用电侧可以通过联合优化实现更好的节能效果。峰谷分时电价将改变原有系统负荷的分布,发电侧机组发电调度的安排将随之改变,发电煤耗水平也将相应地变动。有鉴于此,首先分别构建了需求侧峰谷分时电价响应分析模型以及发电侧节能发电调度的机组组合模型;其次,为了实现分时电价响应向机组煤耗效益的传递,以机组发电煤耗与启停煤耗最少为目标构建了需求侧与发电侧的联合优化模型;最后,通过算例分析发掘模型的节能效益,借助GAMS求解上述模型,优化结果表明分时电价的优化将有助于改进发电侧的节能调度效果,同时将实现一定的环境效益。     

基于用户报价信息划分的可控负荷菜单定价模型

设计合理的需求侧电价是引导和促进用户实施需求响应的重要因素.基于可控负荷参与市场交易时的报价信息,借助机制设计中的激励相容理论,提出了一种可控负荷菜单定价模型,该模型以系统供电成本最小为目标,且用户类型是离散的.通过节点系统IEEE-30仿真实验,设计出适用于5种不同用户类型的菜单电价,并通过对比分析,表明所提出的菜单定价模型节约了系统供电成本,也为需求侧电价的设计提供了理论参考.     

考虑云安全风险的个人云存储服务最优定价策略

针对现有个人云存储服务纷纷关闭的现象,如何根据不同市场时期和云用户特征,制定合理的定价策略已成为个人云存储服务商面临的挑战性问题。本文在考虑云安全风险,云用户感知价值及弹性成本对个人云存储服务定价的影响,建立了基于云安全风险的两阶段定价模型,给出了免费试用时长确定下的最优决策及免费试用时长不确定下的最优免费试用时长及最优价格。借助数值分析,进一步研究了云安全风险系数,云用户感知价值,云安全运营成本系数对最优免费时长,最优利润的影响,结果表明,当云安全风险系数适中时,个人云存储供应商(PCSP)采取两阶段定价策略。另外,最优利润随着云安全风险系数,免费试用时长,单位安全运营成本的增加先增加后减少。     

多方式弹性需求下公交定价双层规划模型

为描述多方式城市交通网络下公交定价与出行选择行为的相互作用与影响,将出行方式选择与路径选择涵盖于同一网络,建立了上层模型分别以企业利润最大化、乘客出行成本最小化和社会福利最大化为目标函数,下层模型为多方式弹性需求随机用户配流模型的公交定价双层规划模型。运用改进遗传算法对模型整体进行求解,下层模型采用综合对角化算法和MSA算法的组合求解算法。最后,设计了一个算例以说明模型应用。结果表明:运用双层规划模型所确定的公交票价较传统静态票价可使政府、企业及出行者三方都获得更高收益,且上层模型以社会福利最大化为目标函数能代表社会群体中多数人利益,优化效果最为理想。     

产能分享对设备制造商和用户企业的影响研究

本文建立Stackelberg博弈模型比较了传统制造模式与产能分享模式下设备制造商和设备用户企业的利润,发现设备用户企业总是从产能分享模式中受益,但是订单需求增量不同的企业受益大小不同;平台制定的设备租赁价格对设备制造商的利润和设备用户企业的购买决策都将产生影响,对于任意固定价格的设备,平台的制定的最优租赁价格是唯一的,并且平台的最优利润是设备价格的倒U型函数;产能分享业务的出现对设备制造商产生了损益影响,当平台采取最优的定价策略时,价格相对比较高的设备的购买需求增加,设备制造商从产能分享业务中受益,价格相对比较低的设备的购买需求降低,设备制造商在产能分享业务中利益受损。最后通过算例分析对上述结果进行了验证。     

基于游客年龄特征的旅游巴士定价研究

为旅游巴士设计合理的定价,对旅游公共交通的发展有着积极影响。通过对游客出行偏好的分析,考虑不同年龄阶段的游客在选择行为上有较大的差异,建立了上层以旅游巴士企业利润最大为目标,下层为多方式多人群弹性需求随机用户平衡的旅游巴士定价模型,并设计了改进粒子群算法求解问题。数值实验结果表明:1)年龄特征会影响最优定价策略,考虑游客年龄在选择行为上的差异得出的票价更优;2)舒适度敏感系数对定价有影响,且旅游巴士较常规公交,舒适度更好,一定程度上提高了旅游巴士企业的竞争力;3)改进粒子群算法较标准粒子群算法,有更好的求解性能和质量。     

在线定制中的用户配置逆优化模型

针对多产品定制环境中的可实现性约束冲突问题和用户满意度最大化问题,基于Kano模型建立了多产品在线定制的原始模型;并通过逆优化模型来合理调整其中的产品配置参数和可实现性约束参数,对用户现有的个性化产品配置方案进行优化.算例表明,方法能够保证给定产品需求成为最优解,为有效引导用户在线定制活动提供新思路.     

On a Profit Maximizing Location Model

In this paper we discuss a locational model with a profit-maximizing objective. The model can be illustrated by the following situation. There is a set of potential customers in a given region. A firm enters the market and wants to sell a certain product to this set of customers. The location and demand of each potential customer are assumed to be known. In order to maximize its total profit, the firm has to decide: (1) where to locate its distribution warehouse to serve the customers; (2) the price for its product. Due to existence of competition, each customer holds a reservation price for the product. This reservation price is a decreasing function in the distance to the warehouse. If the actual price is higher than the reservation price, then the customer will turn to some other supplier and hence is lost from the firm's market. The problem of the firm is to find the best location for its warehouse and the best price for its product at the same time in order to maximize the total profit. We show that under certain assumptions on the complexity counts, a special case of this problem can be solved in polynomial time.     

Modeling customer satisfaction with new product design using a flexible fuzzy regression-data envelopment analysis algorithm

The success of new products depends greatly on customer satisfaction and meeting the customer needs is vital for new product development. By incorporating customer needs in the design and development process, organizations can improve productivity for their new products and reduce the risks associated with new product markets. Hence, design teams require methods to model customer satisfaction when setting the associated product design attributes. Thus, different approaches have been developed for modeling the relationship between customer satisfaction and product design parameters. In this study, 16 well-known fuzzy regression (FR) models are considered to understand the relationship between customer satisfaction and new product design. The design of FR models is based on the 4Ps marketing mix (product, price, place, and promotion) concept in fuzzy environments. A flexible algorithm is then presented based on the index of confidence, error measures, and data envelopment analysis for selecting the best FR model. The applicability and usefulness of the proposed algorithm is demonstrated experimentally based on an actual case study, where the flexible algorithm is employed to predict customer satisfaction with a new product design in the freezer/refrigerator industry.     

A discrete long-term location–price problem under the assumption of discriminatory pricing: Formulations and parametric analysis

We consider a competitive location problem in which a new firm has to make decisions on the locations of several new facilities as well as on its price setting in order to maximise profit. Under the assumption of discriminatory prices, competing firms set a specific price for each market area. The customers buy one unit of a single homogeneous price-inelastic product from the facility that offers the lowest price in the area the consumers belong to. Three customer choice rules are considered in order to break ties in the offered prices. We prove that, considering long-term competition on price, this problem can be reduced to a problem with decisions on location only. For each one of the choice rules the location problem is formulated as an integer programming model and a parametric analysis of these models is given. To conclude, an application with real data is presented.     

Measuring Customer Satisfaction Using a Collective Preference Disaggregation Model

A new model to assess customer satisfaction is developed through this paper. The proposed model is based on the principles of multicriteria analysis, using ordinal regression techniques. The procedure uses survey's data on customer satisfaction criteria and disaggregates simultaneously all the global satisfaction judgments via a linear programming disaggregation formulation. The model provides collective global and partial satisfaction functions as well as average satisfaction indices. These results sufficiently describe customer behavior and they can be used in the strategic planning of an organization. The implementation of the model in three real world applications is used for illustration and for testing the model's reliability. Finally, several extensions and future research in the area of customer satisfaction analysis are discussed.     

A pricing problem under Monge property

We study a pricing problem where buyers with non-uniform demand purchase one of many items. Each buyer has a known benefit for each item and purchases the item that gives the largest utility, which is defined to be the difference between the benefit and the price of the item. The optimization problem is to decide on the prices that maximize total revenue of the seller. This problem is also called the optimal product line design problem in the absence of competition.

Even though the general problem is known to be NP-hard, it can be solved efficiently under some natural assumptions on customer benefits. In this paper we study properties of optimal solutions and present a dynamic programming algorithm when customer benefits satisfy the Monge property. The same algorithm can also be used to solve the problem under the additional requirement that all buyers should be served.     

Newsvendor with multiple options of expediting

We consider a manufacturer facing single period inventory planning problem with uncertain demand and multiple options of expediting. The demand comes at a certain time in the future. The manufacturer may order the product in advance with a relatively low cost. She can order additional amount by expediting after the demand is realized. There are a number of expediting options, each of which corresponds to a certain delivery lead time and a unit procurement price. The unit procurement price is decreasing over delivery lead time. The selling price is also decreasing over time. In this paper, we assume that the manufacturer must deliver all products to the customer in a single shipment. The problem can be formulated as a profit maximization problem. We develop structural properties and show how the optimal solution can be identified efficiently. In addition, we compare our model with the classical newsvendor model and obtain a number of managerial insights.     

质量、价格对顾客满意度的影响

关于质量、价格对顾客满意度的影响,尽管许多学者已经取得了显著的成就,但是这些研究都是基于定性分析,本部分转变研究角度,利用数学方法,分析质量、价格对顾客满意度的影响,给出顾客满意度和质量、价格的函数关系式;提出顾客满意度关于质量的弹性概念和顾客满意度关于价格的弹性概念,这样就可以从量化的角度来研究不同水平的质量或者不同价格对顾客满意度的影响及其非线性关系.     

Optimal Product Line Design: Genetic Algorithm Approach to Mitigate Cannibalization

In this marketing-oriented era where manufacturers maximize profits through customer satisfaction, there is an increasing need to design a product line rather than a single product. By offering a product line, the manufacturer can customize his or her products to the needs of a variety of segments in order to maximize profits by satisfying more customers than a single product would. When the amount of data on customer preferences or possible product configurations is large and no analytical relations can be established, the problem of an optimal product line design becomes very difficult and there are no traditional methods to solve it. In this paper, we show that the usage of genetic algorithms, a mathematical heuristics mimicking the process of biological evolution, can solve efficiently the problem. Special domain operators were developed to help the genetic algorithm mitigate cannibalization and enhance the algorithm’s local search abilities. Using manufacturer’s profits as the criteria for fitness in evaluating chromosomes, the usage of domain specific operators was found to be highly beneficial with better final results. Also, we have hybridized the genetic algorithm with a linear programming postprocessing step to fine tune the prices of products in the product line. Attacking the core difficulty of cannibalization in the algorithm, the operators introduced in this work are unique.     

Inventory control by different service levels

This paper examines the multiple period inventory control problem of a single product with multiple (two) prices, depending on service level, in which optimal pricing and ordering decisions are made in each period. Traditional inventory and pricing models consider only single products, single prices, and single service levels. However, this research paper finds that a seller can improve inventory control and revenue by offering multiple prices depending on service level. This research considers a single product with multiple (two) pricing policies corresponding to service level as follows: if the customer is willing to delay the shipment, he/she will be offered a lower regular price. Otherwise, the customer will pay the regular price plus extra charges for express service. In this paper, I show the following: (1) there is an optimal pricing and replenishment policy that can control inventory and (2) there exists a finite threshold for inventory levels such that if the inventory level at the beginning of each period is higher than the threshold, the customer will be offered the express service at the regular price, without any extra charge.