基于混合数据挖掘模型预测用户流失

用户流失预测问题广泛应用在银行、金融、电信等多种领域。对用户行为进行有效的预测和分析有助于企业的竞争和了解瞬息万变的市场规律。采用3种混合的数据挖掘模型对用户流失问题进行了研究,以形成一个准确高效的用户流失预测模型。这3种模型应用于数据挖掘的两个阶段:聚类阶段和预测分析阶段。在第1阶段中,对用户的数据进行过滤。第2阶段对用户行为进行预测。第1个模型采用了二分k-means算法进行数据过滤和多层感知人工神经网络(MLP-ANN)相结合进行预测。第2个模型采用层次化聚类与MLP–ANN相结合进行预测。第3个模型使用自组织映射(Self-Organizing Maps)与MLP-ANN进行预测。这3种模型预测分析基于真实数据,用户流失率采用3种模型混合计算的方式得出结果并同真实值进行比较。分析结果表明采用多模型的混合数据挖掘模型的数据准确度优于普通的单一模型。     

Coordinated scheduling of customer orders for quick response

The scheduling problem addressed in this paper concerns a manufacturer who produces a variety of product types and operates in a make‐to‐order environment. Each customer order consists of known quantities of the different product types, and must be delivered as a single shipment. Periodically the manufacturer schedules the accumulated and unscheduled customer orders. Instances of this problem occur across industries in manufacturing as well as in service environments. In this paper we show that the problem of minimizing the weighted sum of customer order delivery times is unary NP‐hard. We characterize the optimal schedule, solve several special cases of the problem, derive tight lower bounds, and propose several heuristic solutions. We report the results of a set of computational experiments to evaluate the lower bounding procedures and the heuristics, and to determine optimal solutions. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

Algorithms for computing waiting time distributions under different queue disciplines for the D-BMAP/PH/1

A queueing system characterized by the discrete batch Markovian arrival process (D-BMAP) and a probability of phase type distribution for the service time is one that arises frequently in the area of telecommunications. Under this arrival process and service time distribution we derive the waiting time distribution for three queue disciplines: first in first out (FIFO), last in first out (LIFO), and service in random order (SIRO). We also outline efficient algorithmic procedures for computing the waiting time distributions under each discipline. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 559–576, 1997     

Synchronizing pricing and replenishment to serve forward-looking customers

We incorporate strategic customer waiting behavior in the classical economic order quantity (EOQ) setting. The seller determines not only the timing and quantities of the inventory replenishment, but also the selling prices over time. While similar ideas of market segmentation and intertemporal price discrimination can be carried over from the travel industries to other industries, inventory replenishment considerations common to retail outlets and supermarkets introduce additional features to the optimal pricing scheme. Specifically, our study provides concrete managerial recommendations that are against the conventional wisdom on “everyday low price” (EDLP) versus “high-low pricing” (Hi-Lo). We show that in the presence of inventory costs and strategic customers, Hi-Lo instead of EDLP is optimal when customers have homogeneous valuations. This result suggests that because of strategic customer behavior, the seller obtains a new source of flexibility—the ability to induce customers to wait—which always leads to a strictly positive increase of the seller's profit. Moreover, the optimal inventory policy may feature a dry period with zero inventory, but this period does not necessarily result in a loss of sales as customers strategically wait for the upcoming promotion. Furthermore, we derive the solution approach for the optimal policy under heterogeneous customer valuation setting. Under the optimal policy, the replenishments and price promotions are synchronized, and the seller adopts high selling prices when the inventory level is low and plans a discontinuous price discount at the replenishment point when inventory is the highest.     

A refined deterministic linear program for the network revenue management problem with customer choice behavior

We present a new deterministic linear program for the network revenue management problem with customer choice behavior. The novel aspect of our linear program is that it naturally generates bid prices that depend on how much time is left until the time of departure. Similar to the earlier linear program used by van Ryzin and Liu (2004), the optimal objective value of our linear program provides an upper bound on the optimal total expected revenue over the planning horizon. In addition, the percent gap between the optimal objective value of our linear program and the optimal total expected revenue diminishes in an asymptotic regime where the leg capacities and the number of time periods in the planning horizon increase linearly with the same rate. Computational experiments indicate that when compared with the linear program that appears in the existing literature, our linear program can provide tighter upper bounds, and the control policies that are based on our linear program can obtain higher total expected revenues. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Priority queue with customer upgrades

This article is concerned with a general multi‐class multi‐server priority queueing system with customer priority upgrades. The queueing system has various applications in inventory control, call centers operations, and health care management. Through a novel design of Lyapunov functions, and using matrix‐analytic methods, sufficient conditions for the queueing system to be stable or instable are obtained. Bounds on the queue length process are obtained by a sample path method, with the help of an auxiliary queueing system. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

Dynamic pricing in a trade-in program with replacement and new customers

Trade-in programs have been widely adopted to enhance repeat purchase from replacement customers. Considering that a market consists of replacement and new segments, we study the joint and dynamic decisions on the selling price of new product (hereafter, “selling price”) and the trade-in price involved in the program. By adopting a vertical product differentiation choice model, we investigate two scenarios in this paper. In the base model, the manufacturer has sufficiently large production capacity to fulfill the customer demand. We characterize the structural properties of the joint pricing decisions and compare them with the optimal pricing policy under regular selling. We further propose a semi-dynamic trade-in program, under which the new product is sold at a fixed price and the trade-in price can be adjusted dynamically. Numerical experiments are conducted to evaluate the performance of the dynamic and semi-dynamic trade-in programs. In an extended model, we consider the scenario in which the manufacturer stocks a batch of new products in the beginning of the selling horizon and the inventory cannot be replenished. Following a revenue management framework, we characterize the structural properties with respect to time period and inventory level of new products.     

Approximations for heavily loaded G/GI/n + GI queues

Motivated by applications to service systems, we develop simple engineering approximation formulas for the steady‐state performance of heavily loaded G/GI/n+GI multiserver queues, which can have non‐Poisson and nonrenewal arrivals and non‐exponential service‐time and patience‐time distributions. The formulas are based on recently established Gaussian many‐server heavy‐traffic limits in the efficiency‐driven (ED) regime, where the traffic intensity is fixed at ρ > 1, but the approximations also apply to systems in the quality‐and‐ED regime, where ρ > 1 but ρ is close to 1. Good performance across a wide range of parameters is obtained by making heuristic refinements, the main one being truncation of the queue length and waiting time approximations to nonnegative values. Simulation experiments show that the proposed approximations are effective for large‐scale queuing systems for a significant range of the traffic intensity ρ and the abandonment rate θ, roughly for ρ > 1.02 and θ > 2.0. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 187–217, 2016