全渠道服务商在线上自助服务技术背景下的定价策略研究

在线上自助服务技术兴起的背景下,研究餐饮业服务商整合全渠道的定价策略。分析归纳了三种提供服务的渠道(线下取号排队、线上取号排队、线上预定);利用排队论以及依据消费者效用对服务系统进行理论建模,并结合数值分析,为服务商如何应对不同的消费者与市场环境指明了方向。研究发现:(1)给出了三种服务渠道的最优定价模型表示,并结合市场情况分析不同的定价策略,如在服务高峰期可以采取歧视定价策略;(2)指明了线上取号与线上预定两种渠道不宜同时提供,并给出了最优的线上预定的预留容量比例,对商家设计渠道具有指导意义;(3)发现了不管线下消费者比例如何,服务商营收曲线必定经过同一点,对服务商采取措施引流具有启发意义。本文对服务商全渠道理论建模具有启发意义。     

基于机票超售模型的高校图书馆网上预约管理策略

由于图书预约管理体制的不完善,预约借书成功率不高,大量预约登记图书滞留,严重影响图书的流通,因此合理可行的预约管理方案是各高校图书馆共同的迫切需求.基于机票超售模型,结合高校图书馆的实际情况,根据预约者是否按时来图书馆领取预约图书,总体分析复本数、预约期限、预约成功率与图书滞留天数的关系,应用Matlab7.0求解最佳的预约方案来减少预约服务造成的图书滞留.并且以天津大学图书馆为例验证模型效果,以图书滞留天数衡量预约图书流通情况,应用高校图书预约模型对预约方案进行改进,使得预约图书滞留天数明显减少,成功达到提高预约图书流通率,更好地为高校师生服务的目的.     

A Markov model for single-leg air cargo revenue management under a bid-price policy

In this paper, we consider the capacity allocation problem in single-leg air cargo revenue management. We assume that each cargo booking request is endowed with a random weight, volume and profit rate and propose a Markovian model for the booking request/acceptance/rejection process. The decision on whether to accept the booking request or to reserve the capacity for future bookings follows a bid-price control policy. In particular, a cargo will be accepted only when the revenue from accepting it exceeds the opportunity cost, which is calculated based on bid prices. Optimal solutions are derived by maximizing a reward function of a Markov chain. Numerical comparisons between the proposed approach and two existing static single-leg air cargo capacity allocation policies are presented.     

Airline network revenue management by multistage stochastic programming

A multistage stochastic programming approach to airline network revenue management is presented. The objective is to determine seat protection levels for all itineraries, fare classes, points of sale of the airline network and all dcps of the booking horizon such that the expected revenue is maximized. While the passenger demand and cancelation rate processes are the stochastic inputs of the model, the stochastic protection level process represents its output and allows to control the booking process. The stochastic passenger demand and cancelation rate processes are approximated by a finite number of tree structured scenarios. The scenario tree is generated from historical data using a stability-based recursive scenario reduction scheme. Numerical results for a small hub-and-spoke network are reported. This research is supported by the DFG Research Center Matheon “Mathematics for key technologies” in Berlin.     

Price and lead time decisions in dual-channel supply chains

Manufacturers today are increasingly adopting a dual channel to sell their products, i.e., the traditional retail channel and an online direct channel. Empirical studies have shown that service quality (we focus on the delivery lead time of the direct channel) even goes beyond product price as one of the major factors influencing consumer acceptance of the direct channel. Delivery lead time has significant effects on demand, profit, and pricing strategy. However, there is scant literature addressing the decision on the promised delivery lead time of a direct channel and its impact on the manufacturer’s and retailer’s pricing decisions. To fill this gap, we examine the optimal decisions of delivery lead time and prices in a centralized and a decentralized dual-channel supply chain using the two-stage optimization technique and Stackelberg game, and analyze the impacts of delivery lead time and customer acceptance of a direct channel on the manufacturer’s and retailer’s pricing behaviours. We analytically show that delivery lead time strongly influences the manufacturer’s and the retailer’s pricing strategies and profits. Our numerical studies reveal that the difference between the demand transfer ratios in the two channels with respect to delivery lead time and direct sale price, customer acceptance of the direct channel, and product type have great effects on the lead time and pricing decisions.     

Incorporating the pricing decisions into the dynamic fleet management problem

This paper presents a model to coordinate the pricing and fleet management decisions of a freight carrier. We consider a setting where the loads faced by the carrier over a certain time horizon are deterministic functions of the prices. We want to find what prices the carrier should charge so that its pricing and fleet management decisions jointly maximize the profits. Our solution approach is an iterative one. At each iteration, we solve the fleet management problem with fixed prices, and then, adjust these prices by using the primal-dual solution to the fleet management problem so as to obtain ‘better’ prices. Computational experiments show that our approach yields high-quality solutions and can efficiently be applied on large problems.     

A competitive dynamic pricing model when demand is interdependent over time

In this study, we contribute to the dynamic pricing literature by developing a finite horizon model for two firms offering substitutable and nonperishable products with different quality levels. Customers can purchase and store the products, even if they do not need them at the time, in order to use them in future. The stockpile of the products generated by customers affects the demand in future periods. Therefore, the demand for each product not only is a function of prices and quality levels, but also of the products’ stockpile levels. In addition, the stockpile levels change the customers’ consumption behavior; more product in a stockpile leads to more consumption. Therefore, we address not only the price and demand relationship but also the stockpiling and consumption relationship in a competitive environment.     

Network capacity management under competition

We consider capacity management games between airlines who transport passengers over a joint airline network. Passengers are likely to purchase alternative tickets of the same class from competing airlines if they do not get tickets from their preferred airlines. We propose a Nash and a generalized Nash game model to address the competitive network revenue management problem. These two models are based on well-known deterministic linear programming and probabilistic nonlinear programming approximations for the non-competitive network capacity management problem. We prove the existence of a Nash equilibrium for both games and investigate the uniqueness of a Nash equilibrium for the Nash game. We provide some further uniqueness and comparative statics analysis when the network is reduced to a single-leg flight structure with two products. The comparative statics analysis reveals some useful insights on how Nash equilibrium booking limits change monotonically in the prices of products. Our numerical results indicate that airlines can generate higher and more stable revenues from a booking scheme that is based on the combination of the partitioned booking-limit policy and the generalized Nash game model. The results also show that this booking scheme is robust irrespective of which booking scheme the competitor takes.     

Competitive Multi-period Pricing for Perishable Products: A Robust Optimization Approach

We study a multi-period oligopolistic market for a single perishable product with fixed inventory. Our goal is to address the competitive aspect of the problem together with demand uncertainty using ideas from robust optimization and variational inequalities. The demand function for each seller has some associated uncertainty and we assume that the sellers would like to adopt a policy that is robust to adverse uncertain circumstances. We believe this is the first paper that uses robust optimization for dynamic pricing under competition. In particular, starting with a given fixed inventory, each seller competes over a multi-period time horizon in the market by setting prices and protection levels for each period at the beginning of the time horizon. Any unsold inventory at the end of the horizon is worthless. The sellers do not have the option of periodically reviewing and replenishing their inventory. We study non-cooperative Nash equilibrium policies for sellers under such a model. This kind of a setup can be used to model pricing of air fares, hotel reservations, bandwidth in communication networks, etc. In this paper we demonstrate our results through some numerical examples.     

Ordering and pricing of service products in an advance sales system with price-dependent demand

This investigation addresses a service inventory control problem in which a firm orders and sells a service which will be used or consumed by customers on a specific future date. The firm sells the product through an advance booking system, aiming to optimize product price to maximize the total expected profit. Considering situations in which product demand is price-dependent and customers with reservations may cancel advance orders, this work develops a continuous-time model to simultaneously determine the order quantity and selling prices. The analytical results reveal that the optimal ordering quantity and prices are derived via closed-form solutions. In addition, sensitivity analysis of the optimal prices with respect to the system parameters is also conducted to illustrate optimal decision characteristics.     

Improved bid prices for choice-based network revenue management

One of the latest developments in network revenue management (RM) is the incorporation of customer purchase behavior via discrete choice models. Many authors presented control policies for the booking process that are expressed in terms of which combination of products to offer at a given point in time and given resource inventories. However, in many implemented RM systems—most notably in the hotel industry—bid price control is being used, and this entails the problem that the recommended combination of products as identified by these policies might not be representable through bid price control. If demand were independent from available product alternatives, an optimal choice of bid prices is to use the marginal value of capacity for each resource in the network. But under dependent demand, this is not necessarily the case. In fact, it seems that these bid prices are typically not restrictive enough and result in buy-down effects.We propose (1) a simple and fast heuristic that iteratively improves on an initial guess for the bid price vector; this first guess could be, for example, dynamic estimates of the marginal value of capacity. Moreover, (2) we demonstrate that using these dynamic marginal capacity values directly as bid prices can lead to significant revenue loss as compared to using our heuristic to improve them. Finally, (3) we investigate numerically how much revenue performance is lost due to the confinement to product combinations that can be represented by a bid price.The heuristic is not restricted to a particular choice model and can be combined with any method that provides us with estimates of the marginal values of capacity. In our numerical experiments, we test the heuristic on some popular networks examples taken from peer literature. We use a multinomial logit choice model which allows customers from different segments to have products in common that they consider to purchase. In most problem instances, our heuristic policy results in significant revenue gains over some currently available alternatives at low computational cost.     

基于线上评论的生鲜产品供应链定价策略

随着网络购物的普及,线上评论对消费者的购买决策发挥着越来越重要的作用,零售商和制造商可以利用这些评论调整、优化生产流程。本文基于消费者效用理论,构建了由一个零售商和一个制造商组成的生鲜产品供应链定价决策模型,研究了三种决策模式下生鲜产品零售价、利润和保鲜努力水平的差异。研究发现:集中决策下生鲜产品的最优零售价最低、利润最高;随着运输时间增加,保鲜努力水平和新鲜度均呈现下降趋势;当成本分担系数较高时,在成本分担和收益共享的分散决策模式下,保鲜努力最优水平高于集中决策模式下保鲜努力最优水平。     

Forecasting cancellation rates for services booking revenue management using data mining

Revenue management (RM) enhances the revenues of a company by means of demand-management decisions. An RM system must take into account the possibility that a booking may be canceled, or that a booked customer may fail to show up at the time of service (no-show). We review the Passenger Name Record data mining based cancellation rate forecasting models proposed in the literature, which mainly address the no-show case. Using a real-world dataset, we illustrate how the set of relevant variables to describe cancellation behavior is very different in different stages of the booking horizon, which not only confirms the dynamic aspect of this problem, but will also help revenue managers better understand the drivers of cancellation. Finally, we examine the performance of the state-of-the-art data mining methods when applied to Passenger Name Record based cancellation rate forecasting.     

考虑平台竞争的众包物流社会配送服务最优定价策略

基于互联网的众包物流服务平台共享社会闲置人力资源,为电子商务O2O提供最后一公里配送服务。通过配送服务的动态定价来调控众包物流的社会配送供应能力,成为优化众包物流平台运营的重要手段。在众包物流平台激烈的竞争环境下,考虑到众包物流社会化配送供应能力的不确定性,采用动态优化理论,建立平台价格竞争下众包物流社会配送服务最优定价模型。运用汉密尔顿函数求解社会配送服务最优价格,研究众包物流社会配送服务供应能力随着价格变化的规律,进一步,分析众包物流平台竞争对最优价格变化规律的影响。数值仿真结果表明,众包物流社会配送服务价格增长率随着平台竞争的加剧而增加,可以有效调控众包物流服务的供应与需求平衡,优化众包物流平台的期望收益。     

Dynamic pricing with uncertain production cost: An alternating-move approach

This article studies a two-firm dynamic pricing model with random production costs. The firms produce the same perishable products over an infinite time horizon when production (or operation) costs are random. In each period, each firm determines its price and production levels based on its current production cost and its opponent’s previous price level. We use an alternating-move game to model this problem and show that there exists a unique subgame perfect Nash equilibrium in production and pricing decisions. We provide a closed-form solution for the firm’s pricing policy. Finally, we study the game in the case of incomplete information, when both or one of the firms do not have access to the current prices charged by their opponents.     

交货期差异下的双渠道订货与协调优化策略

由于顾客往往具有不同交货期的期望,从而导致线上与线下渠道冲突加剧,同时也为其进行合作提供了基础。因此针对该问题,以一个开辟线上渠道的制造商和一个线下零售商组成的双渠道供应链系统为研究对象,分别建立了考虑交货期差异下二者合作时的利润优化模型。在此基础上,首先采用不动点定理证明了制造商开辟的线上渠道与线下零售商订货纳什均衡的存在性与唯一性,并探讨了二者均衡订货量与交货期差异率的关系;其次,证明了仅仅采用批发价格与服务费用契约无法协调制造商与线下零售商组成的供应链系统,进一步优化设计了两部定价协调机制,并实现二者的双赢。最后通过算例证明了纳什均衡的存在性以及订货均衡与交货期差异率的关系。     

Optimal Basket Liquidation for CARA Investors is Deterministic

Abstract

We consider the problem faced by an investor who must liquidate a given basket of assets over a finite time horizon. The investor's goal is to maximize the expected utility of the sales revenues over a class of adaptive strategies. We assume that the investor's utility has constant absolute risk aversion (CARA) and that the asset prices are given by a very general continuous-time, multiasset price impact model. Our main result is that (perhaps surprisingly) the investor does no worse if he narrows his search to deterministic strategies. In the case where the asset prices are given by an extension of the nonlinear price impact model of Almgren [(2003) Applied Mathematical Finance, 10, pp. 1–18], we characterize the unique optimal strategy via the solution of a Hamilton equation and the value function via a nonlinear partial differential equation with singular initial condition.     

Estimating intermediate price transitions in online auctions

This paper discusses a statistical model regarding intermediate price transitions of online auctions. The objective was to characterize the stochastic process by which prices of online auctions evolve and to estimate conditional intermediate price transition probabilities given current price, elapsed auction time, number of competing auctions, and calendar time. Conditions to ensure monotone price transitions in the current price and number of competing auctions are discussed and empirically validated. In particular, we show that over discrete periods, the intermediate price transitions are increasing in the current price, decreasing in the number of ongoing auctions at a diminishing rate, and decreasing over time. These results provide managerial insight into the effect of how online auctions are released and overlap. The proposed model is based on the framework of generalized linear models using a zero‐inflated gamma distribution. Empirical analysis and parameter estimation is based on data from eBay auctions conducted by Dell. Copyright © 2011 John Wiley & Sons, Ltd.     

Electricity pool prices: long-term uncertainty characterization for futures-market trading and risk management

Organized trading for electricity includes both the pool and the futures market. Pool prices are volatile while the prices of the futures-market products are comparatively more stable. Thus, futures-market products constitute hedging instruments to reduce the risk suffered by any market agent. Electricity market agents engage in both pool and futures market transactions seeking to maximize their respective profits/utilities for a given risk level on profit variability. To make informed decisions, the market agent must gather as much accurate information as possible on the pool prices covering the whole time horizon spanned by the futures-market product. This paper provides a novel technique to represent conveniently the uncertainty associated with pool prices during long- or medium-term horizons through a set of scenarios, that is, pool price realizations. The proposed technique uses the prices of the futures-market products as long-term explanatory variables and exploits the short-term structure of the pool prices.     

Short-term electricity procurement: A rolling horizon stochastic programming approach

This paper addresses the problem faced by a large electricity consumer in determining the optimal procurement plan over a short-term time horizon. The inherent complexity of the problem, due to its dynamic and stochastic nature, is dealt by means of the stochastic programming modeling framework. In particular, a two-stage problem is formulated with the aim of establishing the optimal amount of electricity to be purchased through bilateral contracts and in the Day-Ahead Electricity Market. Recourse actions are used to hedge against uncertainty related to future electricity prices and consumer’s needs. The optimal plan is defined so to minimize the overall cost and to control risk, which is measured in the form of violation of budget constraints. The stochastic model is dynamically solved in a rolling horizon fashion by iteratively considering more and more recent information and a planning horizon of decreasing length. Extensive numerical experiments have been carried out to assess the performance of the proposed dynamic decision approach. The results collected considering a real test case are very encouraging and provide evidence of the superiority of the approach also in comparison with other alternative procurement strategies.