Network revenue management with inventory-sensitive bid prices and customer choice

We develop an approximate dynamic programming approach to network revenue management models with customer choice that approximates the value function of the Markov decision process with a non-linear function which is separable across resource inventory levels. This approximation can exhibit significantly improved accuracy compared to currently available methods. It further allows for arbitrary aggregation of inventory units and thereby reduction of computational workload, yields upper bounds on the optimal expected revenue that are provably at least as tight as those obtained from previous approaches. Computational experiments for the multinomial logit choice model with distinct consideration sets show that policies derived from our approach can outperform some recently proposed alternatives, and we demonstrate how aggregation can be used to balance solution quality and runtime.     

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.     

Comparing improvement strategies for inventory inaccuracy in a two-echelon supply chain

Classical inventory theory often assumes no discrepancies between recorded inventory and actual inventory. However, inventory records are usually inaccurate due to many reasons in practice. For example, inventory shrinkage refers to the difference between booked inventory that a company should have as a result of its sales, purchasing, and manufacturing processes and actual inventory that it has on hand. This paper concerns the impact of inventory shrinkage to a supply chain and investigates different strategies to deal with inventory shrinkage. We consider a single-period two-echelon supply chain consisting of a Stackelberg manufacturer and a retailer whose inventory is subject to shrinkage errors. Our analysis is based on a single-period newsvendor model and considers the following cases of order decisions: (a) the retailer ignores the inventory errors; (b) the retailer estimates the errors; (c) the retailer shares the inventory error information with the manufacturer; and (d) RFID technology is used to reduce or eliminate the errors. The first case is the base strategy and a common practice for many supply chains, the other two represent certain non-technological strategies of the retailer when dealing with inventory inaccuracy, and the last one represents a technology improvement strategy by the manufacturer to reduce or eliminate inventory shrinkage errors. We compare these improvement strategies and derive critical tag price for RFID implementation as a technological remedy for the inventory inaccuracy problem. Conditions for the profitability of RFID adoption are discussed.     

Integrated revenue management approaches for capacity control with planned upgrades

In many service industries, firms offer a portfolio of similar products based on different types of resources. Mismatches between demand and capacity can therefore often be managed by using product upgrades. Clearly, it is desirable to consider this possibility in the revenue management systems that are used to decide on the acceptance of requests. To incorporate upgrades, we build upon different dynamic programming formulations from the literature and gain several new structural insights that facilitate the control process under certain conditions. We then propose two dynamic programming decomposition approaches that extend the traditional decomposition for capacity control by simultaneously considering upgrades as well as capacity control decisions. While the first approach is specifically suited for the multi-day capacity control problem faced, for example, by hotels and car rental companies, the second one is more general and can be applied in arbitrary network revenue management settings that allow upgrading. Both approaches are formally derived and analytically related to each other. It is shown that they give tighter upper bounds on the optimal solution of the original dynamic program than the well-known deterministic linear program. Using data from a major car rental company, we perform computational experiments that show that the proposed approaches are tractable for real-world problem sizes and outperform those disaggregated, successive planning approaches that are used in revenue management practice today.     

Quantum Secret Sharing with Error Correction

We investigate in this work a quantum error correction on a five-qubits graph state used for secret sharing through five noisy channels. We describe the procedure for the five, seven and nine qubits codes. It is known that the three codes always allow error recovery if only one among the sent qubits is disturbed in the transmitting channel. However, if two qubits and more are disturbed, then the correction will depend on the used code. We compare in this paper the three codes by computing the average fidelity between the sent secret and that measured by the receivers. We will treat the case where, at most, two qubits are affected in each one of five depolarizing channels.     

基于混合产品提供的后市场知识共享激励机制设计研究

制造企业对混合产品专有知识出于盈利与保护目的封锁行为,将使得供需之间产生知识缺口。在厘清缺口形成原因的基础上,分别构建了不同信息条件及不同风险偏好下的供需知识共享激励模型。研究结果表明:在信息对称的条件下,供需双方可达到帕累托最优,客户可通过买断知识产权或建立知识联盟共享企业混合产品的专有知识;而信息不对称的情形下,制造企业采取知识授权的形式进行共享,客户提高收益分配比例对保护性知识的共享边际作用递增,而对盈利性知识的共享边际作用保持不变;考虑知识泄露的风险,仿真发现制造企业愿意承担的风险比例对保护性知识的共享成本系数的敏感性要高于盈利性知识。     

考虑平台影响因素的虚拟社区知识共享演化博弈研究

知识共享是虚拟社区用户学习和传播知识的重要途径。本文考虑到虚拟社区平台对参与用户知识共享行为的激励作用,引入平台的奖励和不分享知识所产生的机会成本,以及知识共享收益因素,构建考虑虚拟社区平台影响因素的虚拟社区知识共享演化博弈支付矩阵,并求得演化均衡解。通过小米社区的案例引入,仿真模拟平台对用户知识共享的奖励、机会成本、收益分配系数以及参与用户共享程度等因素对参与用户知识共享行为意愿的影响。研究发现:虚拟社区参与用户的知识共享意愿对因为知识共享所产生新知识的收益系数变化较为敏感;平台的奖励以及不分享知识所产生的机会成本对用户知识共享意愿影响巨大;同时,不同参与用户因为知识水平层次不同,参与知识共享的目的也不相同,这会影响到他们的知识共享策略选择。     

Asymptotic revenue equivalence of asymmetric auctions with interdependent values

We prove an asymptotic revenue equivalence among weakly asymmetric auctions with interdependent values, in which bidders have either asymmetric utility functions or asymmetric distributions of signals.     

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.     

Dynamic cruise ship revenue management

Recently, it has been recognized that revenue management of cruise ships is different from that of airlines or hotels. Among the main differences is the presence of multiple capacity constraints in cruise ships, i.e., the number of cabins in different categories and the number of lifeboat seats, versus a single constraint in airlines and hotels (i.e., number of seats or rooms). We develop a discrete-time dynamic capacity control model for a cruise ship characterized by multiple constraints on cabin and lifeboat capacities. Customers (families) arrive sequentially according to a stochastic process and request one cabin of a certain category and one or more lifeboat seats. The cruise ship revenue manager decides which requests to accept based on the remaining cabin and lifeboat capacities at the time of an arrival as well as the type of the arrival. We show that the opportunity cost of accepting a customer is not always monotone in the reservation levels or time. This non-monotone behavior implies that “conventional” booking limits or critical time periods capacity control policies are not optimal. We provide analysis and insights justifying the non-monotone behavior in our cruise ship context. In the absence of monotonicity, and with the optimal solution requiring heavy storage for “large” (industry-size) problems, we develop several heuristics and thoroughly test their performance, via simulation, against the optimal solution, well-crafted upper bounds, and a first-come first-served lower bound. Our heuristics are based on rolling-up the multi-dimensional state space into one or two dimensions and solving the resulting dynamic program (DP). This is a strength of our approach since our DP-based heuristics are easy to understand, solve and analyze. We find that single-dimensional heuristics based on decoupling the cabins and lifeboat problems perform quite well in most cases.