Preliminary Insights into Optimal Pricing and Space Allocation at Intermodal Terminals with Elastic Arrivals and Capacity Constraint

This paper discusses derivations, and implications of, formulae to compute optimal space allocation and pricing for storage at container terminals. The case discussed in the paper considers elastic arrivals and container dwelling times, which is a more general version of the case considered by the authors in their first publication on the subject. In general terms, the optimal prices have three components that capture the different facets of the process. The first element captures the combined effect of willingness to pay and marginal cost, i.e., the classic solution after Ramsey (1927). The second element represents the contribution of the capacity constraint, i.e., the element introduced in Holguín-Veras and Jara-Díaz (1999). The third element captures the role of elastic arrivals and represents the main contribution of this paper. The role of the cost structure is also analyzed.     

量子粒子群算法在易逝品多目标定价中的应用

对易逝品的多目标定价问题进行了研究。从利润最大化角度建立易逝品多目标最优定价模型。模型中涉及复杂的需求函数,常规函数极值法不易获得问题解析解,因此引入量子粒子群算法,结合惩罚函数对模型进行演化求解。根据给出的算例分析表明,利用量子粒子群算法,可以快速有效地得到不同订货量下的最优定价与折扣价组合。     

基于竞价的租赁车辆资源分配和定价机制

针对目前线上汽车租赁平台使用固定价格的方式来租赁车辆给用户,存在着租赁车辆分配不合理、定价不能及时反映真实的市场供需情况、整体社会福利较低等问题,提出一种基于竞价的租赁车辆分配与定价机制。首先,研究了线上汽车租赁问题模型,抽象出数学模型与社会福利最大目标函数;然后,在租赁车辆分配算法中采用基于最小费用最大流算法的最优化车辆资源分配算法;最后,在价格支付算法中采用了可信的VCG价格算法,计算出用户最终的支付价格。对比传统的先来先服务算法,基于竞价的租赁车辆分配与定价机制在订单成功率方面提升了20%~30%,在平台收益方面增加了30%左右。实验结果表明,所提出的机制具有车辆分配最优化、弹性价格的优势。     

基于可变连接价格的TCP接入控制研究

本文面向有连接的弹性应用,探讨了基于可变连接价格的TCP接入控制问题.将该问题描述为追求连接阻塞率最小化和活动连接时长最大化的多目标优化问题,分别考察了在连接请求确定到达和随机到达时的连接价格确定问题.提出了在阻塞率不高于某一设定值的情况下,使得单位时间内期望活动连接时长最大化的连接价格调整算法,并给出了基于Socks代理协议的价格协商和接入控制实现方法.     

Computational pricing in Internet era

Pricing plays a central rule to a company’s profitability, and therefore has been extensively studied in the literature of economics. When designing a pricing mechanism/ model, an important principle to consider is “price discrimination”, which refers to selling the same resources with different prices according to different values of buyers. To meet the “price discrimination” principle, especially when the number of buyers is large, computational methods, which act in a more accurate and principled way, are usually needed to determine the optimal allocation of sellers’ resources (^{whom to sell to}) and the optimal payment of buyers (^{what to charge}). Nowadays, in the Internet era in which quite a lot of buy and sell processes are conducted through Internet, the design of computational pricing models faces both new challenges and opportunities, considering that (i) nearly realtime interactions between people enable the buyers to reveal their needs and enable the sellers to expose their information in a more expressive manner, (ii) the large-scale interaction data require powerful methods for more efficient processing and enable the sellers to model different buyers in a more precise manner. In this paper, we review recent advances on the analysis and design of computational pricing models for representative Internet industries, e.g., online advertising and cloud computing. In particular, we introduce how computational approaches can be used to analyze buyer’s behaviors (i.e., equilibrium analysis), improve resource utilization (i.e., social welfare analysis), and boost seller’s profit (i.e., revenue analysis). We also discuss how machine learning techniques can be used to better understand buyer’s behaviors and design more effective pricing mechanisms, given the availability of large scale data. Moreover, we make discussions on future research directions on computational pricing, which hopefully can inspire more researchers to contribute to this important domain.     

一种基于RAN架构无线接入网系统容量最大化的功率分配算法

针对新型RAN架构无线接入网中功率分配问题进行研究, 建立系统容量最大化的最优化数学模型, 提出了一种基于RAN架构无线接入网的系统容量最大化的功率分配算法, 并且应用人工鱼群算法求解了算法中目标函数的全局最优解, 即一组使系统容量最大化的发射功率。仿真结果表明, 与现有的分布式网络架构中非合作的功率控制博弈算法相比, 新算法可以显著提高系统容量, 即证明了该算法的有效性。     

The Power of Fair Pricing Mechanisms

We explore the revenue capabilities of truthful, monotone (“fair”) allocation and pricing functions for resource-constrained auction mechanisms within a general framework that encompasses unlimited supply auctions, knapsack auctions, and auctions with general non-decreasing convex production cost functions. We study and compare the revenue obtainable in each fair pricing scheme to the profit obtained by the ideal omniscient multi-price auction. We show that for capacitated knapsack auctions, no constant pricing scheme can achieve any approximation to the optimal profit, but proportional pricing is as powerful as general monotone pricing. In addition, for auction settings with arbitrary bounded non-decreasing convex production cost functions, we present a proportional pricing mechanism which achieves a poly-logarithmic approximation. Unlike existing approaches, all of our mechanisms have fair (monotone) prices, and all of our competitive analysis is with respect to the optimal profit extraction.     

Web Based Capacity Allocation Strategies for Customers with Heterogeneous Preferences

We study different mechanisms for the pricing and allocation of capacity to customers with heterogeneous and unknown preferences. The mechanisms we study include posting of prices, auctions, with and without the possibility of resale. We compare the performance of each mechanism with the optimal selling procedure in terms of expected seller's profit and efficiency. We also discuss the feasibility of web based implementation.     

Game and Dynamic Communication Path-Based Pricing Strategies for Microgrids Under Communication Interruption

Nowadays,the microgrid cluster is an important application scenario for energy trading.In trading,one of the most important research directions is the issue of pricing.To determine reasonable pricing for the microgrid cluster,data communication is used to create the cyber-physical system(CPS),which can improve the observability of microgrids.Then,the following works are carried out in the CPS.In the physical layer:1) Regarding trading between microgrids and the load,based on the generalized game...     

能量收集无线网络中面向统计服务质量的最优功率分配

针对能量收集无线网络中的服务质量（QoS）保障问题,提出了面向统计QoS的最优功率分配方案,从而最大化时延QoS约束下的有效容量。首先,构建能量收集无线网络的有效容量最大化问题。其次,利用凸优化理论求解出最优功率分配方案以及该方案下的最大有效容量,并得出了两种特例下的最优功率分配方案,即：当QoS需求非常松弛时,最优化功率分配方案收敛于注水策略;当QoS需求非常严格时,最优化功率分配方案收敛于信道反转策略。然后,求解了所提最优功率分配方案得到的信道中断概率。最后,通过仿真实验,检验了该最优功率分配方案的性能。实验结果表明,与现有策略相比,所提方案能够获得更大的有效容量。     

Price differentiation and control in the Kelly mechanism

The design and implementation of resource allocation and pricing for computing and network resources are crucial for system and user performance. Among various designing objectives, we target on maximizing the social welfare, i.e., the summation of all user utilities. The challenge comes from the fact that users are autonomous and their utilities are unknown to the system designer. Under the Kelly mechanism, users bid and proportionally share resources. When user population is large and “price-taking” can be assumed, the Kelly mechanism maximizes the social welfare; however, under oligopolistic competitions, this mechanism might induce an efficiency loss up to 25% of the welfare optimum.     

公平视角下全渠道服务商定价和服务能力分配策略

随着移动互联网的发展,基于本地服务的全渠道运营模式被广大服务运营商和消费者所接受.对于有限服务能力的服务商在全渠道运营的过程中,需要考虑其服务能力在不同渠道中的公平分配.针对同时运营实体和O2O渠道的服务商进行研究,探索考虑公平的服务商不同分配机制下的渠道定价及分配策略的选择.通过消费者效用模型的构建,刻画消费者的渠道...     

The effects of decision timing for pricing and marketing efforts in a supply chain with competing manufacturers

This paper investigates the impact of decision timing for pricing and marketing efforts in a supply chain led by competing manufacturers. We develop and solve six games to consider the scenarios (games) where prices and marketing efforts (ME) are decided simultaneously, and when they are not (i.e., ME is set either before or after prices). We examine these three scenarios for the benchmark case of a bilateral monopolistic channel, then extend the analysis to a supply chain with competing manufacturers. We identify the optimal decision timing by comparing equilibrium profits and strategies across games in each supply chain setup. We find that a monopolistic manufacturer always prefers that prices and ME be decided simultaneously. However, this result does not hold when product competition is taken into account. The optimal decision timing for competing manufacturers depends on the retailer's and manufacturers' ME effectiveness levels as well as on competition intensity. Specifically, when ME are not very effective, a simultaneous decision scenario is preferred because it provides the advantage of higher profit margins or sales. However, for highly effective ME, manufacturers prefer to decouple ME and pricing decisions. The retailer's optimal scenario is either to make all decisions simultaneously or to choose prices prior to ME. This means that supply chain firms can face conflict due to the decision timing for prices and ME.     

需求依赖于产品价格和消费者时间偏好的定价与订货决策

研究产品的市场需求依赖于价格和消费者时间偏好情形下的供应链及其成员的最优定价与订货问题. 利用时间偏好因子刻画消费者的时间偏好, 利用产品市场需求关于零售价格的弹性指数反映消费者的价格敏感程度, 建立存在强势零售商、强势供应商和供需双方势力均衡3 种渠道权力结构下的定价与订货模型. 数值结果表明, 在3 种渠道权力结构下, 消费者的时间偏好和产品市场需求关于零售价格的弹性指数是供应链主导者最优定价、订货量和期望利润的单调减函数, 将其纳入定价与订货决策的影响因素予以考量能够增加供应链主导者的最大期望利润.

     

Price-Based Residential Demand Response Management in Smart Grids: A Reinforcement Learning-Based Approach

This paper studies price-based residential demand response management (PB-RDRM) in smart grids, in which non-dispatchable and dispatchable loads (including general loads and plug-in electric vehicles (PEVs)) are both involved. The PB-RDRM is composed of a bi-level optimization problem, in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company (UC) by selecting optimal retail prices (RPs), while the lower-level demand response (DR) problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior. The challenges here are mainly two-fold: 1) the uncertainty of energy consumption and RPs; 2) the flexible PEVs’ temporally coupled constraints, which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM. To address these challenges, we first model the dynamic retail pricing problem as a Markovian decision process (MDP), and then employ a model-free reinforcement learning (RL) algorithm to learn the optimal dynamic RPs of UC according to the loads’ responses. Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches (i.e., distributed dual decomposition-based (DDB) method and distributed primal-dual interior (PDI)-based method), which require exact load and electricity price models. The comparison results show that, compared with the benchmark solutions, our proposed algorithm can not only adaptively decide the RPs through on-line learning processes, but also achieve larger social welfare within an unknown electricity market environment.      

Buffer allocation in flow-shop-type production systems with general arrival and service patterns

This study investigates the buffer allocation strategy of a flow-shop-type production system that possesses a given total amount of buffers and finite buffer capacity for each workstation as well as general interarrival and service times in order to optimize such system performances as minimizing work-in-process, cycle time and blocking probability, maximizing throughput, or their combinations. In theory, the buffer allocation problem is in itself a difficult NP-hard combinatorial optimization problem, it is made even more difficult by the fact that the objective function is not obtainable in closed form for interrelating the integer decision variables (i.e., buffer sizes) and the performance measures of the system. Therefore, the purpose of this paper is to present an effective design methodology for buffer allocation in the production system. Our design methodology uses a dynamic programming process along with the embedded approximate analytic procedure for computing system performance measures under a certain allocation strategy. Numerical experiments show that our design methodology can quickly and quite precisely seek out the optimal or sub-optimal allocation strategy for most production system patterns.Scope and purposeBuffer allocation is an important, yet intriguingly difficult issue in physical layout and location planning for production systems with finite floor space. Adequate allocation and placement of available buffers among workstations could help to reduce work-in-process, alleviate production system's congestion and even blocking, and smooth products manufacturing flow. In view of the problem complexity, we focus on flow-shop-type production systems with general arrival and service patterns as well as finite buffer capacity. The flow-shop-type lines, which usually involve with product-based layout, play an important role in mass production type of manufacturing process organization such as transfer line, batch flow line, etc. The purpose of this paper is to present a design methodology with heuristic search and imbedded analytic algorithm of system performances for obtaining the optimal or sub-optimal buffer allocation strategy. Successful use of this design methodology would improve the production efficiency and effectiveness of flow-shop-type production systems.     

易腐商品定价模型及其粒子群解法

论文研究了易腐商品的定价问题,并基于随机需求分布假设和利润最大化原则提出了一种新的易腐商品最优定价模型。多个随机分布的存在使该模型比传统模型更加复杂,因此难以使用常规函数极值法获得解析解,论文尝试将粒子群优化算法引入该模型进行演化求解。最后给出的算例分析表明:该模型和算法可以快速有效地解决随机需求情况下的易腐商品最优定价问题。     

Utility maximization for communication networks with multipath routing

In this paper, we study utility maximization problems for communication networks where each user (or class) can have multiple alternative paths through the network. This type of multi-path utility maximization problems appear naturally in several resource allocation problems in communication networks, such as the multi-path flow control problem, the optimal quality-of-service (QoS) routing problem, and the optimal network pricing problem. We develop a distributed solution to this problem that is amenable to online implementation. We analyze the convergence of our algorithm in both continuous-time and discrete-time, and with and without measurement noise. These analyses provide us with guidelines on how to choose the parameters of the algorithm to ensure efficient network control.     

Transmission pricing and recovery of investment costs in the deregulated power system based on optimal circuit prices

Transmission pricing has become a major issue in the discussions about the deregulated electricity markets.Consequently,open access to the transmission system is one of the basic topics to allow competition among participants in the energy market.Transmission costs have an important impact on relative competition among participants in the energy market as well as on short-and long-term economic efficiencies of the whole electricity industry,although they represent only close to 10% of the energy market price.This paper deals with the design and tests of a transmission pricing method based on the optimal circuit prices derived from the economically adapted network(EAN).Prices derived from the EAN have the advantage of being in tune with the maximum revenue allowed to the owner of transmission assets and simplifying the optimal allocation of transmission costs among participants.Beginning from the conceptual design,the proposed method is tested on a three-bus network and on the IEEE 24-bus reliability test system.