Forecasting market prices in a supply chain game

Predicting the uncertain and dynamic future of market conditions on the supply chain, as reflected in prices, is an essential component of effective operational decision-making. We present and evaluate methods used by our agent, Deep Maize, to forecast market prices in the trading agent competition supply chain management game (TAC/SCM). We employ a variety of machine learning and representational techniques to exploit as many types of information as possible, integrating well-known methods in novel ways. We evaluate these techniques through controlled experiments as well as performance in both the main TAC/SCM tournament and supplementary Prediction Challenge. Our prediction methods demonstrate strong performance in controlled experiments and achieved the best overall score in the Prediction Challenge.     

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.

     

Pricing and production decisions in dual-channel supply chains with demand disruptions

This paper develops a two-period pricing and production decision model in a one- manufacturer-one-retailer dual-channel supply chain that experiences a disruption in demand during the planning horizon. While disruption management has long been a key research issue in supply chain management, little attention has been given to disruption management in a dual-channel supply chain once the original production plan has been made. Generally, changes to the original production plan induced by a disruption may impose considerable deviation costs throughout the supply chain system. In this paper, we examine how to adjust the prices and the production plan so that the potential maximal profit is obtained under a disruption scenario. We first study the scenario where the manufacturer and the retailer are vertically integrated with demand disruptions. Then we further assume that the manufacturer bears the deviation costs and obtain the manufacturer’s and the retailer’s individual optimal pricing decision, as well as the manufacturer’s optimal production quantity in a decentralized decision-making setting. We derive conditions under which the maximum profit can be achieved. The results indicate that the optimal production quantity has some robustness under a demand disruption, in both centralized and decentralized dual-channel supply chains. We also find that the optimal pricing decisions are affected by customers’ preference for the direct channel and the market scale change, in both centralized and decentralized dual-channel supply chains.     

Pricing strategy in a dual-channel and remanufacturing supply chain system

This article addresses the pricing strategy problems in a supply chain system where the manufacturer sells original products and remanufactured products via indirect retailer channels and direct Internet channels. Due to the complexity of that system, agent technologies that provide a new way for analysing complex systems are used for modelling. Meanwhile, in order to reduce the computational load of searching procedure for optimal prices and profits, a learning search algorithm is designed and implemented within the multi-agent supply chain model. The simulation results show that the proposed model can find out optimal prices of original products and remanufactured products in both channels, which lead to optimal profits of the manufacturer and the retailer. It is also found that the optimal profits are increased by introducing direct channel and remanufacturing. Furthermore, the effect of customer preference, direct channel cost and remanufactured unit cost on optimal prices and profits are examined.     

Optimal pricing decisions in a fuzzy dual-channel supply chain

This paper analyzes the pricing decisions of a dual-channel supply chain including one retailer and one manufacturer who produces a product and sells it to the end customer through retailer or directly. Both the manufacturing cost and the customer demand are considered as fuzzy variables. Two pricing models, including centralized decision model and manufacturer-leader Stackelberg game, with consideration of different market power structures are adopted. Optimal decisions on wholesale price and retail prices are determined using game theoretical approach and fuzzy set theory for each model. Finally, a numerical example is solved to illustrate the effectiveness of models and provide some managerial insights from analysis.     

Learning approaches for developing successful seller strategies in dynamic supply chain management

Variable, dynamic pricing is a key characteristic of the modern electronic trading environments, allowing for prices that change or fluctuate due to uncertainty and different conditions and context. Being able to manage dynamic pricing strategies is vital for companies wishing to succeed in the world of modern business. The ability to accurately predict selling prices at a given time can help organizations to maximize their profit. This paper addresses the problem of predicting customer order prices and choosing the selling strategy which can lead to a greater profit in the context of supply chain management (SCM). The potential of the Neural Networks (NN) and Genetic Programming (GP) learning techniques is explored for making price forecasts. In particular, different parameter settings and methods for preprocessing input data are investigated in the paper. Although, both techniques showed the potential for dealing with the problem of dynamic pricing in SCM, NN models outperform GP models in the context under consideration in terms of accuracy of prediction, complexity of implementation, and execution time.     

考虑服务的零售商渠道选择与定价策略

面对强势制造商控制网络直销渠道和批发价格，零售商在此不利局面下如何改善自身绩效，针对该问题，构建了零售商提高服务水平和开辟网络渠道的最优定价决策模型。在集中式和Stackelberg分散式两种供应链情形下，研究了零售商开辟和未开辟网络渠道下服务水平对定价策略和各成员利润的影响。研究表明，零售商提高服务水平有利于零售商和制造商以及整个供应链的绩效；开辟网络渠道会提升零售价格，增加零售商和制造商以及整个供应链的利润。     

基于Agent面向软件重用的敏捷供应链模型

敏捷性和快速响应性是现代企业成功的关键因素，供应链管理正成为现代企业提高竞争力而采取的有效的先进手段，它要求敏捷化的信息系统支持，满足企业快速构造供应链的需要，因此，如何实现供应链系统的软件重用，就具有非常重要的现实意义，提出了将企业的商务逻辑与具体事务处理分开的思想，建立基于agent的敏捷化供应链管理模型，通过对商务逻辑的规则模型和软件代理的活动行为模型建立，利用ECA规则协调多代理系统的活动，从而有效地支持供应链动态建立，过程重组，增强供应链系统信息分布处理，软件可重用和规模可扩展能力。     

Supply chain pricing and coordination with markdown strategy in the presence of conspicuous consumers

This paper studies the problem of supply chain pricing and coordination with markdown policy in the presence of conspicuous consumption. A two‐period pricing model is proposed to investigate the effect of conspicuous consumption on pricing decisions with markdown pricing policy for a supply chain that consists of a manufacturer and retailer. The optimal pricing strategies for the integrated supply chain and decentralized supply chain are derived respectively. Our results show that an anticipated conspicuous consumption can benefit the supply chain even though the conspicuous consumption aggravates the double marginalization effect. The conspicuous consumption can serve as a level to segment the market and can promote the supply chain revenue. In addition, our results indicate that the supply chain can benefit from rationing. We also introduce a revenue‐sharing contract and prove that it can be applied to coordinate the supply chain with conspicuous consumption. An interval of the revenue‐sharing rate for coordinating with the supply chain can be coordinated without any additional transfer payment being provided.     

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.     

CMieux: Adaptive strategies for competitive supply chain trading

Supply chains are a central element of today’s global economy. Existing management practices consist primarily of static interactions between established partners. Global competition, shorter product life cycles and the emergence of Internet-mediated business solutions create an incentive for exploring more dynamic supply chain practices. The supply chain trading agent competition (TAC SCM) was designed to explore approaches to dynamic supply chain trading between automated software agents. TAC SCM pits trading agents developed by teams from around the world against one another. Each agent is responsible for running the procurement, planning and bidding operations of a PC assembly company, while competing with others for both customer orders and supplies under varying market conditions. This paper presents Carnegie Mellon University’s 2005 TAC SCM entry, the CMieux supply chain trading agent. CMieux implements a novel approach for coordinating supply chain bidding, procurement and planning, with an emphasis on the ability to rapidly adapt to changing market conditions. We present empirical results based on 200 games involving agents entered by 25 different teams during what can be seen as the most competitive phase of the 2005 tournament. Not only did CMieux perform among the top five agents, it significantly outperformed these agents in procurement while matching their bidding performance. We also simulated 40 games against the best publicly available agent binaries. Our results show CMieux has significantly better average overall performance than any of these agents.     

Pricing with local interactions on agent-based electronic marketplaces

This paper investigates the optimal pricing strategies of a selling agent that is randomly matched with several heterogeneous buying agents whose reservation prices are initially unknown. The seller perceives the behaviors of the buying agents through a logistic distribution with unknown parameters. We study the optimal learning by experimentation model of the logistic distribution. We extend this framework to a dynamic pricing model in which the selling agent is randomly matched with buying agents that are able to communicate their purchase experience to other buying agents. We carry out multi-agent system simulations of this dynamic pricing decision problem and we discuss some properties of the price dynamics one can observe on such marketplaces.     

基于市场竞标机制的网格资源管理模型

网格资源管理是网格计算首要解决的核心问题。现行的网格资源管理模型通常需要在网格系统中建立一个高于其他节点的自身庞大的管理体系,以掌握全网格内资源动态信息,实现全局资源调配,这种模式的主要问题是管理成本高且资源配置效率低。文章基于市场经济中的招投标机理,提出了一种新的网格资源管理模型,本模型的资源分配机制是根据网格内资源供需关系和资源的市场价格通过招投标方式来决定。网格内站点是否参与网格计算交易活动,完全由站点自主地根据自身资源现状和资源的市场价格来确定而不需要一个高于自己的管理机构,本模型是一个完全的市场经济的资源分配模型。     

Dynamic Model for the Simulation of Equilibrium Status in the Land Use Market

This paper presents a dynamic equilibrium model for the real estate market. Households have stochastic behavior and compete for quasi-unique locations (real estate goods), which are assigned to the best bidder through an auction-type mechanism. The producers are modeled as maximizers of their profits over the long-term through the production of real estate assets, represented by the present value of future sales. It is assumed that the producers do not possess complete information about future levels of demand or prices. Rather, it is assumed that producers are myopic, meaning that they take the actual and historic prices in each period as the relevant information for their decision-making. A notion of equilibrium is used that adjusts prices given two situations: supply and demand surplus. In the supply surplus case, the prices are diminished and supply in the market is reduced until supply equals demand. In the case of demand surplus, the prices rise and demand diminishes (homeless households) until demand equals supply. This equilibrium condition yields prices that are jumpy over time, resembling observations of inventories in the real estate market and the manufacture industry.     

IaaS云服务定价机制的仿真研究

定价机制是影响云计算用户利益和云服务提供商收益的关键因素。本文以IaaS云服务定价机制为研究对象,首先将IaaS云服务定价机制分为固定定价和动态定价2大类,其中固定定价细分为即用即付费和预订定价2种,从理论上分析2类定价机制对IaaS云服务提供商收益的影响;然后,分别建立即用即付费和预订定价机制下IaaS云服务提供商的收益模型,并用启发式算法构建动态定价机制下IaaS云服务提供商的收益模型;最后,基于Repast Simphony建立多主体仿真模型,比较分析3种不同定价机制下IaaS云服务提供商的收益。研究结果表明,随着服务时长和顾客数量的增加,动态定价机制更能为IaaS云服务提供商带来更多收益,同时IaaS云服务提供商可以通过改变服务等级和资源价格等相关参数,改变顾客行为,使顾客数量增加,从而提高收益。     

基于消费者时间偏好和决策者风险偏好的定价与订货策略*

在供应链管理过程中, 消费者时间偏好和决策者风险偏好是影响产品定价与订货决策的两个重要因素。本文以累积前景理论为框架, 将消费者时间偏好与价格依赖等影响产品市场需求的因素和决策者风险偏好与参考依赖等影响决策的因素共同纳入模型考量范围, 建立了基于消费者的时间偏好和决策者风险偏好的定价与订货模型。研究表明：随着消费者时间偏好的增大, 产品的最优定价和订货量都将减少;随着决策者心理参考点的增大, 产品的最优定价降低,最优订货量增加;随着决策者损失规避程度的增大, 产品最优定价增加, 而最优订货量减少。考虑消费者时间偏好和决策者的风险偏好的定价与订货模型能够提高供应链中决策者的最大累积前景效用。     

零售商定价/订货决策下竞争/互补制造商采用RFID的策略研究

以一个零售商主导的两级供应链为研究对象,构建两个生产竞争性或互补性产品的制造商是否采用无线射频识别技术(radio frequency identification,RFID)时链上成员的收益模型,分析求解出各种情景下零售商定价/订货决策时制造商的最优批发价和RFID标签成本分摊系数,以及零售商的最优零售价/订货量,进...     

Multi-agent simulation for the dominant players’ behavior in supply chains

This paper describes a multi-agent simulation model for analyzing the dominant player’s behavior of supply chains. The supply chain consists of a raw material supplier and component supplier, a manufacturer and retailer are simulated by the model. The main sources of uncertainty for the dominant players’ behavior are identified, including of market information, selling prices, purchasing prices. The economic phenomenon (Players can maintain their profits stably in spite of decreasing the selling price) is shown by the model, and the SPP (Stable Profit Platform) is used to indicate the level of domination power for the player achievement. Moreover, the model can also be extended to various supply chain for better understanding of the dominant players’ behavior and improving the effective decision making of supply chain cooperation.     

Dynamic pricing and channel efficiency in the presence of the cost learning effect

This paper investigates the impacts of the supply‐side cost learning effect on dynamic pricing strategies and the channel efficiency in a decentralized supply chain consisting of a manufacturer and a retailer. The unit production cost incurred by the manufacturer declines as the cumulative output increases. With the manufacturer being the price leader, the interactions between the two channel members are modeled as a Stackelberg differential game, where the two channel members dynamically set the wholesale and retail prices over time to optimize their respective profits. We find that while the cost learning effect is beneficial to both channel members in terms of long‐term profitability, such an effect is detrimental to the channel inefficiency caused by price double marginalization. In a setting where the manufacturer sells through competing retailers, we show that the channel efficiency can be improved with the emergence of horizontal competition. Our result also indicates that the channel efficiency with a feedback pricing equilibrium is higher than that with an open‐loop equilibrium.