Absolute optimal solution for a compact and convex game

This paper investigates the existence of absolute optimal solutions for a partition P in continuous and quasiconcave games. We show that the P-consistency property introduced in the paper, together with the quasiconcavity and continuity of payoffs, permits the existence of P-absolute optimal solutions in games with compact and convex strategy spaces. The P-consistency property is a general condition that cannot be dispensed with for the existence of P-absolute optimal solutions. We also characterize the existence of P-absolute optimal solutions by providing necessary and sufficient conditions. Moreover, we suggest an algorithm for efficiently computing P-absolute optimal solutions.     

Optimal selection of retailers for a manufacturing vendor in a vendor managed inventory system

A Vendor Managed Inventory (VMI) system consists of a manufacturing vendor and a number of retailers. In such a system, it is essential for the vendor to optimally determine retailer selection and other related decisions, such as the product’s replenishment cycle time and the wholesale price, in order to maximize his profit. Meanwhile, each retailer’s decisions on her willingness to enter the system and retail price are simultaneously considered in the retailer selection process. However, the above interactive decision making is complex and the available studies on interactive retailer selection are scarce. In this study, we formulate the retailer selection problem as a Stackelberg game model to help the manufacturer, as a vendor, optimally select his retailers to form a VMI system. This model is non-linear, mixed-integer, game-theoretic, and analytically intractable. Therefore, we further develop a hybrid algorithm for effectively and efficiently solving the developed model. The hybrid algorithm combines dynamic programming (DP), genetic algorithm (GA) and analytical methods. As demonstrated by our numerical studies, the optimal retailer selection can increase the manufacturer’s profit by up to 90% and the selected retailers’ profits significantly compared to non-selection strategy. The proposed hybrid algorithm can solve the model within a minute for a problem with 100 candidate retailers, whereas a pure GA has to take more than 1 h to solve a small sized problem of 20 candidate retailers achieving an objective value no worse than that obtained by the hybrid algorithm.     

Non-cooperative joint replenishment under asymmetric information

We consider jointly replenishing n ex-ante identical firms that operate under an EOQ like setting using a non-cooperative game under asymmetric information. In this game, each firm, upon being privately informed about its demand rate (or inventory cost rate), submits a private contribution to an intermediary that specifies how much it is willing to pay for its replenishment per unit of time and the intermediary determines the maximum feasible frequency for the joint orders that would finance the fixed replenishment cost. We show that a Bayesian Nash equilibrium exists and characterize the equilibrium in this game. We also show that the contributions are monotone increasing in each firm’s type. We finally conduct a numerical study to compare the equilibrium to solutions obtained under independent and cooperative ordering, and under full information. The results show that while information asymmetry eliminates free-riding in the contributions game, the resulting aggregate contributions are not as high as under full information, leading to higher aggregate costs.     

Variational Inequalities for Combined Control and Stopping Game

Abstract

We study a nonlinear elliptic variational inequality associated with the combined stochastic control problem. By using the dynamic programming principle and the method of penalization, we establish the existence of a unique viscosity solution of the variational inequality and describe it as the value function of the corresponding combined stochastic game problem.     

Partially Observable Semi-Markov Games with Discounted Payoff

Abstract

We study partially observable semi-Markov game with discounted payoff on a Borel state space. We study both zero sum and nonzero sum games. We establish saddle point equilibrium and Nash equilibrium for zero sum and nonzero sum cases, respectively.     

Nonlinear modeling and performance analysis of a closed-loop supply chain in the presence of stochastic noise

ABSTRACT

We study four-echelon supply chains consisting of manufacturer, wholesaler, retailer and customer with recovery center as hybrid recycling channels. In order to gain a larger market share, the retailer often takes the sales as a decision-making variable. For this purpose, in this supply chain, the retailer limits the forecast of market demand in future periods with expected logic. It also manages demand by leveraging prices and choosing market. In this paper, first, we investigate the state-space model of this supply chain system and examine the effect of complex dynamic and stochastic noise on the bullwhip effect. We analytically prove that this factor leads to the bullwhip effect. So, first, we filtered the information between nodes with extended Kalman filter after which we regulated the destructive effects of the bullwhip phenomenon by designing a non-linear quadratic Gaussian optimal controller. Eventually, the simulation results indicate the efficiency of the proposed method.     

变时延移动Ad-Hoc网络容量非合作规划博弈模型的渐近稳定性

移动Ad-Hoc网络容量的稳定性是保证其服务质量的关键性质之一.本文提出一种新颖的考虑时变传播时延的非合作规划博弈移动Ad-Hoc网络容量分析模型稳定性控制技术.首先求得加入时变传播时延项的非合作规划博弈移动Ad-Hoc网络容量分析模型的源节点发送流量速率演化方程组—–一类非线性时变时滞微分方程组,在此基础上采用描述器技术结合线性矩阵不等式技术得到该模型的渐进稳定性准则,并设计了模型稳定性控制的迭代算法.由于是基于等价模型变换,所提出的渐近稳定性判别准则具有较小的保守性.仿真实验验证了本算法的有效性.本建模与分析方法虽以具体的非合作规划博弈移动Ad-Hoc网络容量分析模型为例,但其可以应用于一般的移动Ad-Hoc网络容量稳定性控制问题.     

基于提前期压缩的供应链三方利益博弈

讨论基于提前期压缩的供应链利益协调问题。供应商采用批量分解方式向采购商供货,由3 PL承担运输。供应链成员通过提前期赶工压缩提前期,降低成本。基于斯坦克尔伯格博弈模型,设计一种决策原则,对采购商、供应商、3PL和系统四方成本节约情况进行比较并根据决策原则挑选出最佳方案。仿真分析讨论了相关参数变化对决策变量和各方成本节约额的影响。结论表明,三方博弈的结果倾向于选择A模式或D模式,且当采购商单位库存成本较大时,各方成本节约额随采购商订货成本变化波动较大。结论证明了基于提前期压缩的博弈决策方式的有效性。     

具有不完全信息的内部交易动态博弈

在Kyle模型中的线性均衡假设进行了修正的基础上,针对内部交易者只具有资产价值不完全信息情况,建立两期风险厌恶型内部交易均衡模型,并求得该模型的子博弈纳什均衡解.由此发现资产价值不完信息中噪音对市场干扰程度愈小(波动程度愈小),就愈有利于内部交易者的收益;内部交易者的交易就愈活跃;交易均衡价格包含资产价值信息就愈多.     

基于古诺博弈策略的报童问题的模型及分析

通过建立和分析基于古诺博弈策略的报童问题的数学模型,寻求两个销售商有重叠销售区域时的竞争价格策略优化方法.模型的主要特点是:基于蜂窝理论刻画每个销售商的营销区域呈正六边形,分析了相邻销售商有重叠销售区域时,如何确定最优竞争价格策略,保证其利润最大;其次,该模型把需求量看成价格的函数,将消费者与销售商的距离看成是影响实际消费行为的重要因素纳入到消费者密度函数.