Application of Dynamic Programming Method to Marketing Decisions Based on Customer Database

2016 ◽  
Vol 4 (2) ◽  
pp. 169-176 ◽  
Author(s):  
Zhongqiu Zhao ◽  
Xiaofei Li ◽  
Baolong Ma ◽  
Jinlin Li
Keyword(s):  
Dynamic Programming ◽  
Marketing Strategy ◽  
Customer Behavior ◽  
Purchase Behavior ◽  
Dynamic Programming Method ◽  
Model Estimation ◽  
Reward Program ◽  
Transaction Database ◽  
Reward Programs ◽  
Repeat Purchase Behavior

AbstractThe paper focuses on modeling longitudinal customer behavior and develops a dynamic programming (DP) to show how customer transaction database may be used to guide marketing decisions such as pricing and the design of customer reward programs. Dynamic programming is not as a tool to marketing decisions making in this research but rather as a description of consumer behavior. The results show that the method provides a means for evaluating the effectiveness of marketing strategy, for example, customer reward programs. Moreover, the findings from the model estimation indicate that reward program can actually increase the customer’s purchase level and stimulate the repeat purchase behavior.

scholarly journals Single-Manufacturer Multi-Retailer Supply Chain Models with Discrete Stochastic Demand

2021 ◽  
Vol 13 (15) ◽  
pp. 8271
Author(s):  
Yaqing Xu ◽  
Jiang Zhang ◽  
Zihao Chen ◽  
Yihua Wei
Keyword(s):  
Dynamic Programming ◽  
Supply Chain ◽  
Computational Complexity ◽  
Stackelberg Game ◽  
Programming Model ◽  
Dynamic Programming Method ◽  
Game Model ◽  
Chain Model ◽  
Stochastic Demands ◽  
Demand Models

Although there are highly discrete stochastic demands in practical supply chain problems, they are seldom considered in the research on supply chain systems, especially the single-manufacturer multi-retailer supply chain systems. There are no significant differences between continuous and discrete demand supply chain models, but the solutions for discrete random demand models are more challenging and difficult. This paper studies a supply chain system of a single manufacturer and multiple retailers with discrete stochastic demands. Each retailer faces a random discrete demand, and the manufacturer utilizes different wholesale prices to influence each retailer’s ordering decision. Both Make-To-Order and Make-To-Stock scenarios are considered. For each scenario, the corresponding Stackelberg game model is constructed respectively. By proving a series of theorems, we transfer the solution of the game model into non-linear integer programming model, which can be easily solved by a dynamic programming method. However, with the increase in the number of retailers and the production capacity of manufacturers, the computational complexity of dynamic programming drastically increases due to the Dimension Barrier. Therefore, the Fast Fourier Transform (FFT) approach is introduced, which significantly reduces the computational complexity of solving the supply chain model.

scholarly journals Initializing the EM Algorithm for Univariate Gaussian, Multi-Component, Heteroscedastic Mixture Models by Dynamic Programming Partitions

2018 ◽  
Vol 15 (03) ◽  
pp. 1850012 ◽  
Author(s):  
Andrzej Polanski ◽  
Michal Marczyk ◽  
Monika Pietrowska ◽  
Piotr Widlak ◽  
Joanna Polanska
Keyword(s):  
Dynamic Programming ◽  
Mixture Models ◽  
Recursive Algorithm ◽  
Dynamic Programming Method ◽  
Mixture Distributions ◽  
Gaussian Mixtures ◽  
Scoring Functions ◽  
Initial Values ◽  
The Em Algorithm

Setting initial values of parameters of mixture distributions estimated by using the EM recursive algorithm is very important to the overall quality of estimation. None of the existing methods are suitable for heteroscedastic mixtures with a large number of components. We present relevant novel methodology of estimating the initial values of parameters of univariate, heteroscedastic Gaussian mixtures, on the basis of dynamic programming partitioning of the range of observations into bins. We evaluate variants of the dynamic programming method corresponding to different scoring functions for partitioning. We demonstrate the superior efficiency of the proposed method compared to existing techniques for both simulated and real datasets.

scholarly journals Solving Channel Allocation by Reinforcement Learning in Cognitive Enabled Vehicular Ad Hoc Networks

2021 ◽  
Author(s):  
Yunfan Su
Keyword(s):  
Dynamic Programming ◽  
Reinforcement Learning ◽  
Optimal Policy ◽  
Ad Hoc ◽  
Transition Probabilities ◽  
Channel Allocation ◽  
Dynamic Programming Method ◽  
Learning Method ◽  
Time Intervals ◽  
Model Free

Vehicular ad hoc network (VANET) is a promising technique that improves traffic safety and transportation efficiency and provides a comfortable driving experience. However, due to the rapid growth of applications that demand channel resources, efficient channel allocation schemes are required to utilize the performance of the vehicular networks. In this thesis, two Reinforcement learning (RL)-based channel allocation methods are proposed for a cognitive enabled VANET environment to maximize a long-term average system reward. First, we present a model-based dynamic programming method, which requires the calculations of the transition probabilities and time intervals between decision epochs. After obtaining the transition probabilities and time intervals, a relative value iteration (RVI) algorithm is used to find the asymptotically optimal policy. Then, we propose a model-free reinforcement learning method, in which we employ an agent to interact with the environment iteratively and learn from the feedback to approximate the optimal policy. Simulation results show that our reinforcement learning method can acquire a similar performance to that of the dynamic programming while both outperform the greedy method.

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