Evolutionary Pricing Games for Supply Chain Networks

Description

Choosing the right strategies in supply chain networks is a well-known representative of decision making in networks. Due to its multi-player competitive nature, game theory has been proven to be so far the best approach to deal with this marathon. On the other hand evolutionary dynamics is an emerging approach in supply chain games leading to a phenomenal integration of theoretical biology and mathematical problems of industrial engineering. This research project aims to find and analyse pricing behaviours and evolutionary stable strategies (ESS) in supply chain by employing dynamic demand functions and evolutionary game models. A combination of theoretical mathematical tools such as combinatory, operations research and differential equations is expected to be utilized in order to deal with the games' dynamic complexity. The games can be defined in highest competition state that is in case of homogenous and perishable goods (time constraint challenge) along with duopolistic or oligopolistic conditions. Several evolutionary approaches such as imitation, replicator dynamics, etc. will be examined. The research will in turn lead to valuable results such as behaviour trends of players, sensitivity analysis (to new endogenous and exogenous parameters) and mathematical theorems providing insights for ESS in supply chain networks beneficial for all players.

A lot of methods have to be examined to reach the consistency with the proposed problem. Aside from the innovations in the field of integrating evolutionary game dynamics and industrial engineering, based on its theoretical structure, this research will be aimed to find the answer to following key questions:

1. What are the best non-zero cooperative games for multiplex networks such as supply chains?

2. What are the evolutionary stable strategies (ESS) for game models coordinated with mathematical models?

3. How to integrate mathematical demand functions in terms of prices to pricing game models without reducing the performance of the decision making or without the loss of generality?

4. How will learning and imitation affect the performance of optimal solutions of a mathematical model?

5. What are the most appropriate game replicator dynamics in case of continuous variables?

6. How can exogenous parameters (such as inflation, etc.) affect the ESS?

7. How can we generalize the evolutionary mathematical and game models from oligopoly and duopoly conditions to wider supply chains with more players?