An integrated product planning model for pricing and bundle selection using Markov decision processes and data envelope analysis

When bundling products during the product planning stage, there are a number of possible combinations that can be offered to customers. Consider a firm that offers N distinctive products, then there are 2^N−(N+1) possible bundling combinations. Now, if we wish to make pricing and bundle selection decisions, keeping in my mind limited inventory and a finite time horizon, then the size of the state space could be very large and finding an optimal solution could be impossible. To tackle this issue, we formulate an integrated model that utilizes a Markov decision process and data envelope analysis. Bundle selections are made using data envelope analysis in each decision epoch. Once the efficient bundles are selected they are priced by solving a Markov decision process using dynamic programming. Numerical examples are solved to exhibit the model's potential in solving real-world problems.