Solving a Joint Pricing and Inventory Control Problem for Perishables via Deep Reinforcement Learning

Joint Pricing and Inventory Control for Seasonal and Substitutable Goods Mentioning the Symmetrical and Asymmetrical Substitution

Nowadays many well-known firms may produce similar products at different prices in order to remain in the competitive environment. The price differences may cause substitution condition which motivates the customers to substitute the similar cheaper product with an expensive one leading to an environment which is known as “customer-based price driven substitution”. This research proposes a new ...

Deep Reinforcement Learning for Solving the Vehicle Routing Problem

We present an end-to-end framework for solving Vehicle Routing Problem (VRP) using deep reinforcement learning. In this approach, we train a single model that finds near-optimal solutions for problem instances sampled from a given distribution, only by observing the reward signals and following feasibility rules. Our model represents a parameterized stochastic policy, and by applying a policy g...

Pricing Perishables

A key feature of food products is their perishability. Within the short marketing window that characterizes most food and ag products, demand is typically highly stochastic and difficult to predict. This combination of features poses substantial challenges to retailers when pricing products and has implications for performance that ripples through vertical food chains. For many food products, p...

Problem solving with reinforcement learning

This thesis is concerned with practical issues surrounding the application of reinforcement learning techniques to tasks that take place in high dimensional continuous state-space environments. In particular, the extension of on-line updating methods is considered, where the term implies systems that learn as each experience arrives, rather than storing the experiences for use in a separate oo-...

Data-driven Approximation for Joint Pricing and Inventory-Control