In this paper, we study the offline sequential feature-based pricing and inventory control problem where the current demand depends on the past demand levels and any demand exceeding the available inventory is lost. Our goal is to leverage the offline dataset, consisting of past prices, ordering quantities, inventory levels, covariates, and censored sales levels, to estimate the optimal pricing and inventory control policy that maximizes long-term profit. While the underlying dynamic without censoring can be modeled by Markov decision process (MDP), the primary obstacle arises from the observed process where demand censoring is present, resulting in missing profit information, the failure of the Markov property, and a non-stationary optimal policy. To overcome these challenges, we first approximate the optimal policy by solving a high-order MDP characterized by the number of consecutive censoring instances, which ultimately boils down to solving a specialized Bellman equation tailored for this problem. Inspired by offline reinforcement learning and survival analysis, we propose two novel data-driven algorithms to solving these Bellman equations and, thus, estimate the optimal policy. Furthermore, we establish finite sample regret bounds to validate the effectiveness of these algorithms. Finally, we conduct numerical experiments to demonstrate the efficacy of our algorithms in estimating the optimal policy. To the best of our knowledge, this is the first data-driven approach to learning optimal pricing and inventory control policies in a sequential decision-making environment characterized by censored and dependent demand. The implementations of the proposed algorithms are available at https://github.com/gundemkorel/Inventory_Pricing_Control

Problem Definition: Are traditional deep reinforcement learning (DRL) algorithms, developed for a broad range of purposes including game-play and robotics, the most suitable machine learning algorithms for applications in inventory control? To what extent would DRL algorithms tailored to the unique characteristics of inventory control problems provide superior performance compared to DRL and traditional benchmarks? Methodology/results: We propose and study Deep Controlled Learning (DCL), a new DRL framework based on approximate policy iteration specifically designed to tackle inventory problems. Comparative evaluations reveal that DCL outperforms existing state-of-the-art heuristics in lost sales inventory control, perishable inventory systems, and inventory systems with random lead times, achieving lower average costs across all test instances and maintaining an optimality gap of no more than 0.1\%. Notably, the same hyperparameter set is utilized across all experiments, underscoring the robustness and generalizability of the proposed method. Managerial implications: These substantial performance and robustness improvements pave the way for the effective application of tailored DRL algorithms to inventory management problems, empowering decision-makers to optimize stock levels, minimize costs, and enhance responsiveness across various industries.

This document provides a brief introduction to learned automated planning problem where the state transition function is in the form of a binarized neural network (BNN), presents a general MaxSAT encoding for this problem, and describes the four domains, namely: Navigation, Inventory Control, System Administrator and Cellda, that are submitted as benchmarks for MaxSAT Evaluation 2021.

AI has left the cinema and reached out into real life, from our homes, transportation and mobile devices, to the groundbreaking developments in business and Industry 4.0. Artificial Intelligence and machine learning are rapidly revolutionising everything we thought we knew about industry, transforming warehouse management and supply chain logistics. A smart warehouse combines various interconnected technologies to form an ecosystem whereby an entire business operation, from supply to delivery, is governed by AI. Goods are received at the warehouse, identified and sorted, processed, packaged, and pulled for shipment, all automatically and with minimal margin for error. Warehouse management will become more agile, with faster responses to logistical demands of material items and personnel, and more scalable in terms of finding new solutions for greater volume and flow of product.

The main objective of any supply chain remains to be the management of inventory, from procurement to supply the right product at the right time in the right place. And, for traditional supply chain companies, it has always been a challenge to achieve it as they focus majorly on optimizing a particular segment of the supply chain, rather than optimizing the entire value chain. This limits their operational efficiency to meet the need for granularity in customers' unique expectations. Artificial Intelligence (AI) can help supply chain companies in breaking the silos to reinvent their operational models. AI in the supply chain helps companies in procuring and processing large datasets and provides better visibility within the supply chain.