Mark Solomon, Bloated inventories hit Walmart, Target, and other retailers’ profits, trucking demand, FreightWaves, May 20, 2022.
However, furniture, home furnishings and appliances, building materials and garden equipment, and a category known as “other general merchandise,” which includes Walmart and Target, among others, reported higher inventory-to-sales ratios, according to government data analyzed by Michigan State.
David Simchi-Levi, William Schmidt, and Yehua Wei, From Superstorms to Factory Fires: Managing Unpredictable Supply-Chain Disruptions, Harvard Business Review, 2014.
MIT Forum for Supply Chain Innovation, MIT Professor David Simchi-Levi’s Risk Exposure Index (REI) used by the UN Office for Disaster Risk Reduction: REI methodology beginning to influence thinking beyond the supply chain world, May 2, 2014.
Gah-Yi Ban and Cynthia Rudin, The Big Data Newsvendor: Practical Insights from Machine Learning, Operations Research, Vol. 67, No. 1, Pages: 90–108, 2019. (MSOM Society 2021 Operations Research Best OM Paper Award)
We investigate the data-driven newsvendor problem when one has n observations of p features related to the demand as well as historical demand data. Rather than a two-step process of first estimating a demand distribution then optimizing for the optimal order quantity, we propose solving the “big data” newsvendor problem via single-step machine-learning algorithms. Specifically, we propose algorithms based on the empirical risk minimization (ERM) principle, with and without regularization, and an algorithm based on kernel-weights optimization (KO). The ERM approaches, equivalent to high-dimensional quantile regression, can be solved by convex optimization problems and the KO approach by a sorting algorithm. We analytically justify the use of features by showing that their omission yields inconsistent decisions. We then derive finite-sample performance bounds on the out-of-sample costs of the feature-based algorithms, which quantify the effects of dimensionality and cost parameters. Our bounds, based on algorithmic stability theory, generalize known analyses for the newsvendor problem without feature information. Finally, we apply the feature-based algorithms for nurse staffing in a hospital emergency room using a data set from a large UK teaching hospital and find that (1) the best ERM and KO algorithms beat the best practice benchmark by 23% and 24%, respectively, in the out-of-sample cost, and (2) the best KO algorithm is faster than the best ERM algorithm by three orders of magnitude and the best practice benchmark by two orders of magnitude.
Kris Johnson Ferreira, David Simchi-Levi, and He Wang. (2018). “Online network revenue management using Thompson sampling.” Operations Research, 66(6), 1586-1602. (Supplemental Material, code, MSOM Society 2021 Operations Research Best OM Paper Award)
We consider a price-based network revenue management problem in which a retailer aims to maximize revenue from multiple products with limited inventory over a finite selling season. As is common in practice, we assume the demand function contains unknown parameters that must be learned from sales data. In the presence of these unknown demand parameters, the retailer faces a trade-off commonly referred to as the “exploration-exploitation trade-off.” Toward the beginning of the selling season, the retailer may offer several different prices to try to learn demand at each price (“exploration” objective). Over time, the retailer can use this knowledge to set a price that maximizes revenue throughout the remainder of the selling season (“exploitation” objective). We propose a class of dynamic pricing algorithms that builds on the simple, yet powerful, machine learning technique known as “Thompson sampling” to address the challenge of balancing the exploration-exploitation trade-off under the presence of inventory constraints. Our algorithms have both strong theoretical performance guarantees and promising numerical performance results when compared with other algorithms developed for similar settings. Moreover, we show how our algorithms can be extended for use in general multiarmed bandit problems with resource constraints as well as in applications in other revenue management settings and beyond.
Christian D. Hubbs, Hector D. Perez, Owais Sarwar, Nikolaos V. Sahinidis, Ignacio E. Grossmann, John M. Wassick, OR-Gym: A Reinforcement Learning Library for Operations Research Problems, arXiv:2008.06319v2. (Python)
Reinforcement learning (RL) has been widely applied to game-playing and surpassed the best human-level performance in many domains, yet there are few use-cases in industrial or commercial settings. We introduce OR-Gym, an open-source library for developing reinforcement learning algorithms to address operations research problems. In this paper, we apply reinforcement learning to the knapsack, multi-dimensional bin packing, multi-echelon supply chain, and multi-period asset allocation model problems, as well as benchmark the RL solutions against MILP and heuristic models. These problems are used in logistics, finance, engineering, and are common in many business operation settings. We develop environments based on prototypical models in the literature and implement various optimization and heuristic models in order to benchmark the RL results. By re-framing a series of classic optimization problems as RL tasks, we seek to provide a new tool for the operations research community, while also opening those in the RL community to many of the problems and challenges in the OR field.