Multi-Echelon Inventory Management for a Non-Stationary Capacitated Distribution Network

We present an inventory management solution for a non-stationary capacitated multi-echelon distribution network involving thousands of products. Assuming backlogged sales, we revisit and leverage the seminal multi-echelon inventory management results in the literature to establish the structural properties of the problem, and derive an efficient and practical solution method. In particular, we describe how the … Read more

Distributionally Robust Inventory Management with Advance Purchase Contracts

We propose a distributionally robust inventory model for finding an optimal ordering policy that attains the minimum worst-case expected total cost. In a classical stochastic setting, this problem is typically addressed by dynamic programming and is solved by the famous base-stock policy. This approach however crucially relies on two controversial assumptions: the demands are serially … Read more

Optimal Robust Policy for Feature-Based Newsvendor

We study policy optimization for the feature-based newsvendor, which seeks an end-to-end policy that renders an explicit mapping from features to ordering decisions. Unlike existing works that restrict the policies to some parametric class which may suffer from sub-optimality (such as affine class) or lack of interpretability (such as neural networks), we aim to optimize … Read more

Aid Allocation for Camp-Based and Urban Refugees with Uncertain Demand and Replenishments

There are nearly 26 million refugees worldwide seeking safety from persecution, violence, conflict, and human rights violations. Camp-based refugees are those that seek shelter in refugee camps, whereas urban refugees inhabit nearby, surrounding populations. The systems that supply aid to refugee camps may suffer from ineffective distribution due to challenges in administration, demand uncertainty and … Read more

Joint Inventory and Revenue Management with Removal Decisions

We study the problem of a retailer that maximizes profit through joint replenishment, pricing and removal decisions. This problem is motivated by the observation that retailers usually retain rights to remove inventory from their network either by returning it to the suppliers or through liquidation in the face of random demand and capacity constraints. We … Read more

From Predictive to Prescriptive Analytics

In this paper, we combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisions in OR/MS problems. In a departure from other work on data-driven optimization and reflecting our practical experience with the data available in applications … Read more

Pareto Efficiency in Robust Optimization

This paper formalizes and adapts the well known concept of Pareto efficiency in the context of the popular robust optimization (RO) methodology. We argue that the classical RO paradigm need not produce solutions that possess the associated property of Pareto optimality, and illustrate via examples how this could lead to inefficiencies and sub-optimal performance in … Read more

Supermodularity and Affine Policies in Dynamic Robust Optimization

This paper considers robust dynamic optimization problems, where the unknown parameters are modeled as uncertainty sets. We seek to bridge two classical paradigms for solving such problems, namely (1) Dynamic Programming (DP), and (2) policies parameterized in model uncertainties (also known as decision rules), obtained by solving tractable convex optimization problems. We provide a set … Read more

A Hierarchy of Near-Optimal Policies for Multi-stage Adaptive Optimization

In this paper, we propose a new tractable framework for dealing with multi-stage decision problems affected by uncertainty, applicable to robust optimization and stochastic programming. We introduce a hierarchy of polynomial disturbance-feedback control policies, and show how these can be computed by solving a single semidefinite programming problem. The approach yields a hierarchy parameterized by … Read more

Integrated Forecasting and Inventory Control for Seasonal Demand: a Comparison with the Holt-Winters Approach

We present a data-driven forecasting technique with integrated inventory control for seasonal data and compare it to the traditional Holt-Winters algorithm. Results indicate that the data-driven approach achieves a 2-5% improvement in the average regret. Citation Technical Report, Lehigh University, Department of Industrial and Systems Engineering, Bethlehem, PA. Article Download View Integrated Forecasting and Inventory … Read more