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The Elements of Joint Learning and Optimization in Operations Management
This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.
Proceedings of the International Conference on Advance Research in Social and Economic Science (ICARSE 2022)
This is an open access book.ThemeOptimizing the use of social science and economics in the post-pandemic revival era The Covid-19 pandemic is slowly starting to be overcome. Contributions from various disciplines are also needed in the context of post-pandemic recovery, including the fields of social science and economics. Thus, the International Conference on Advanced Research in Social and Economic Science is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to various fields of the social sciences, as well as discuss current and future challenges. Join the social sciences conference as we explore the latest trends in social sciences and discuss common challenges in politics, social, communication, humanities, networking society, business, sustainable development, and international relations.
Operations in an Omnichannel World
The world of retailing has changed dramatically in the past decade. Sales originating at online channels have been steadily increasing, and even for sales transacted at brick-and-mortar channels, a much larger fraction of sales is affected by online channels in different touch points during the customer journey. Shopper behavior and expectations have been evolving along with the growth of digital channels, challenging retailers to redesign their fulfillment and execution processes, to better serve their customers. This edited book examines the challenges and opportunities arising from the shift towards omni- channel retail. We examine these issues through the lenses of operations management,...
On the Performance of Myopic and Revenue-Ordered Policies for a Multi-Period Assortment Optimization Under an MNL Model with Popularity Bias
We consider a monopoly firm that aims to maximize its total revenue over a finite horizon by optimizing the assortments across different periods. Customers make their purchase decisions according to a variant of the classical MNL model that incorporates popularity bias. We assume that the strength of popularity bias can be quantified as an increasing concave function of the cumulative historical sales. For this setting, we show that the resulting assortment optimization problem is NP-Hard even when there are only two periods, and we focus on studying the performance of the myopic and revenue-ordered policies. Despite its simplicity and ease-of-implementation, we show that the myopic policy c...
Phase Transitions in Learning and Earning Under Price Protection Guarantee
Motivated by the prevalence of "price protection guarantee", which allows a customer who purchased a product in the past to receive a refund from the seller during the so-called price protection period (typically defined as a certain time window after the purchase date) in case the seller decides to lower the price, we study the impact of such policy on the design of online learning algorithm for data-driven dynamic pricing with initially unknown customer demand. We consider a setting where a firm sells a product over a horizon of $T$ time steps. For this setting, we characterize how the value of $M$, the length of price protection period, can affect the optimal regret of the learning proces...
Adaptive Lagrangian Policies for Multi-Warehouse Multi-Store Inventory System with Lost Sales
We consider the inventory control problem of a Multi-Warehouse Multi-Store (MWMS) system over a time horizon that the warehouses receive no external replenishment. This problem is prevalent in retail settings, and it is referred to in Jackson (1988) as “what to do until your (external) shipment comes in". The warehouses are stocked with initial inventories and the stores dynamically replenish inventory from the warehouses in each period of the planning horizon. Excess demand in each period at any store is lost. The optimal policy for this problem is complex and state-dependent, and due to the curse of dimensionality, computing the optimal policy using standard dynamic programming is numeri...
LP-Based Artificial Dependency for Probabilistic Etail Order Fulfillment
We consider an online multi-item retailer with multiple fulfillment facilities and finite inventory, with the objective of minimizing the expected shipping cost of fulfilling customer orders over a finite horizon. We approximate the stochastic dynamic programming formulation of the problem with an equivalent deterministic linear program, which we use to develop a probabilistic fulfillment heuristic that is provably optimal in the asymptotic sense. This first heuristic, however, relies on solving an LP that is exponential in the size of the input. Therefore, we subsequently provide another heuristic which solves an LP that is polynomial in the size of the input, and prove an upper bound on its asymptotic competitive ratio. This heuristic works by modifying the LP solution with artificial dependencies, with the resulting fractional variables used to probabilistically fulfill orders. A hardness result shows that asymptotically optimal policies that are computationally efficient cannot exist. Finally, we conduct numerical experiments that show that our heuristic's performance is very close to optimal for a range of parameters.
Assortment and Inventory Planning Under Dynamic (Stockout-based) Substitution in the Presence of Customer Returns
We consider a deterministic (fluid) multi-period assortment and inventory planning problem under the Multinomial Logit (MNL) choice model with dynamic (stockout-based) substitution, customer returns with a general return time distribution, and a cumulative capacity (storage) constraint for all products. Specifically, there is a one-time inventory decision at the beginning of the selling horizon and a customer who makes a purchase in the current period is allowed to return the purchase at a future period. The returned item will be inspected and, if it passes the inspection, it will be restocked and becomes available again for sale in the next period. The objective of the firm is to identify t...
Self-adapting Robustness in Demand Learning
We study dynamic pricing over a finite number of periods in the presence of demand model ambiguity. Departing from the typical no-regret learning environment, where price changes are allowed at any time, pricing decisions are made at pre-specified points in time and each price can be applied to a large number of arrivals. In this environment, which arises in retailing, a pricing decision based on an incorrect demand model can significantly impact cumulative revenue. We develop an adaptively-robust-learning (ARL) pricing policy that learns the true model parameters from the data while actively managing demand model ambiguity. It optimizes an objective that is robust with respect to a self-ada...
