Combinatorial Assortment Optimization

Assortment optimization refers to the problem of designing a slate of products to offer potential customers, such as stocking the shelves in a convenience store. The price of each product is fixed in advance, and a probabilistic choice function describes which product a customer will choose from any given subset. We introduce the combinatorial assortment problem, where each customer may select a bundle of products. We consider a model of consumer choice where the relative value of different bundles is described by a valuation function, while individual customers may differ in their absolute willingness to pay, and study the complexity of the resulting optimization problem. We show that any sub-polynomial approximation to the problem requires exponentially many demand queries when the valuation function is XOS, and that no FPTAS exists even for succinctly-representable submodular valuations. On the positive side, we show how to obtain constant approximations under a "well-priced" condition, where each product's price is sufficiently high. We also provide an exact algorithm for $k$-additive valuations, and show how to extend our results to a learning setting where the seller must infer the customers' preferences from their purchasing behavior

A demand-driven approach for a multi-agent system in Supply Chain Management

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit. © 2010 Springer-Verlag Berlin Heidelberg

Flexible Decision Control in an Autonomous Trading Agent

An autonomous trading agent is a complex piece of software that must operate in a competitive economic environment and support a research agenda. We describe the structure of decision processes in the MinneTAC trading agent, focusing on the use of evaluators – configurable, composable modules for data analysis and prediction that are chained together at runtime to support agent decision-making. Through a set of examples, we show how this structure supports sales and procurement decisions, and how those decision processes can be modified in useful ways by changing evaluator configurations. To put this work in context, we also report on results of an informal survey of agent design approaches among the competitors in the Trading Agent Competition for Supply Chain Management (TAC SCM).autonomous trading agent;decision processes

On the Trade-offs between Modeling Power and Algorithmic Complexity

Mathematical modeling is a central component of operations research. Most of the academic research in our field focuses on developing algorithmic tools for solving various mathematical problems arising from our models. However, our procedure for selecting the best model to use in any particular application is ad hoc. This dissertation seeks to rigorously quantify the trade-offs between various design criteria in model construction through a series of case studies. The hope is that a better understanding of the pros and cons of different models (for the same application) can guide and improve the model selection process. In this dissertation, we focus on two broad types of trade-offs. The first type arises naturally in mechanism or market design, a discipline that focuses on developing optimization models for complex multi-agent systems. Such systems may require satisfying multiple objectives that are potentially in conflict with one another. Hence, finding a solution that simultaneously satisfies several design requirements is challenging. The second type addresses the dynamics between model complexity and computational tractability in the context of approximation algorithms for some discrete optimization problems. The need to study this type of trade-offs is motivated by certain industry problems where the goal is to obtain the best solution within a reasonable time frame. Hence, being able to quantify and compare the degree of sub-optimality of the solution obtained under different models is helpful. Chapters 2-5 of the dissertation focus on trade-offs of the first type and Chapters 6-7 the second type

Dynamic, data-driven decision-making in revenue management

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 233-241).Motivated by applications in Revenue Management (RM), this thesis studies various problems in sequential decision-making and demand learning. In the first module, we consider a personalized RM setting, where items with limited inventories are recommended to heterogeneous customers sequentially visiting an e-commerce platform. We take the perspective of worst-case competitive ratio analysis, and aim to develop algorithms whose performance guarantees do not depend on the customer arrival process. We provide the first solution to this problem when there are both multiple items and multiple prices at which they could be sold, framing it as a general online resource allocation problem and developing a system of forecast-independent bid prices (Chapter 2). Second, we study a related assortment planning problem faced by Walmart Online Grocery, where before checkout, customers are recommended "add-on" items that are complementary to their current shopping cart (Chapter 3). Third, we derive inventory-dependent priceskimming policies for the single-leg RM problem, which extends existing competitive ratio results to non-independent demand (Chapter 4). In this module, we test our algorithms using a publicly-available data set from a major hotel chain. In the second module, we study bundling, which is the practice of selling different items together, and show how to learn and price using bundles. First, we introduce bundling as a new, alternate method for learning the price elasticities of items, which does not require any changing of prices; we validate our method on data from a large online retailer (Chapter 5). Second, we show how to sell bundles of goods profitably even when the goods have high production costs, and derive both distribution-dependent and distribution-free guarantees on the profitability (Chapter 6). In the final module, we study the Markovian multi-armed bandit problem under an undiscounted finite time horizon (Chapter 7). We improve existing approximation algorithms using LP rounding and random sampling techniques, which result in a (1/2 - eps)- approximation for the correlated stochastic knapsack problem that is tight relative to the LP. In this work, we introduce a framework for designing self-sampling algorithms, which is also used in our chronologically-later-to-appear work on add-on recommendation and single-leg RM.by Will (Wei) Ma.Ph. D

Optimization Of Strategic Planning Processes For Configurable Products: Considerations For Global Supply, Demand, And Sustainability Issues

The assortment planning problem is to decide on the set of products that a retailer or manufacturer will offer to its customers to maximize profitability. While assortment planning research has been expanding in recent years, the current models are inadequate for the needs of a configurable product manufacturer. In particular, we address assortment planning for an automobile manufacturer. We develop models to integrate assortment planning and supply chain management, designed for use by a large automaker in its strategic planning phase. Our model utilizes a multinomial logit model transformed into a mixed integer linear program through the Charnes-Cooper transformation. It is able to scale to problems that contain thousands of configurations to possibly be offered, a necessity given the number of possible configurations an automaker can build. In addition, most research in assortment planning contains simplified costs associated with product complexity. We model a full supply chain and give a rich treatment of the complexity associated with product complexity. We believe that our model can significantly aid automotive manufacturers to balance their product complexity with supply chain complexity, thus increasing profitability. In addition, we study the effect of packaging on the assortment and supply chain of an automaker. We develop a new model for mathematically expressing the effect that packaging has on the way in which customers choose products. Packaging significantly complicates the search space of the assortment planning problem. We introduce a heuristic method based on our packaging model that speeds up the solve times of the models while finding reasonably good solutions. Finally, we extend our initial model to study the effects of sustainability requirements on an automaker\u27s assortment and supply chain. We introduce constraints on the vehicle program average fuel economy, greenhouse gas emissions in the supply chain, and greenhouse gas emissions in the product use phase. We dive deep into each case to glean insights about how automakers can change their decision-making process to balance making their companies more sustainable with profit maximization. While all the examples discussed are from the automotive industry, the models developed can be adapted to address assortment planning for other types of configurable products (e.g., computers, printers, phones)