Dynamic demand management and online tour planning for same-day delivery

For providers to stay competitive in a context of continued growth in e-retail sales and increasing customer expectations, same-day delivery options have become very important. Typically, with same-day delivery, customers purchase online and expect to receive their ordered goods within a narrow delivery time span. Providers thus experience substantial operational challenges to run profitable tours and generate sufficiently high contribution margins to cover overhead costs. We address these challenges by combining a demand-management approach with an online tour-planning approach for same-day delivery. More precisely, in order to reserve capacity for high-value customer orders and to guide customer choices toward efficient delivery operations, we propose a demand-management approach that explicitly optimizes the combination of delivery spans and prices which are presented to each incoming customer request. The approach includes an anticipatory sample-scenario based value approximation, which incorporates a direct online tour-planning heuristic. It does not require extensive offline learning and is scalable to realistically sized instances with multiple vehicles. In a comprehensive computational study, we show that our anticipatory approach can improve the contribution margin by up to 50% compared to a myopic benchmark approach. We also show that solving an explicit pricing optimization problem is a beneficial component of our approach. More precisely, it outperforms both a pure availability control and a simple pricing rule based on opportunity costs. The latter idea is one used in other approaches for related dynamic pricing problems dealt with in the literature

A review of choice-based revenue management : theory and methods

Over the last fifteen years, the theory and practice of revenue management has experienced significant developments due to the need to incorporate customer choice behavior. In this paper, we portray these developments by reviewing the key literature on choice-based revenue management, specifically focusing on methodological publications of availability control over the years 2004–2017. For this purpose, we first state the choice-based network revenue management problem by formulating the underlying dynamic program, and structure the review according to its components and the resulting inherent challenges. In particular, we first focus on the demand modeling by giving an overview of popular choice models, discussing their properties, and describing estimation procedures relevant to choice-based revenue management. Second, we elaborate on assortment optimization, which is a fundamental component of the problem. Third, we describe recent developments on tackling the entire control problem. We also discuss the relation to dynamic pricing. Finally, we give directions for future research

A review of revenue management : recent generalizations and advances in industry applications

Originating from passenger air transport, revenue management has evolved into a general and indispensable methodological framework over the last decades, comprising techniques to manage demand actively and to further improve companies’ profits in many different industries. This article is the second and final part of a paper series surveying the scientific developments and achievements in revenue management over the past 15 years. The first part focused on the general methodological advances regarding choice-based theory and methods of availability control over time. In this second part, we discuss some of the most important generalizations of the standard revenue management setting: product innovations (opaque products and flexible products), upgrading, overbooking, personalization, and risk-aversion. Furthermore, to demonstrate the broad use of revenue management, we survey important industry applications beyond passenger air transportation that have received scientific attention over the years, covering air cargo, hotel, car rental, attended home delivery, and manufacturing. We work out the specific revenue management-related challenges of each industry and portray the key contributions from the literature. We conclude the paper with some directions for future research

Using Dynamic Programming Decomposition for Revenue Management with Opaque Products

Opaque products enable service providers to hide specific characteristics of their service fulfillment from the customer until after purchase. Prominent examples include internet-based service providers selling airline tickets without defining details, such as departure time or operating airline, until the booking has been made. Owing to the resulting flexibility in resource utilization, the traditional revenue management process needs to be modified. In this paper, we extend dynamic programming decomposition techniques widely used for traditional revenue management to develop an intuitive capacity control approach that allows for the incorporation of opaque products. In a simulation study, we show that the developed approach significantly outperforms other well-known capacity control approaches adapted to the opaque product setting. Based on the approach, we also provide computational examples of how the share of opaque products as well as the degree of opacity can influence the results