Time Window Budgets for Anticipation in Attended Home Delivery

Abstract

Attended home delivery describes the delivery of goods by e-grocers or e-tailers to customers within an agreed time window. Because customers expect narrow time windows, offering such services may lead to expensive fulfillment operations. This has led to research on how to influence customers’ bookings using time window pricing or slotting. In this paper, we reconsider the problem of demand management through dynamic pricing for attended home delivery services. This problem is usually modeled as a stochastic dynamic program, but even small instances cannot be solved to optimality due to the curses of dimensionality. The major challenges consist of finding feasible time windows for an incoming customer, estimating the opportunity cost, i.e., the future monetary loss due to accepting a booking, and optimizing the time window prices in real time. In this paper, we propose a route-based approximate dynamic programming approach to tackle these challenges. The approach carefully combines and partially extends state-of-the-art methods in attended home delivery, dynamic pricing, and dynamic vehicle routing. In an extensive simulation study, we com-pare its performance with state-of-the-art benchmark heuristics. The results indicate a superior perfor-mance of our approach in terms of both profit and number of customers served.