An Optimization Approach to the Ordering Phase of an Attended Home Delivery Service

Abstract

Attended Home Delivery (AHD) systems are used whenever a supplying company offers online shopping services that require that customers must be present when their deliveries arrive. Therefore, the supplying company and the customer must both agree on a time window, which ideally is rather short, during which delivery is guaranteed. Typically, a capacitated Vehicle Routing Problem with Time Windows forms the underlying optimization problem of the AHD system. In this work, we consider an AHD system that runs the online grocery shopping service of an international grocery retailer.The ordering phase, during which customers place their orders through the web service, is the computationally most challenging part of the AHD system. The delivery schedule must be built dynamically as new orders are placed. We propose a solution approach that allows to (non-stochastically) determine which delivery time windows can be offered to potential customers. We split the computations of the ordering phase into four key steps. For performing these basic steps we suggest both a heuristic approach and a hybrid approach employing mixed-integer linear programs. In an experimental evaluation we demonstrate the efficiency of our approaches.

Computational Approaches for Grocery Home Delivery Services

“…The benchmark instances are derived from those originally proposed by reference [13]. They are designed to reproduce the characteristics of an online grocery shopping service offered by an international grocery retailer.…”

“…For a better understanding, we break down the planning and fulfillment process of the e-grocer into four phases (see also reference [13]). The first phase is called the tactical planning phase, which happens several months/weeks before delivery.…”

Effectiveness of demand and fulfillment control in dynamic fleet management of ride‐sharing systems

“…These mechanisms are primarily examined concerning the problem of managing delivery slots in attended home deliveries. Myopic customer acceptance mechanisms are presented for example in Campbell and Savelsbergh [10], Ehmke and Campbell [15], and Cwioro et al [13]. Campbell and Savelsbergh [10] examine financial incentives to encourage customers to choose a delivery time slot that is favorable in terms of myopically planned delivery routes.…”

“…Ehmke and Campbell [15] compare simple static and dynamic customer acceptance mechanisms under consideration of stochastic travel times. Cwioro et al [13] propose an adaptive large neighborhood search (ALNS) for the feasibility check to maximize the number of time slots that can be offered. Beyond that, several anticipatory demand control approaches have been considered.…”