Attended Home Delivery Research Articles

Optimizing Attended Home Delivery: Multiple recovery options and customer availability profiles to face synchronization failures

In the growing sector of Attended Home Delivery, unsynchronized deliveries between couriers and recipients affect both customers’ satisfaction and companies’ costs. Hence, reducing such failures improves companies’ service quality and logistics efficiency. To address this issue, we study an Attended Home Delivery Problem with Recovery Options (AHDPRO) in which customers specify their probability of being at home during different timeslots of the day and their preferred recovery option in case of a synchronization failure. The options include leaving the package in a predefined safe location, bringing it to a generic collection point, or scheduling a second delivery attempt. Each alternative involves different costs and, in most cases, additional operational decisions. The AHDPRO aims to complete all customer deliveries while minimizing overall routing times as well as the overall penalty due to the recovery actions implemented and weighted by the probability of a synchronization failure to occur. We propose a branch-and-cut algorithm, including valid inequalities and heuristic procedures, to solve a Mixed-Integer Linear Programming model based on an expanded graph. Using the developed method as a tool for evaluating costs and operations, we conduct an experimental campaign on scenarios adapted from the literature involving lexicographic-based optimization procedures able to address the multiple attributes of the solutions. The results obtained allow us to assess the impact of the different recovery options on the optimal solutions and their values. Additionally, the results yield several managerial insights for companies operating in the Attended Home Delivery sector, such as the timeslot length, perceived service quality, and other key operational factors contributing to efficient planning and improved customer satisfaction.

An updated survey of attended home delivery and service problems with a focus on applications

AbstractThe research field of Attended Home Delivery (AHD) and Attended Home Service (AHS) problems has experienced fast growing interest in the last two decades, with the rapid growth of online platforms and e-commerce transactions. The radical changes in consumer lifestyles and habits as well as the COVID-19 pandemic contingency have reinforced that interest, raising further challenges and opportunities that need to be addressed by innovative methodologies and policies. The aim of this work is to provide an extensive literature review on the state of the art for AHD and AHS problems, with a particular focus on real-world applications. A discussion of promising future research directions is also provided.

Adaptive incentive mechanism with predictors for on-time attended home delivery problem

The widespread use of the Internet and smart devices has led to a fast growth in online shopping, offering new chances for online retailers to boost profits. However, this expansion has also brought various challenges, such as the heavy workload faced by delivery riders. To meet customers’ delivery time preferences and increase earnings, riders often work long hours, especially during busy periods. This study explores how historical delivery data can be used to balance workload in on-time attended home delivery. Drawing on the actual delivery operations and data of an online shopping platform, we propose a framework that combines delivery demand and customer behavior predictors with an adaptive incentive system to balance rider workload. Specifically focusing on same-day attended home delivery, we introduce a method to forecast future delivery demand, an algorithm to estimate customer choice behavior using a simple model, and an adaptive incentive system to influence customer decisions and achieve workload balance. We show that as order volume increases, the proposed incentive system achieves the pre-determined workload target. Using real data, we conduct numerical experiments which not only underscore the superior predictive performance of our models but also affirm the efficacy of the proposed incentive structure.

Uplift modeling and its implications for appointment date prediction in attended home delivery

Successful attended home delivery (AHD) is the most important aspect of e-commerce order fulfillment. Prior literature focuses on incentive scheme development for customers' choices of delivery windows and predictive analytics for delivery results, but it is not clear whether the effect of AHD on the appointment date set by customers increases the success rate of AHD. Therefore, we developed an uplift modeling method, PSM-NDML, as a relevant prescriptive analytic tool for AHD on an appointment date, which aims to estimate the causal effect of the by-appointment delivery on the delivery result. PSM-NDML integrates propensity score matching and double machine learning, effectively addressing sample selection bias, low predictive performance, and poor interpretability. Applied to a real-world product delivery dataset of a Chinese logistics company, PSM-NDML achieves superior performance relative to ten other state-of-the-art uplift models in terms of cumulative gain and the Qini coefficient. The predicted responses gained from PSM-NDML are also visually interpreted at the global and local levels, which reveals various managerial insights. In practice, the findings expand managers' understanding of the heterogeneous effects of AHD on appointment dates and provide decision support for logistics companies in the development of home delivery plans.

Managing Concurrent Interactions in Online Time Slot Booking Systems for Attended Home Delivery

Many goods and services require the customer to be at home to receive the delivery. In the context of attended home delivery, customers can typically choose from a menu of delivery time slots. We consider the problem of dynamically managing the offered time slots and delivery bookings given the available fleet capacity. When multiple customers interact with the online booking system at the same time, this can lead to conflicts. Although managing such concurrent interactions is an important challenge in attended home delivery systems, it has not yet been addressed in the literature. We present a concurrency control strategy and several fast route planning approaches to manage time slots in real time. To combine fast response times with high quality slotting decisions, we introduce background procedures that use the time between successive order placements to improve the performance of the time slot offer and validation procedures. Our detailed computational experiments based on realistic instances provide insights into the effectiveness of our background procedures and the complex trade-offs between waiting times, valid orders, and invalid orders. We also discuss several relevant new areas of research in concurrency control for time slot management. Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant 438-213-204]. It is co-funded by ORTEC B.V. and AH.nl.

Multi-period time window assignment for attended home delivery

We study a multi-period stochastic variant of the Time Window Assignment Vehicle Routing Problem, where customers’ demands, locations, and service times are uncertain. Customers are partitioned into geographical zones, each of which has to be visited a predetermined number of times over a planning period of several days. Whenever a zone is visited, a time window is assigned. Time windows are decided before knowing customers and their demands. A fleet of homogeneous vehicles is available to serve customers each day. At a tactical level, the problem looks for a static time window assignment that minimizes the expected traveling costs plus the expected penalty costs for unserved customers. We propose a two-stage formulation and a solution approach, which relies on the Sample Average Approximation Method, while encompassing a perturbation method to assign time windows in the first stage and an Adaptive Large Neighborhood Search to optimize routes in the second stage. We experimentally evaluate three instance sets, including real ones from a Canadian company, comparing our results to lower bounds from the exact solution of a deterministic equivalent formulation over a finite number of scenarios. Our method outperforms the manual approach used by the company.

Customer Preferences for Delivery Service Attributes in Attended Home Delivery

Retailers face increasing competitive pressure to determine how best to deliver products purchased online to the end customer. Grocery retailers often require attended home delivery where the customer must be present to receive the delivery. For attended home delivery to function, the retailer and customer must agree on a delivery time slot that works for both parties. Using online data from a grocery retailer, we observe customer preferences for three delivery service attributes associated with each time slot: speed, precision, and timing. We define speed as the expected time between the placement of an order and its delivery, precision as the duration of the offered time slot, and timing as the availability of choices across times of the day and days of the week. We show that customers not only value speed as an attribute of delivery service but that precision and timing are also key drivers of the customer’s time slot selection process. We also observe substantial customer heterogeneity in the willingness of customers to pay for time slots. Customers that differ in their loyalty to the retailer, basket value, basket size, and basket composition exhibit distinct differences in their willingness to pay. We show that retailers with the capability to tailor their time slot offerings to specific customer segments have the potential to generate approximately 9% more shipping revenue than those who cannot. Our findings inform practitioners seeking to design competitive fulfillment strategies and academics modeling customer behavior in the attended home delivery context. This paper was accepted by Vishal Gaur, operations management. Funding: This work was supported by the Fundação para a Ciência e a Tecnologia [PTDC/EME-SIS/31908/2017]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2020.01274 .

Service time window selection for attended home deliveries: a case study for urban and rural areas

Attended home deliveries are one of the most challenging logistics services with different customer expectations and challenges in urban and rural areas. For different demand densities, retailers must strike a balance between providing excellent customer service and optimizing routing efficiency. While customers often expect delivery promises with narrow time windows, research has demonstrated that longer time windows can increase the flexibility and the ability to accept more customers. However, it is not clear how different demand densities impact flexibility and customer acceptance. To serve as many customers as possible with excellent service quality, this paper reviews and expands on ideas for offering short and long time windows in a flexible manner in urban and rural areas. This study proposes different methods for providing customers with time windows of different lengths and investigates their performance based on a case study in Vienna and Upper Austria.

A survey of attended home delivery and service problems with a focus on applications

A survey of attended home delivery and service problems with a focus on applications

Strategies to improve customer service in delivery time slot management

AbstractIn several online retail contexts, such as grocery retailing, customers have to be present at the moment of delivery, that is, an attended home delivery service is in place. This requirement adds new challenges to this channel, often leading to narrow profitability. From an operations perspective, this service is performed with the retailer offering multiple time slots for the customer to choose from. Retailers target a cost‐efficient delivery process that also accounts for customers' preferences by properly managing the options to show to customers, that is, time slot management. This study analyzes a dynamic slotting problem, that is, choosing the best slots to show for each customer, which is close to many practical cases pursuing a customer service orientation. We study two new strategies to improve customer service while satisfying cost‐efficiency goals: (i) enforcing a constraint on the minimum number or percentage of slots to show to customers and (ii) integrating multiple days when tackling this challenging problem. Our results show under which conditions these proposed strategies can lead to win‐win situations for both customer service and profit.

A greener last mile: Analyzing the carbon emission impact of pickup points in last-mile parcel delivery

This paper analyzes the carbon emission impact of pickup points in last-mile parcel delivery. Pickup points provide customers and delivery companies with an alternative to attended home delivery. The delivery company can drop a parcel off at the pickup point, such as a service desk in a grocery store or a parcel locker, from where the customer collects the parcel. Because of the potential efficiency gains for the delivery vehicle, pickup points are often presented as a sustainable alternative to home delivery. The efficiency gains for the delivery vehicle need to be weighed against customers traveling to the pickup point by car, however. The mathematical analysis presented in this paper integrates continuous approximation techniques to assess the potential for improved delivery route efficiency with multinomial logistic regression for estimating the travel distance and mode choice of customers collecting their parcels. The results challenge the suggestion that pickup points are a universally sustainable alternative to home delivery. The potential for a net positive carbon emission impact is greatest when pickup points are established in urban settings, while in rural settings, the carbon emission benefits derived from improved delivery route efficiency are quickly offset by the carbon footprint associated with customer travel.

Making opportunity sales in attended home delivery

Research on time window management in attended home delivery mainly focuses on influencing customers’ delivery time choices to reduce monetary and environmental costs. In this study, we adopt a different perspective and propose exploiting otherwise idle time and vehicle capacities to generate extra profits through opportunity sales for e-groceries. We consider nudging potential target customers (residing in locations that are ”easy” to insert into the delivery tours) with push notifications to generate new sales. These customers are incentivized for purchases by dropping the terms imposed on standard e-grocery sales such as service fees or minimum purchase quantities. Managing the delivery operations for this innovative business model requires concurrently choosing the target customers and planning the vehicle routes under the offer acceptance and response time uncertainties. To solve this challenging problem, we propose an integer linear programming model that enables a decomposition of the problem into a routing problem and several customer selection problems. For the solution to the customer selection problems, we propose mathematical models with varying risk-taking levels. We also investigate the benefits of dynamic policies to take advantage of the information revealed during the delivery operations in order to adjust customer selection and vehicle routing decisions. Our extensive numerical experiments show that these models, equipped with dynamic decision making, can compete with the risk-ignorant models for the total profit while generating more sales per offer, as well as ensuring timely execution of the delivery operations.

Mobile parcel lockers with individual customer service

AbstractThe ongoing growth of e‐commerce deliveries has led to a significant increase in last‐mile delivery volumes. New technologies are being investigated to provide these deliveries efficiently and in a customer‐friendly manner. A common practice is to use fixed parcel lockers (FPLs) to make deliveries independent from the presence of the customer as is the case in attended home deliveries (AHDs). FPLs are usually installed at key locations in cities, and customers can collect their package at any time once it has been delivered to this particular location. Mobile parcel lockers (MPLs) represent a new idea: they can be parked for temporary collection of items at different locations, keeping the pickup distance to the customer short and avoiding high infrastructure costs. However, customers need to collect their parcels within a restricted time window. This service is supposed to become especially efficient through autonomously operating vehicles that can move MPLs at low costs. In this article, we introduce the heterogeneous locker location problem to study the effects of fleets combining two of the services—FPLs, MPLs, AHDs—within one framework. In our comparison, we consider that customers may have different expectations regarding their maximum pickup distance as well as their temporal flexibility in accepting deliveries. A fixed fleet is applied to maximize the number of customers served, respecting individual customer preferences in terms of pickup distances and time windows. We evaluate the different delivery services regarding managerial insights on service quality and efficiency. In the experiments, we analyze the impact of structural demand differences and different fleet sizes, as well as the operational fleet utilization and the individual customer experience. Results show the potential to increase the number of customers served by about 14%–19% through the use of MPLs while considering individual customer preferences.

Integrated optimization and decision support systems for attended home delivery and service problems

Integrated optimization and decision support systems for attended home delivery and service problems

Personalized choice model for forecasting demand under pricing scenarios with observational data—The case of attended home delivery

Discrete choice models can forecast market shares and individual choice probabilities with different price and alternative set scenarios. This work introduces a method to personalize choice models involving causal variables, such as price, using rich observational data. The model provides interpretable customer- and context-specific preferences, and price sensitivity, with an estimation procedure that uses orthogonalization. We caution against the naïve use of regularization to deal with the high-dimensional observational data challenge. We experiment with the attended home delivery (AHD) slot choice problem using data from a European online retailer. Our results indicate that while the popular non-personalized multinomial logit (MNL) model does very well at the aggregate (day–slot) level, personalization provides significantly and substantially more accurate predictions at the individual–context level. But the ”naïve” personalization approach using regularization without orthogonalization wrongly predicts that the choice probability will increase if the slot price increases, rendering it unfit for forecasting demand with pricing scenarios. The proposed method avoids this problem. Further, we introduce features based on potential consideration sets in the AHD slot choice context that increase accuracy and allow for more realistic substitution patterns than the proportional substitution implied by MNL.

An investigation on consumers' preferences for parcel deliveries: applying consumer logistics in omni-channel shopping

PurposeOmni-channel shopping affords consumers a variety of delivery options to receive products based on their preferred times and locations. By considering consumers' contributions (physical, social and attentive efforts) in co-creating delivery services, this study investigates their preferences for parcel delivery.Design/methodology/approachA scenario-based questionnaire survey is conducted for data collection in Singapore (n = 483). Furthermore, a multinomial logistic regression is performed to assess consumers' choice mode of delivery among five alternatives, that is attended home delivery, unattended home delivery, automated self-collection locker, attended pickup point and click-and-collect.FindingsCompared to attended home delivery, consumers who choose the alternatives are found to be more willing to contribute physical effort but less interested in responding attentively to informational updates. Efforts required for social interactions discourage consumers from choosing attended deliveries, prompting unattended alternatives (e.g. home delivery and self-collection) as more attractive choices. Additionally, socio-demographic factors and product value also influence consumers' preferences.Originality/valueThis study contributes to the literature by integrating the theoretical concept of consumer logistics into omni-channel studies, providing a new approach to examining consumers' channel behaviour. With detailed profiling that links product value and consumers' socio-demographics to their choice mode of delivery, the authors create practical insight into the optimal design of omni-channel distribution systems that best harness consumers' voluntary contributions.

Data-driven customer acceptance for attended home delivery

Home delivery services require the attendance of the customer during delivery. Hence, retailers and customers mutually agree on a delivery time window in the booking process. However, when a customer requests a time window, it is not clear how much accepting the ongoing request significantly reduces the availability of time windows for future customers. In this paper, we explore using historical order data to manage scarce delivery capacities efficiently. We propose a sampling-based customer acceptance approach that is fed with different combinations of these data to assess the impact of the current request on route efficiency and the ability to accept future requests. We propose a data-science process to investigate the best use of historical order data in terms of recency and amount of sampling data. We identify features that help to improve the acceptance decision as well as the retailer’s revenue. We demonstrate our approach with large amounts of real historical order data from two cities served by an online grocery in Germany.

An Adaptive Large Neighborhood Search heuristic for last-mile deliveries under stochastic customer availability and multiple visits

Attended Home Delivery, where customer attendance at home is required, is an essential last-mile delivery challenge, e.g., for valuable, perishable, or oversized items. Logistics service providers are often faced no-show customers. In this paper, we consider the delivery problem in which customers can be revisited on the same day by a courier in the case of a failed first delivery attempt. Specifically, customer presence uncertainty is considered in a two-stage stochastic program, where penalties are introduced as recourse actions for failed deliveries. We build on the notion of a customer availability profile defined as a profile containing historical time-varying probability information of successful deliveries. We tackle this stochastic program by developing an efficient parallelized Adaptive Large Neighborhood Search algorithm. Our results show that by achieving a right balance between increasing the hit rate and reducing travel cost, logistics service providers can realize costs savings as high as 32% if they plan for second visits on the same day.

Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeService ScienceVol. 15, No. 1 Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1Guest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif BenjaafarGuest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif BenjaafarPublished Online:13 Mar 2023https://doi.org/10.1287/serv.2023.0317Rapid developments in city infrastructure and technologies are creating numerous opportunities and inspiring innovative and emerging urban services. Among these innovations, complex systems of urban transportation and logistics have embraced advances and have been reshaped significantly. They enable innovative new urban services, which are now booming and changing everyday life for urban residents.This special issue of Service Science explores perspectives on innovation in transportation-enabled urban services. We hope that the special issue will enhance the understanding of the planning, operation, and management of such services. Contributions are expected to demonstrate rigorous model development, economic/econometric analysis, and decision-making tools based on optimization and/or data-driven approaches. Research in this special issue addresses the novel challenges and opportunities that arise in these transportation/logistics systems and relevant transportation-enabled urban services.The articles in the special issue represent an exciting diversity in research settings, methodologies, and applications. The articles are composed into two parts. Part 1 of the special issue includes four papers. In what follows, we provide a brief description of the papers in Part 1. The introduction of papers in Part 2 is rendered to the journal issue scheduled for September 2023.Yan et al. (2023) study an incentive mechanism for private parking-sharing programs in an imperfect information setting. They propose a matching-and-pricing mechanism for a driver’s demand-reporting problem in parking-sharing programs in which owners share their private parking slots with other drivers. They generate a driver-slot matching solution and show that, under the assumption that drivers do not know with certainty whether other drivers will show up to compete for the parking slots, the mechanism proposed induces drivers to truthfully report their private information about the travel plans and guarantees some desirable properties.Schwamberger et al. (2023) study the dynamic customer contacting for e-fulfillment in times of crisis. Specifically, the outbreak of the coronavirus disease 2019 pandemic increased demand for online grocery orders for both click & collect (C&C) and attended home delivery (AHD) and outstripped delivery capacity by a wide margin. They investigate a new demand management concept of proactively contacting customers and develop a decision policy for the C&C fulfillment method to address the problem of when to contact customers. They also extend this approach to the AHD setting and propose a three-step procedure to solve this problem.van der Gaast and Arslan (2023) explore the last-mile logistics operations of instant grocery delivery services. They introduce an instant delivery problem to replicate and examine two widely adopted strategies in the rapid delivery market: the personal shopper system (PSS) and the inventory owned delivery (IOD) system. They propose a tailored rolling horizon framework that utilizes column generation to browse updated delivery plans for arriving customer orders. Computational studies show that the PSS is a highly competitive strategy compared with IOD, particularly when dealing with small-sized customer orders.Ke and Mo (2023) study a one-way car-sharing system with both station-based and free-floating service types, which apply the two types in different parts of the service area. They establish a mixed-integer linear-programming model to maximize the operator profit, considering both strategic decisions of service type and operational decisions of relocation and trip selection. They propose a heuristic algorithm to effectively solve the model. Computational results demonstrate that the integrated system achieves a larger profit and higher demand fulfillment rate compared with the single station-based or free-floating system.ReferencesKe H, Mo Y (2023) Station-based or free-floating? An integrated carsharing system with dual service types. Service Sci. 15(1):58–76.Google ScholarSchwamberger J, Fleischmann M, Strauss A (2023) Feeding the nation—dynamic customer contacting for e-fulfillment in times of crisis. Service Sci. 15(1):22–40.Google Scholarvan der Gaast JP, Arslan AM (2023) Personal shopper systems in last-mile logistics. Service Sci. 15(1):41–57.Google ScholarYan P, Cai X, Chu F, Ni D, He H (2023) An incentive mechanism for private parking-sharing programs in an imperfect information setting. Service Sci. 15(1):3–21.Google Scholar Back to Top Next FiguresReferencesRelatedInformation Volume 15, Issue 1March 2023Pages 1-78, C3 Article Information Metrics Information Published Online:March 13, 2023 Copyright © 2023, INFORMSCite asGuest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif Benjaafar (2023) Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1. Service Science 15(1):1-2. https://doi.org/10.1287/serv.2023.0317 PDF download

Advancing the marketing‐operations interface in omnichannel retail

Advancing the marketing‐operations interface in omnichannel retail