Fighting the E-commerce Giants: Efficient Routing and Effective Consolidation for Local Delivery Platforms

Schrotenboer, Albert H.; Broek, Michiel A. J. uit het; Buijs, Paul; Ulmer, Marlin W.

doi:10.48550/arxiv.2108.12608

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Beside the aforementioned studies that assume a depot acts as the single source of goods to be delivered to end customers, there are other well-known crowd-shipping systems in which every good is sent from a specific origin location to a specific destination location. Such problems commonly denote the pickup-and-delivery problem (Savelsbergh & Sol, 1995;Schrotenboer et al, 2021). Dahle et al (2019) are the first to consider ODs into the pickup and delivery problem.…”

Section: Distribution Systems Using Parcel Lockers and Crowd-shippingmentioning

confidence: 99%

The two-echelon vehicle routing problem with time windows, intermediate facilities, and occasional drivers

Jodiawan

Schrotenboer

et al. 2023

Expert Systems with Applications

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Section: Distribution Systems Using Parcel Lockers and Crowd-shippingmentioning

confidence: 99%

The two-echelon vehicle routing problem with time windows, intermediate facilities, and occasional drivers

Jodiawan

Schrotenboer

et al. 2023

Expert Systems with Applications

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“…The studies by Arslan et al (2021), Schrotenboer et al (2021) are the closest ones in terms of the setting of the IDP as these papers explore the idea of retail stores as storage facilities for expedited deliveries. Nevertheless, both of these papers, similar to the restaurant meal delivery problem, consider a setting in which customers specify the store where orders will be collected.…”

Section: Last-mile Logisticsmentioning

confidence: 99%

Personal Shopper Systems in Last-Mile Logistics

Gaast

Arslan

2023

Service Science

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This paper explores the logistics operations of instant grocery delivery services. Therefore, we introduce the instant delivery problem (IDP) 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. In the PSS, couriers visit affiliated brick-and-mortar stores in the delivery area to pick up and purchase ordered products and then deliver them to customers. In the IOD system, couriers collect products from a single distribution center, or so-called dark store, in which the platform manages the inventory. Even though a PSS strategy is asset light because of the utilization of existing retailers in the area, maintaining a good level of on-time instant deliveries with the PSS is more complex than with IOD. This is because the PSS requires deciding which store to purchase ordered goods from, and picking and shopping at stores needs to be considered in the real-time decision process. We propose a tailored rolling horizon framework that utilizes column generation to browse updated delivery plans for arriving customer orders. Computational studies both in real life–inspired settings and in case studies on selected urban areas show that the PSS is a highly competitive strategy compared with IOD, particularly when dealing with small-sized customer orders. We observe that the performance of the PSS is robust when varying the delivery service time frame. The case studies also suggest that the PSS becomes even more competitive in areas where the retail store density is high. History: Special Issue—Innovation in Transportation-Enabled Urban Services. Funding: This work was supported by National Natural Science Foundation of China [Grant 72150410444] to J.P. van der Gaast. Supplemental Material: The e-companion is available at https://doi.org/10.1287/serv.2022.0310 .

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“…Finally, another close work is paper Schrotenboer et al (2021), where the authors study the problem of balancing the consolidation potential and delivery urgency of orders. The problem is a pickup-and-delivery problem with a fleet of vehicles and the objective is to maximize the expected customer satisfaction.…”

Section: Literature Reviewmentioning

confidence: 99%

Reinforcement Learning Approaches for the Orienteering Problem with Stochastic and Dynamic Release Dates

Li¹,

Archetti²,

Ljubić³

2022

Preprint

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In this paper, we study a sequential decision making problem faced by e-commerce carriers related to when to send out a vehicle from the central depot to serve customer requests, and in which order to provide the service, under the assumption that the time at which parcels arrive at the depot is stochastic and dynamic. The objective is to maximize the number of parcels that can be delivered during the service hours. We propose two reinforcement learning approaches for solving this problem, one based on a policy function approximation (PFA) and the second on a value function approximation (VFA). Both methods are combined with a look-ahead strategy, in which future release dates are sampled in a Monte-Carlo fashion and a tailored batch approach is used to approximate the value of future states. Our PFA and VFA make a good use of branch-and-cut-based exact methods to improve the quality of decisions. We also establish sufficient conditions for partial characterization of optimal policy and integrate them into PFA/VFA. In an empirical study based on 720 benchmark instances, we conduct a competitive analysis using upper bounds with perfect information and we show that PFA and VFA greatly outperform two alternative myopic approaches. Overall, PFA provides best solutions, while VFA (which benefits from a two-stage stochastic optimization model) achieves a better tradeoff between solution quality and computing time.

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Fighting the E-commerce Giants: Efficient Routing and Effective Consolidation for Local Delivery Platforms

Cited by 3 publications

References 21 publications

The two-echelon vehicle routing problem with time windows, intermediate facilities, and occasional drivers

The two-echelon vehicle routing problem with time windows, intermediate facilities, and occasional drivers

Personal Shopper Systems in Last-Mile Logistics

Reinforcement Learning Approaches for the Orienteering Problem with Stochastic and Dynamic Release Dates

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