Simulation-Based Efficiency Assessment of Integrated First-Mile Pickup and Last-Mile Delivery in an E-Commerce Logistics Network

Ranathunga, M. I. D.; Wijayanayake, A.; Niwunhella, D. H. H.

doi:10.1109/scse56529.2022.9905083

2022 International Research Conference on Smart Computing and Systems Engineering (SCSE) 2022

DOI: 10.1109/scse56529.2022.9905083

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Simulation-Based Efficiency Assessment of Integrated First-Mile Pickup and Last-Mile Delivery in an E-Commerce Logistics Network

M. I. D. Ranathunga

A. Wijayanayake

D. H. H. Niwunhella

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Cited by 3 publications

References 33 publications

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Improving freight parking needs in Brazilian cities with city logistics initiatives

Silva,

Labegalini,

Lima

et al. 2024

Case Studies on Transport Policy

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Improving freight parking needs in Brazilian cities with city logistics initiatives

Silva,

Labegalini,

Lima

et al. 2024

Case Studies on Transport Policy

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Optimization algorithms for adaptative route sequencing on real-world last-mile deliveries

Hernandez,

Sotelo,

Forets

2024

ings

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This article explores the design and application of machine learning techniques to enhance traditional approaches for solving NP-hard optimization problems. Specifically, it focuses on the Last-Mile Routing Research Challenge (LMRRC), supported by Amazon and MIT, which sought innovative solutions for cargo routing optimization. While the challenge provided travel times and zone identifiers, the dependency on these factors raises concerns about the algorithms’ generalizability to different contexts and regions with standard delivery services registries. To address these concerns, this study proposes personalized cost matrices that incorporate both distance and time models, along with the relationships between delivery stops. Additionally, it presents an improved approach to sequencing stops by combining exact and approximate algorithms, utilizing a customized regression technique alongside fine-tuned metaheuristics and heuristics refinements. The resulting methodology achieves competitive scores on the LMRRC validation dataset, which comprises routes from the USA. By carefully delineating route characteristics, the study enables the selection of specific technique combinations for each route, considering its geometrical and geographical attributes. Furthermore, the proposed methodologies are successfully applied to real-case scenarios of last-mile deliveries in Montevideo (Uruguay), demonstrating similar average scores and accuracy on new testing routes. This research contributes to the advancement of last-mile delivery optimization by leveraging personalized cost matrices and algorithmic refinements. The findings highlight the potential for improving existing approaches and their adaptability to diverse geographic contexts, paving the way for more efficient and effective delivery services in the future.

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Study of Urban Logistics Drone Path Planning Model Incorporating Service Benefit and Risk Cost

Shao

et al. 2022

Drones

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The application of drones provides a powerful solution for “the last-mile” logistics services, while the large-scale implementation of logistics drone services will threaten the safety of buildings, pedestrians, vehicles, and other elements in the urban environment. The balance of risk cost and service benefit is accordingly crucial to managing logistics drones. In this study, we proposed a cost-benefit assessment model for quantifying risk cost and service benefit in the urban environment. In addition, a global heuristic path search rule was developed to solve the path planning problem based on risk mitigation and customer service. The cost-benefit assessment model quantifies the risk cost from three environmental elements (buildings, pedestrians, and vehicles) threatened by drone operations based on the collision probability, and the service benefit based on the characteristics of logistics service customers. To explore the effectiveness of the model in this paper, we simulate and analyse the effects of different risk combinations, unknown risk zones, and risk-benefit preferences on the path planning results. The results show that compared with the traditional shortest-distance method, the drone path planning method proposed in this paper can accurately capture the distribution of risks and customers in the urban environment. It is highly reusable in ensuring service benefits while reducing risk costs and generating a cost-effective path for logistics drones. We also compare the algorithm in this paper with the A* algorithm and verify that our algorithm improves the solution quality in complex environments.

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Simulation-Based Efficiency Assessment of Integrated First-Mile Pickup and Last-Mile Delivery in an E-Commerce Logistics Network

Cited by 3 publications

References 33 publications

Improving freight parking needs in Brazilian cities with city logistics initiatives

Improving freight parking needs in Brazilian cities with city logistics initiatives

Optimization algorithms for adaptative route sequencing on real-world last-mile deliveries

Study of Urban Logistics Drone Path Planning Model Incorporating Service Benefit and Risk Cost

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