A learning and optimization framework for collaborative urban delivery problems with alliances

The emergence of e-Commerce imposes a tremendous strain on urban logistics which in turn raises concerns on environmental sustainability if not performed efficiently. While large logistics service providers (LSPs) can perform fulfillment sustainably as they operate extensive logistic networks, last-...

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Main Authors: YANG, Jingfeng, LAU, Hoong Chuin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Online Access:https://ink.library.smu.edu.sg/sis_research/6231
https://ink.library.smu.edu.sg/context/sis_research/article/7234/viewcontent/ICCL_2021_Learning_and_Optimization_Framework_Alliance.pdf
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spelling sg-smu-ink.sis_research-72342021-11-02T09:23:05Z A learning and optimization framework for collaborative urban delivery problems with alliances YANG, Jingfeng LAU, Hoong Chuin The emergence of e-Commerce imposes a tremendous strain on urban logistics which in turn raises concerns on environmental sustainability if not performed efficiently. While large logistics service providers (LSPs) can perform fulfillment sustainably as they operate extensive logistic networks, last-mile logistics are typically performed by small LSPs who need to form alliances to reduce delivery costs and improve efficiency, and to compete with large players. In this paper, we consider a multi-alliance multi-depot pickup and delivery problem with time windows (MAD-PDPTW) and formulate it as a mixed-integer programming (MIP) model. To cope with large-scale problem instances, we propose a two-stage approach of deciding how LSP requests are distributed to alliances, followed by vehicle routing within each alliance. For the former, we propose machine learning models to learn the values of delivery costs from past delivery data, which serve as a surrogate for deciding how requests are assigned. For the latter, we propose a tabu search heuristic. Experimental results on a standard dataset and a real case in Singapore show that our proposed learning-based optimization framework is efficient and effective in outperforming the direct use of tabu search in most instances. Using our approach, we demonstrate that substantial savings in costs and hence improvement in sustainability can be achieved when these LSPs form alliances and requests are optimally assigned to these alliances. 2021-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6231 info:doi/10.1007/978-3-030-87672-2_21 https://ink.library.smu.edu.sg/context/sis_research/article/7234/viewcontent/ICCL_2021_Learning_and_Optimization_Framework_Alliance.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Alliances Collaboration Machine Learning Pickup-and-delivery Tabu Search Artificial Intelligence and Robotics Operations Research, Systems Engineering and Industrial Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Alliances
Collaboration
Machine Learning
Pickup-and-delivery
Tabu Search
Artificial Intelligence and Robotics
Operations Research, Systems Engineering and Industrial Engineering
spellingShingle Alliances
Collaboration
Machine Learning
Pickup-and-delivery
Tabu Search
Artificial Intelligence and Robotics
Operations Research, Systems Engineering and Industrial Engineering
YANG, Jingfeng
LAU, Hoong Chuin
A learning and optimization framework for collaborative urban delivery problems with alliances
description The emergence of e-Commerce imposes a tremendous strain on urban logistics which in turn raises concerns on environmental sustainability if not performed efficiently. While large logistics service providers (LSPs) can perform fulfillment sustainably as they operate extensive logistic networks, last-mile logistics are typically performed by small LSPs who need to form alliances to reduce delivery costs and improve efficiency, and to compete with large players. In this paper, we consider a multi-alliance multi-depot pickup and delivery problem with time windows (MAD-PDPTW) and formulate it as a mixed-integer programming (MIP) model. To cope with large-scale problem instances, we propose a two-stage approach of deciding how LSP requests are distributed to alliances, followed by vehicle routing within each alliance. For the former, we propose machine learning models to learn the values of delivery costs from past delivery data, which serve as a surrogate for deciding how requests are assigned. For the latter, we propose a tabu search heuristic. Experimental results on a standard dataset and a real case in Singapore show that our proposed learning-based optimization framework is efficient and effective in outperforming the direct use of tabu search in most instances. Using our approach, we demonstrate that substantial savings in costs and hence improvement in sustainability can be achieved when these LSPs form alliances and requests are optimally assigned to these alliances.
format text
author YANG, Jingfeng
LAU, Hoong Chuin
author_facet YANG, Jingfeng
LAU, Hoong Chuin
author_sort YANG, Jingfeng
title A learning and optimization framework for collaborative urban delivery problems with alliances
title_short A learning and optimization framework for collaborative urban delivery problems with alliances
title_full A learning and optimization framework for collaborative urban delivery problems with alliances
title_fullStr A learning and optimization framework for collaborative urban delivery problems with alliances
title_full_unstemmed A learning and optimization framework for collaborative urban delivery problems with alliances
title_sort learning and optimization framework for collaborative urban delivery problems with alliances
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url https://ink.library.smu.edu.sg/sis_research/6231
https://ink.library.smu.edu.sg/context/sis_research/article/7234/viewcontent/ICCL_2021_Learning_and_Optimization_Framework_Alliance.pdf
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