TY - JOUR
T1 - A multi-depot pollution routing problem with time windows in e-commerce logistics coordination
AU - Zhang, Mengdi
AU - Chen, Aoxiang
AU - Zhao, Zhiheng
AU - Huang, George Q.
N1 - Funding information:
The authors express their gratitude to the Editor-in-Chief and the reviewers for their invaluable comments, which have greatly enhanced the quality of this article. It is important to note that all figures, tables, appendix materials and algorithms included in this work are original creations of the authors. This work is supported by the Jiangsu Province Natural Science Foundation (Grant No. BK20220382), National Natural Science Foundation of China (Grant No. 52305557), China Postdoctoral Science Foundation (Grant No. 2022M712394), SynchroHub, HK RGC TRS, No. T32-707/22-N, and iSPY, HK RGC RIF, No. R7036-22.
Publisher Copyright:
© 2023, Emerald Publishing Limited.
PY - 2024/1/2
Y1 - 2024/1/2
N2 - Purpose: This research explores mitigating carbon emissions and integrating sustainability in e-commerce logistics by optimizing the multi-depot pollution routing problem with time windows (MDPRPTW). A proposed model contrasts non-collaborative and collaborative decision-making for order assignment among logistics service providers (LSPs), incorporating low-carbon considerations. Design/methodology/approach: The model is substantiated using improved adaptive large neighborhood search (IALNS), tabu search (TS) and oriented ant colony algorithm (OACA) within the context of e-commerce logistics. For model validation, a normal distribution is employed to generate random demand and inputs, derived from the location and requirements files of LSPs. Findings: This research validates the efficacy of e-commerce logistics optimization and IALNS, TS and OACA algorithms, especially when demand follows a normal distribution. It establishes that cooperation among LSPs can substantially reduce carbon emissions and costs, emphasizing the importance of integrating sustainability in e-commerce logistics optimization. Research limitations/implications: This paper proposes a meta-heuristic algorithm to solve the NP-hard problem. Methodologies such as reinforcement learning can be investigated in future work. Practical implications: This research can help logistics managers understand the status of sustainable and cost-effective logistics operations and provide a basis for optimal decision-making. Originality/value: This paper describes the complexity of the MDPRPTW model, which addresses both carbon emissions and cost reduction. Detailed information about the algorithm, methodology and computational studies is investigated. The research problem encompasses various practical aspects related to routing optimization in e-commerce logistics, aiming for sustainable development.
AB - Purpose: This research explores mitigating carbon emissions and integrating sustainability in e-commerce logistics by optimizing the multi-depot pollution routing problem with time windows (MDPRPTW). A proposed model contrasts non-collaborative and collaborative decision-making for order assignment among logistics service providers (LSPs), incorporating low-carbon considerations. Design/methodology/approach: The model is substantiated using improved adaptive large neighborhood search (IALNS), tabu search (TS) and oriented ant colony algorithm (OACA) within the context of e-commerce logistics. For model validation, a normal distribution is employed to generate random demand and inputs, derived from the location and requirements files of LSPs. Findings: This research validates the efficacy of e-commerce logistics optimization and IALNS, TS and OACA algorithms, especially when demand follows a normal distribution. It establishes that cooperation among LSPs can substantially reduce carbon emissions and costs, emphasizing the importance of integrating sustainability in e-commerce logistics optimization. Research limitations/implications: This paper proposes a meta-heuristic algorithm to solve the NP-hard problem. Methodologies such as reinforcement learning can be investigated in future work. Practical implications: This research can help logistics managers understand the status of sustainable and cost-effective logistics operations and provide a basis for optimal decision-making. Originality/value: This paper describes the complexity of the MDPRPTW model, which addresses both carbon emissions and cost reduction. Detailed information about the algorithm, methodology and computational studies is investigated. The research problem encompasses various practical aspects related to routing optimization in e-commerce logistics, aiming for sustainable development.
KW - Carbon emission
KW - Coordination
KW - E-commerce logistics
KW - Metaheuristic
KW - Pollution routing problem
UR - http://www.scopus.com/inward/record.url?scp=85173728126&partnerID=8YFLogxK
U2 - 10.1108/IMDS-03-2023-0193
DO - 10.1108/IMDS-03-2023-0193
M3 - Journal article
AN - SCOPUS:85173728126
SN - 0263-5577
VL - 124
SP - 85
EP - 119
JO - Industrial Management and Data Systems
JF - Industrial Management and Data Systems
IS - 1
ER -