A global method from predictive to prescriptive analytics considering prediction error for “Predict, then optimize” with an example of low-carbon logistics

Shuaian Wang; Ran Yan

doi:10.1016/j.clscn.2022.100062

A global method from predictive to prescriptive analytics considering prediction error for “Predict, then optimize” with an example of low-carbon logistics

Shuaian Wang, Ran Yan^*

^*Corresponding author for this work

Hong Kong Polytechnic University

Research output: Contribution to journal › Article › peer-review

22 Citations (Scopus)

Original language	English
Article number	100062
Journal	Cleaner Logistics and Supply Chain
Volume	4
DOIs	https://doi.org/10.1016/j.clscn.2022.100062
Publication status	Published - Jul 2022
Externally published	Yes

ASJC Scopus Subject Areas

Management of Technology and Innovation
Industrial and Manufacturing Engineering
Management, Monitoring, Policy and Law
Environmental Science (miscellaneous)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.clscn.2022.100062

Cite this

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A global method from predictive to prescriptive analytics considering prediction error for “Predict, then optimize” with an example of low-carbon logistics

ASJC Scopus Subject Areas

UN SDGs

Access to Document

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Cite this