期刊
JOURNAL OF CLEANER PRODUCTION
卷 185, 期 -, 页码 105-127出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2018.02.255
关键词
sustainable supply chain; Robust optimization; Closed-loop supply chain; Data-driven approaches
资金
- Key Project of the Major Research Plan of the National Natural Science Foundation of China [91746210]
- National Science Foundation of China [71672011, 71432002]
- Beijing Natural Science Foundation [9172016]
- China Postdoctoral Science Foundation [2017M622287]
In this paper, the problem of sustainable closed-loop supply chain (CLSC) design under multi-uncertainties is studied. To identify an efficient way to enhance environmental and operational benefits of CLSC, we use Big Data and propose data-driven approaches to generating robust CLSC designs that mitigate uncertainty and greenhouse gas (GHG) emissions burdens. More specifically, in addressing multi-uncertainties (i.e., buyers' expectations, demands, and recovery uncertainties), a distributed robust optimization model (DRO) and an adaptive robust model (ARO) are developed for designing carryings and waste disposal facility locations of CLSC. Both models use historical data based on uncertain parameters for previous periods to make decisions on future stages in a robust way. Moreover, we incorporate K-L divergence into an ambiguous set of uncertain parameters to measure the value of data. The results of numerical analysis show the need to account for K-L divergence in an ambiguous set of DRO models, as GHG emission costs increase even when little K-L divergence disturbance is in place. Furthermore, from the data-driven framework, we find that government subsidies and an accurate estimation method (i.e., less K-L divergence) enhance environmental and operational benefits. Regarding model robustness levels, solutions generated from our ARO models outperform deterministic solutions not only in terms of their average objective value but also in terms of differences from ideal solutions. (C) 2018 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据