In this section
@article{Zhang:2014:10.1016/j.cie.2014.05.002,
author = {Zhang, Q and Shah, N and Wassick, J and Helling, R and van, Egerschot P},
doi = {10.1016/j.cie.2014.05.002},
journal = {Computers and Industrial Engineering},
pages = {68--83},
title = {Sustainable supply chain optimisation: An industrial case study},
url = {http://dx.doi.org/10.1016/j.cie.2014.05.002},
volume = {74},
year = {2014}
}
TY - JOUR
AB - Sustainability plays a key role in the management of a successful and responsible business. When trying to improve the sustainability performance of a business, there are three major challenges that need to be addressed. First, assessment of sustainability requires consideration of not just economic, but also environmental and social impacts. Second, we need to find appropriate sustainability indicators and gather the necessary data in order to quantify sustainability performance. Finally, sustainability has to be seen in the context of the whole system, i.e. it has to include all activities along the supply chain. In this work, we consider all three aspects and propose a multi-objective optimisation framework for the optimisation of a sustainable supply chain. Three sustainability indicators have been considered, namely the total cost, GHG emissions and lead time. We apply this framework to an industrial test case using real-world data drawn from a Dow Chemical business. The results show clear trade-offs between the three different objectives. However, we can also observe that typically a considerable decrease in GHG emissions or lead time can already be achieved by only a relatively small increase in cost. The proposed framework enables us to determine such trade-off relations and consequently make decisions that improve the sustainability performance of the supply chain.
AU - Zhang,Q
AU - Shah,N
AU - Wassick,J
AU - Helling,R
AU - van,Egerschot P
DO - 10.1016/j.cie.2014.05.002
EP - 83
PY - 2014///
SN - 0360-8352
SP - 68
TI - Sustainable supply chain optimisation: An industrial case study
T2 - Computers and Industrial Engineering
UR - http://dx.doi.org/10.1016/j.cie.2014.05.002
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000340335000006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://www.sciencedirect.com/science/article/abs/pii/S0360835214001478?via%3Dihub
VL - 74
ER -