TY - JOUR
T1 - Grain supply chain network design and logistics planning for reducing post-harvest loss
AU - Nourbakhsh, Seyed Mohammad
AU - Bai, Yun
AU - Maia, Guilherme D.N.
AU - Ouyang, Yanfeng
AU - Rodriguez, Luis
N1 - Funding Information:
This research was supported in part by the ADM Institute for the Prevention of Post-harvest Loss at the University of Illinois, Urbana-Champaign. The very helpful feedback from the anonymous reviewers is also gratefully acknowledged.
Publisher Copyright:
© 2016 IAgrE
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/11/1
Y1 - 2016/11/1
N2 - In this paper we present a mathematical model for reducing post-harvest loss (PHL) in grain supply chain networks. The proposed model determines the optimal logistics for grain transportation and infrastructure investment by identifying the optimal locations for new pre-processing facilities and by optimising roadway/railway capacity expansion. The objective is to minimise the total system cost, including both infrastructure investment and economic cost from PHL. In this paper we incorporated both quality and quantity PHL during the transportation, transhipment, and pre-processing stages in the supply chain and considers different PHL rates for processed and unprocessed grains. Finally, we conducted a numerical analysis on a real-world network in the State of Illinois and a series of sensitivity analyses to provide insights into the optimal system design under different scenarios.
AB - In this paper we present a mathematical model for reducing post-harvest loss (PHL) in grain supply chain networks. The proposed model determines the optimal logistics for grain transportation and infrastructure investment by identifying the optimal locations for new pre-processing facilities and by optimising roadway/railway capacity expansion. The objective is to minimise the total system cost, including both infrastructure investment and economic cost from PHL. In this paper we incorporated both quality and quantity PHL during the transportation, transhipment, and pre-processing stages in the supply chain and considers different PHL rates for processed and unprocessed grains. Finally, we conducted a numerical analysis on a real-world network in the State of Illinois and a series of sensitivity analyses to provide insights into the optimal system design under different scenarios.
KW - Grain supply chain
KW - Infrastructure expansion
KW - Post-harvest loss
KW - Quality and quantity losses
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U2 - 10.1016/j.biosystemseng.2016.08.011
DO - 10.1016/j.biosystemseng.2016.08.011
M3 - Article
AN - SCOPUS:84987948304
SN - 1537-5110
VL - 151
SP - 105
EP - 115
JO - Biosystems Engineering
JF - Biosystems Engineering
ER -