Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Optimization Model for the Energy Supply Chain Management Problem of Supplier Selection in Emergency Procurementopen access

Authors
Noh, JiseongHwang, Seung-June
Issue Date
Jan-2023
Publisher
MDPI AG
Keywords
energy supply chain management; replenishment problem; emergency procurement; carbon emissions; rolling horizon
Citation
Systems, v.11, no.1, pp 1 - 13
Pages
13
Indexed
SSCI
SCOPUS
Journal Title
Systems
Volume
11
Number
1
Start Page
1
End Page
13
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/111649
DOI
10.3390/systems11010048
ISSN
2079-8954
Abstract
In energy supply chain management (ESCM), the supply chain members try to make long-term contracts for supplying energy stably and reducing the cost. Currently, optimizing ESCM is a complex problem with two social issues: environmental regulations and uncertainties. First, environmental regulations have been tightened in countries around the world, leading to eco-friendly management. As a result, it has become imperative for the energy buyer to consider not only the total operating cost but also carbon emissions. Second, the uncertainties, such as pandemics and wars, have had a serious impact on handling ESCM. Since the COVID-19 pandemic disrupted the supply chain, the supply chain members adopted emergency procurement for sustainable operations. In this study, we developed an optimization model using mixed-integer linear programming to solve ESCM with supplier selection problems in emergency procurement. The model considers a single thermal power plant and multiple fossil fuel suppliers. Because of uncertainties, energy demand may suddenly change or may not be supplied on time. To better manage these uncertainties, we developed a rolling horizon method (RHM), which is a well-known method for solving deterministic problems in mathematical programming models. To test the model and the RHM, we conducted three types of numerical experiments. First, we examined replenishment strategies and schedules under uncertain demands. Second, we conducted a supplier selection experiment within a limited budget and carbon emission regulations. Finally, we conducted a sensitivity analysis of carbon emission limits. The results show that our RHM can handle ESCM under uncertain situations effectively.
Files in This Item
Go to Link
Appears in
Collections
COLLEGE OF BUSINESS AND ECONOMICS > DIVISION OF BUSINESS ADMINISTRATION > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Hwang, Seung June photo

Hwang, Seung June
COLLEGE OF BUSINESS AND ECONOMICS (DIVISION OF BUSINESS ADMINISTRATION)
Read more

Altmetrics

Total Views & Downloads

BROWSE