TY - GEN
T1 - A Mixed Integer Linear Programming for COVID-19 Related Medical Waste Reverse Logistics Network Design
AU - Santos, Aira Ronalie P.
AU - Maghfiroh, Meilinda Fitriani Nur
AU - Sapiter, John Rayam
AU - Prasetyo, Yogi Tri
AU - Redi, Anak Agung Ngurah Perwira
AU - Persada, Satria Fadil
AU - Young, Michael Nayat
AU - Nadlifatin, Reny
AU - Ardiansyahmiraja, Bobby
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/4/28
Y1 - 2022/4/28
N2 - Medical wastes bring probable hazards and public risks if not handled correctly, especially during the covid-19 pandemic. Waste management costs are rising due to continues devastation of covid-19 virus. Increased care and handling processes have been implemented to avoid further spreading the infection while ensuring the proper disposal. Given this, medical waste related to covid-19, including other wastes generated by medical facilities, requires delicate integration into the waste management process thru a reverse logistics network for cost-efficient collection, processing, and transport style. Using AMPL software as solving tool, this paper was designed to utilize a mixed-integer linear programming model of a reverse logistics network to improve medical waste management by identifying the total minimum cost of the waste disposal cycle from Waste generating facilities, treatment facilities, and to the disposal factory.
AB - Medical wastes bring probable hazards and public risks if not handled correctly, especially during the covid-19 pandemic. Waste management costs are rising due to continues devastation of covid-19 virus. Increased care and handling processes have been implemented to avoid further spreading the infection while ensuring the proper disposal. Given this, medical waste related to covid-19, including other wastes generated by medical facilities, requires delicate integration into the waste management process thru a reverse logistics network for cost-efficient collection, processing, and transport style. Using AMPL software as solving tool, this paper was designed to utilize a mixed-integer linear programming model of a reverse logistics network to improve medical waste management by identifying the total minimum cost of the waste disposal cycle from Waste generating facilities, treatment facilities, and to the disposal factory.
KW - COVID-19
KW - medical waste
KW - mixed integer linear programming
KW - reverse logistics
KW - waste management
UR - http://www.scopus.com/inward/record.url?scp=85135027893&partnerID=8YFLogxK
U2 - 10.1145/3535782.3535845
DO - 10.1145/3535782.3535845
M3 - Conference contribution
AN - SCOPUS:85135027893
T3 - ACM International Conference Proceeding Series
SP - 473
EP - 477
BT - MSIE 2022 - 2022 4th International Conference on Management Science and Industrial Engineering
PB - Association for Computing Machinery
T2 - 4th International Conference on Management Science and Industrial Engineering, MSIE 2022
Y2 - 28 April 2022 through 30 April 2022
ER -