Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal International Journal of Enterprise Modelling
Patrisia Teresa Marsoit
Institute of Computare Science, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Data envelopment analysis for stochastic production and supply chain planning Hengki Tamando Sihotang; Patrisia Teresa Marsoit; Kouvelis Geovany Ortizan
International Journal of Enterprise Modelling Vol. 16 No. 3 (2022): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (535.788 KB) | DOI: 10.35335/emod.v16i3.63

Abstract

This research presents a stochastic Data Envelopment Analysis (DEA) model for production and supply chain planning. The objective is to evaluate the efficiency of decision-making units (DMUs) in a system considering the stochastic nature of inputs and outputs. The proposed model incorporates uncertainty by assuming normal distributions for the stochastic variables. The model formulates a linear programming problem to maximize the efficiency scores of DMUs subject to constraints that ensure the efficiency of the system. The weights assigned to DMUs and input variables provide insights into their relative importance. A numerical example is presented to demonstrate the application of the model, and the results highlight the efficiency scores and weights for the DMUs. The findings contribute to improving decision-making in production and supply chain systems under uncertain conditions. The developed model offers a practical tool for evaluating efficiency and identifying areas for improvement in real-world systems. Further research can explore extensions and variations of the model to enhance its applicability in different contexts
Co-Authors Hengki Tamando Sihotang Kouvelis Geovany Ortizan