Assessing manufacturing flow lines under uncertainties in processing time: An application based on max-plus equations, multicriteria decisions, and global sensitivity analysis
Claudio M. Rocco,
Elvis Hernandez-Perdomo and
Johnathan Mun
International Journal of Production Economics, 2021, vol. 234, issue C
Abstract:
In this paper, a novel application on how uncertainties in a manufacturing flow line − MFL (e.g., times required to perform an action) could be analyzed and what the benefits are of such analysis. The approach proposed investigates three main goals: i) Uncertainty analysis, ii) Stochastic dominance, and iii) Sensitivity analysis. In particular, this paper extends the application of max-plus algebra to model MFL with different flow configurations and buffer capacities and provides the approximated probability density functions (PDFs) of selected performance indicators (e.g., the total idle time in the whole line, output rates, throughputs, among others). As a result, it is possible to quantify the variability of the selected output, compare different possible configurations among MFL, choose the best one, and identify critical variables and risk drivers (e.g., the processing times that affect the most a KPI − key performance indicator). The approach, illustrated by analyzing a case study of the literature, emphasizes the benefits for a decision-maker in charge of the design or managing of the manufacturing system.
Keywords: Global sensitivity analysis; Manufacturing flow line; Max-plus algebra; Monte Carlo simulation; Uncertainty; Stochastic dominance; TOPSIS (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0925527321000463
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:234:y:2021:i:c:s0925527321000463
DOI: 10.1016/j.ijpe.2021.108070
Access Statistics for this article
International Journal of Production Economics is currently edited by Stefan Minner
More articles in International Journal of Production Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().