Izvorni znanstveni članak
https://doi.org/10.17559/TV-20171115130250
Reducing Demand Signal Variability via a Quantitative Fuzzy Grey Regression Approach
Hakan Tozan
; School of Engineering and Natural Sciences, Istanbul Medipol University, Istanbul, 34810, Turkey
Mumtaz Karatas
orcid.org/0000-0002-6287-3216
; (1) Department of Industrial Engineering, Naval Academy, National Defense University, Istanbul, 34942, Turkey / (2) Department of Industrial Engineering, Bahcesehir University, Istanbul, 34349, Turkey
Ozalp Vayvay
; Faculty of Engineering, Marmara University, Istanbul, 34722, Turkey
Sažetak
The total system performance of dynamic and complex supply chain networks depends mainly on accurate demand signal estimation as incorporated with an appropriate decision-making process. Due to the field of activity and architecture, however, it is hard to choose a proper forecasting and demand decision model that would befit the complexity of the system. This paper develops a conjoint intelligent hybrid system, comprised of an adaptive neuro-fuzzy inference system (ANFIS) based demand decision process, integrated with crisp grey GM (1,1) and fuzzy grey regression (FGR) forecasting models. We adopt this approach in an attempt to reduce the demand signal variability in supply-chain networks and to evaluate the system response to the proposed models under predefined, relatively low, medium and high demand signal variations. The results obtained from the simulation runs illustrate that the proposed hybrid system reduces the variability considerably; and also, could be considered as a substantial tool for reduction of supply chain phenomenon so called Bullwhip effect.
Ključne riječi
ANFIS; demand signal processing; fuzzy forecasting; fuzzy logic; grey systems
Hrčak ID:
205938
URI
Datum izdavanja:
22.9.2018.
Posjeta: 1.659 *