An aspect of discrepancy in the implementation of modus ponens in the presence of fuzzy quantities
International Journal of Approximate Reasoning
Database models and managerial institution: 50% model + 50% manager
Management Science
Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part II
Fuzzy Sets and Systems
Genetic algorithms for learning in fuzzy relational structures
Fuzzy Sets and Systems
What are fuzzy rules and how to use them
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives
Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Real-time supply chain control via multi-agent adjustable autonomy
Computers and Operations Research
Backtracking search algorithm for satisfiability degree calculation
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
An algorithm for calculating the satisfiability degree
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
A collaborative demand forecasting process with event-based fuzzy judgements
Computers and Industrial Engineering
Forecasting model selection through out-of-sample rolling horizon weighted errors
Expert Systems with Applications: An International Journal
A dual hybrid forecasting model for support of decision making in healthcare management
Advances in Engineering Software
| Hi-index | 0.21 |
In this paper, a new decision support system for demand forecasting DSS_DF is presented. A demand forecast is generated in DSS_DF by combining four forecasts values. Two of them are obtained independently, one by a customer and the other by a market expert. They represent subjective judgments on future demand, given as linguistic values, such as ''demand is around a certain value'' or ''demand is not lower than a certain value'', etc. Two additional forecasts are crisp values, obtained using conventional statistical methods, one using time-series analysis based on decomposition (TSAD), and the other using an auto regressive integrated moving average (ARMA) model. The combination of these four forecast values into one improved forecast is made by applying fuzzy IF-THEN rules. A modified Mamdani-style inference is used, which enables reasoning with fuzzy inputs. A new learning mechanism is developed and incorporated into the DSS_DF to adapt the rule bases that combine the individual forecasted values. The rule bases are adapted taking into consideration the performance of each of the forecast methods recorded in the past. The application of DSS_DF is demonstrated by an illustrative example. The forecasts obtained by DSS_DF are compared with results procured by applying the conventional TSAD and ARMA methods separately. The results obtained are encouraging and indicate that combining forecasts obtained by different methods may be beneficial.