Abstract
Supply Chain Management (SCM) is a new approach to production planning. It integrates the components
of supply chain in a holistic manner. Modeling this large-scale system, which contains all effective
enterprises in production such as raw material suppliers, part manufacturers, assembly plants, distribution
organizations, and the like, is challenging for managers, engineers and researchers. This paper concentrates
on supply chain system modeling with fuzzy linear programming, and fuzzy expert system for an automobile
plant. First, a linear programming model is developed in such a way that while the input data is fuzzy, the
constraints are crisp. In the second linear model, the coefficients of the model are crisp while the constraints
are fuzzy. In the third model, we aggregate the first and the second models into one fuzzy linear programming
where all constraints and coefficients are fuzzy. In each case, we compare the results with those of classical
SC models. Finally, a rule based fuzzy expert system for SC is developed and the results are compared with
those of the classical and fuzzy LP models. The results of the fuzzy expert system show its superiority over
the former crisp and fuzzy linear programming models.
| Original language | English |
|---|---|
| Pages (from-to) | 178-196 |
| Journal | International Journal of Management Science and Engineering Management |
| Volume | 2 |
| Issue number | 3 |
| Publication status | Published - 2007 |
| Externally published | Yes |
Keywords
- Business and management studies