Name
Abstract | ||
|---|---|---|
We consider the issue of supplier selection by using rule-based methodology. Supplier Selection (SS) is an important activity in Logistics and Supply Chain Management in today's global market. It is one of major applications of Multiple Criteria Decision Analysis (MCDA) that concerns about preference-related decision information. The rule-based methodology is proven of its effectiveness in handling preference information and performs well in sorting or ranking alternatives. However, how to utilize them in SS still remains open for more studies. In this paper, we propose a novel Believable Rough Set Approach (BRSA). This approach performs the complete problem-solving procedures including (1) criteria analysis, (2) rough approximation, (3) decision rule induction, and (4) a scheme for rule application. Unlike other rule-based solutions that just extract certain information, the proposed solution additionally extracts valuable uncertain information for rule induction. Due to such mechanism, BRSA outperforms other solutions in evaluation of suppliers. A detailed empirical study is provided for demonstration of decision-making procedures and multiple comparisons with other proposals. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.eswa.2013.07.014 | Expert Syst. Appl. |
Keywords | Field | DocType |
supplier selection,preference-related decision information,extracts valuable uncertain information,preference information,certain information,rule induction,rule-based solution,rule application,rule-based methodology,novel believable rough set,decision rule induction,logistics and supply chain management,multiple criteria decision analysis | Decision rule,Data mining,Multiple-criteria decision analysis,Ranking,Computer science,Rough set,Sorting,Supply chain management,Artificial intelligence,Rule induction,Machine learning,Empirical research | Journal |
Volume | Issue | ISSN |
41 | 1 | 0957-4174 |
Citations | PageRank | References |
19 | 0.61 | 23 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junyi Chai | 1 | 213 | 12.09 |
| James N. K. Liu | 2 | 529 | 44.35 |