Abstract
In recent years, Reverse Logistics (RL) has been touted as one of the strategies of improving organization performance and generating a competitive advantage. In RL, the generic routing problem has become a focus since it provides a great flexibility in modeling, e.g., selection of suppliers by using a node as a supplier candidate in a network. To date, complicated networks make decision makers hard to search a desired routine. In addition, the traditional network defines and resolves such a problem only at one soot. The solution cannot be acquired from multiple perspectives like minimal cost, minimal delivery time, maximal reliability, and optimal “3Rs”—reduce, reuse, and recycle. In this study, rough set theory is applied to reduce complexity of the RL data sets and induct decision rules. Through incorporating the decision rules, the generic label correcting algorithm is used to solve generic routing problems by integrating various operators and comparators in the GLC algorithm. Consequently, the desired RL suppliers are selected.
| Original language | English |
|---|---|
| Pages (from-to) | 781-795 |
| Number of pages | 15 |
| Journal | Journal of Intelligent Manufacturing |
| Volume | 27 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2016 Aug 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Industrial and Manufacturing Engineering
- Artificial Intelligence
Cite this
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The rough set based approach to generic routing problems : case of reverse logistics supplier selection. / Huang, Chun Che; Liang, Wen Yau; Tseng, Tzu Liang; Chen, Ping Houa.
In: Journal of Intelligent Manufacturing, Vol. 27, No. 4, 01.08.2016, p. 781-795.Research output: Contribution to journal › Article
TY - JOUR
T1 - The rough set based approach to generic routing problems
T2 - case of reverse logistics supplier selection
AU - Huang, Chun Che
AU - Liang, Wen Yau
AU - Tseng, Tzu Liang
AU - Chen, Ping Houa
PY - 2016/8/1
Y1 - 2016/8/1
N2 - In recent years, Reverse Logistics (RL) has been touted as one of the strategies of improving organization performance and generating a competitive advantage. In RL, the generic routing problem has become a focus since it provides a great flexibility in modeling, e.g., selection of suppliers by using a node as a supplier candidate in a network. To date, complicated networks make decision makers hard to search a desired routine. In addition, the traditional network defines and resolves such a problem only at one soot. The solution cannot be acquired from multiple perspectives like minimal cost, minimal delivery time, maximal reliability, and optimal “3Rs”—reduce, reuse, and recycle. In this study, rough set theory is applied to reduce complexity of the RL data sets and induct decision rules. Through incorporating the decision rules, the generic label correcting algorithm is used to solve generic routing problems by integrating various operators and comparators in the GLC algorithm. Consequently, the desired RL suppliers are selected.
AB - In recent years, Reverse Logistics (RL) has been touted as one of the strategies of improving organization performance and generating a competitive advantage. In RL, the generic routing problem has become a focus since it provides a great flexibility in modeling, e.g., selection of suppliers by using a node as a supplier candidate in a network. To date, complicated networks make decision makers hard to search a desired routine. In addition, the traditional network defines and resolves such a problem only at one soot. The solution cannot be acquired from multiple perspectives like minimal cost, minimal delivery time, maximal reliability, and optimal “3Rs”—reduce, reuse, and recycle. In this study, rough set theory is applied to reduce complexity of the RL data sets and induct decision rules. Through incorporating the decision rules, the generic label correcting algorithm is used to solve generic routing problems by integrating various operators and comparators in the GLC algorithm. Consequently, the desired RL suppliers are selected.
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UR - http://www.scopus.com/inward/citedby.url?scp=84901581274&partnerID=8YFLogxK
U2 - 10.1007/s10845-014-0913-8
DO - 10.1007/s10845-014-0913-8
M3 - Article
AN - SCOPUS:84901581274
VL - 27
SP - 781
EP - 795
JO - Journal of Intelligent Manufacturing
JF - Journal of Intelligent Manufacturing
SN - 0956-5515
IS - 4
ER -