Paper Info
Title
A possibilistic approach to the modeling and resolution of uncertain closed-loop logistics
Abstract
Closed-loop logistics planning is an important tactic for the achievement of sustainable development. However, the correlation among the demand, recovery, and landfilling makes the estimation of their rates uncertain and difficult. Although the fuzzy numbers can present such kinds of overlapping phenomena, the conventional method of defuzzification using level-cut methods could result in the loss of information. To retain complete information, the possibilistic approach is adopted to obtain the possibilistic mean and mean square imprecision index (MSII) of the shortage and surplus for uncertain factors. By applying the possibilistic approach, a multi-objective, closed-loop logistics model considering shortage and surplus is formulated. The two objectives are to reduce both the total cost and the root MSII. Then, a non-dominated solution can be obtained to support decisions with lower perturbation and cost. Also, the information on prediction interval can be obtained from the possibilistic mean and root MSII to support the decisions in the uncertain environment. This problem is non-deterministic polynomial-time hard, so a new algorithm based on the spanning tree-based genetic algorithm has been developed. Numerical experiments have shown that the proposed algorithm can yield comparatively efficient and accurate results.
Year
DOI
Venue
2012
10.1007/s10700-012-9120-2
FO & DM
Keywords
Field
DocType
Closed-loop logistics,Fuzzy number,Possibilistic mean,Genetic algorithms,Shortage and surplus
Mathematical optimization,Defuzzification,Prediction interval,Spanning tree,Fuzzy number,Economic shortage,Total cost,Genetic algorithm,Complete information,Mathematics
Journal
Volume
Issue
ISSN
11
2
1568-4539
Citations 
PageRank 
References 
2
0.40
18
Authors
2
Name
Order
Citations
PageRank
Hsiao-Fan Wang127827.24
Hsin-Wei Hsu2441.79