Fuzzy Sets and Systems
The mean value of a fuzzy number
Fuzzy Sets and Systems - Fuzzy Numbers
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach
Computers and Industrial Engineering - Supply chain management
Proceedings of the 6th International Conference on Genetic Algorithms
On Possibilistic Mean Value and Variance of Fuzzy Numbers
On Possibilistic Mean Value and Variance of Fuzzy Numbers
Parallel machine scheduling models with fuzzy processing times
Information Sciences—Informatics and Computer Science: An International Journal
ISEE '03 Proceedings of the Electronics and the Environment, 2003. on IEEE International Symposium
Logistics distribution centers location problem and algorithm under fuzzy environment
Journal of Computational and Applied Mathematics
Multi-objective genetic algorithm for single machine scheduling problem under fuzziness
Fuzzy Optimization and Decision Making
A closed-loop logistic model with a spanning-tree based genetic algorithm
Computers and Operations Research
IEEE Transactions on Evolutionary Computation
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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.