Volume 7, Issue 4, August 2014, Pages 696 - 705
A Synthesizing Effect-Based Solution Method for Stochastic Rough Multi-objective Programming Problems
Lei Zhou, Guoshan Zhang, Fachao Li
Available Online 1 August 2014.
- https://doi.org/10.1080/18756891.2013.856255How to use a DOI?
- Multi-objective Programming, Random rough variable, Stochastic Programming, Genetic algorithm, Synthesis effect
- Multi-objective programming with uncertain information has been widely applied in modeling of industrial produce and logistic distribution problems. Usually the expectation value model and chance-constrained model as solution models are used to deal with such uncertain programming. In this paper, we consider the uncertain programming problem which contains random information and rough information and is hard to be solved. A new solution model, called stochastic rough multi-objective synthesis effect (MOSE) model, is developed to deal with a class of multi-objective programming problems with random rough coefficients. The MOSE model contains expectation value model and chance-constrained model by choosing different synthesis effect functions and can be considered as an extension of crisp multi-objective programming model. Combined with genetic algorithm, the optimal solution of the MOSE model can be obtained. It shows that the solutions of the MOSE model are better than that of other solution models. Finally, an illustrative example is provided to show the effectiveness of the proposed method.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Lei Zhou AU - Guoshan Zhang AU - Fachao Li PY - 2014 DA - 2014/08 TI - A Synthesizing Effect-Based Solution Method for Stochastic Rough Multi-objective Programming Problems JO - International Journal of Computational Intelligence Systems SP - 696 EP - 705 VL - 7 IS - 4 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2013.856255 DO - https://doi.org/10.1080/18756891.2013.856255 ID - Zhou2014 ER -