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Enhanced-interval linear programming
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DOI：10.1016/j.ejor.2008.12.019.png)
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An enhanced-interval linear programming (EILP) model and its solution algorithm have been developed that incorporate enhanced-interval uncertainty (e.g., A(+/-), B-+/- and C-+/-) in a linear optimization framework. As a new extension of linear programming, the EILP model has the following advantages. Its solution space is absolutely feasible compared to that of interval linear programming (ILP), which helps to achieve insight into the expected-value-oriented trade-off between system benefits and risks of constraint violations. The degree of uncertainty of its enhanced-interval objective function (EIOF) would be lower than that of ILP model when the solution space is absolutely feasible, and the EIOF's expected value could be used as a criterion for generating the appropriate alternatives, which help decision-makers obtain non-extreme decisions. Moreover, because it can be decomposed into two submodels. EILP's computational requirement is lower than that of stochastic and fuzzy LP models. The results of a numeric example further indicated the feasibility and effectiveness of EILP model. In addition, El nonlinear programming models, hybrid stochastic or fuzzy EILP models as well as risk-based trade-off analysis for El uncertainty within decision process can be further developed to improve its applicability. (C) 2008 Elsevier B.V. All rights reserved.
Keyword:
Linear programming
Optimization
Uncertainty
Feasibility
Solution algorithm
Risk-based decision making
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6
论文数:
2.2W
被引数:
6.4W
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