Enhanced-interval linear programming

Feng Zhou

doi:10.1016/j.ejor.2008.12.019

Return

Enhanced-interval linear programming

2009-12-01

PRE

DOI：10.1016/j.ejor.2008.12.019

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

Linear programming

Optimization

Uncertainty

Feasibility

Solution algorithm

Risk-based decision making

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.