Linear programming with interval coefficients

By John W. Chinneck, K. Ramadan. Journal of the Operational Research Society, 2000

369 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/1457a99e05df5dca4ed5c99703d9a31d4f4aa56f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Wei-guo Zhang
2. Pu Zhang
3. Yurui Fan
4. Guohe Huang
5. Milan Hladík
6. Yongjun Liu

In Numerical Analysis

1. Wei-guo Zhang
2. Pu Zhang
3. Yurui Fan
4. Guohe Huang
5. Milan Hladík
6. Yongjun Liu

Citations in the corpus

• Optimal value bounds in nonlinear programming with interval data (2011, background): “In the last years, many papers studied the problem of computing the range of optimal values of linear programming problems with data varying inside intervals (Chinneck and Ramadan 2000; Fiedler et al. 2006; Hladík 2007; Mráz 1998) among others.”
• A multi-period portfolio selection optimization model by using interval analysis (2013, methodology): “Following the widely used interval programming approaches, such as Inuiguchi and Sakawa (1995), Chinneck and Ramadan (2000) and Jiang et al. (2008), some applications of them can be found in dealing with portfolio selection problems.”
• An interactive method of tackling uncertainty in interval multiple objective linear programming (2009, background): “Nevertheless, although the DM prefers the most favorable situation, the results obtained through the algorithm do not allow the DM to take into account the worst case and the best case “scenarios” in order to perceive the risk at stake (see [ 4 ]).”
• An interactive approach for the multi-objective transportation problem with interval parameters (2015, background): “…is solved by considering the most favourable version of each objective function and largest feasible region; if r ¼ 1, then the model is solved by considering the less favourable version of each objective function and the narrowest feasible region (Urli and Nadeau 1992; Chineck and Ramadan 2000).”
• A Robust Two-Step Method for Solving Interval Linear Programming Problems within an Environmental Management Context (2012, methodology): “Among them, IMP was an effective approach in dealing with uncertainties, since it does not require distributional information for input parameters (Tong, 1994; Chinneck and Ramada, 2000; Sengupta et al., 2001; Fiedler et al., 2006; Oliveira and Antunes, 2007). Huang et al. (1992) proposed an interva”