Firm Failure Timeline Prediction: Math Programming Approaches

Author(s):  
Young U. Ryu
Keyword(s):  
2008 ◽  
Vol 29 (1) ◽  
pp. 5-8 ◽  
Author(s):  
Lindy Crawford ◽  
Leanne R. Ketterlin-Geller

2011 ◽  
Vol 35 (11) ◽  
pp. 2366-2377 ◽  
Author(s):  
Sudhakar Jonnalagadda ◽  
Balaji Balagurunathan ◽  
Rajagopalan Srinivasan

2020 ◽  
Vol 54 (6) ◽  
pp. 1439-1445
Author(s):  
Prahalad Venkateshan

In this paper, it is shown that the polynomially bounded enumerative procedure to solve the facility location problem with limited distances, originally described by Drezner, Mehrez, and Wesolowsky [Drezner Z, Mehrez A, Wesolowsky GO (1991) The facility location problem with limited distances. Transportation Sci. 25(3):183–187.], and subsequently corrected by Aloise, Hansen, and Liberti [Aloise D, Hansen P, Liberti L (2012) An improved column generation algorithm for minimum sum-of-squares clustering. Math. Programming 131(1–2):195–220.], can still fail to optimally solve the problem. Conditions under which the procedures succeed are identified. A new modified algorithm is presented that solves the facility location problem with limited distances. It is further shown that the proposed correction is complete in that it does not require further corrections.


Author(s):  
Robert L. Carraway

The problem set contains three problems designed to help students practice their ability to build math programming models. Problem # 1 is a portfolio problem where the student is asked to find a portfolio that minimizes risk (variance) subject to a required rate of return; as such, it is nonlinear. Problem # 2 is aggregate production scheduling; hence, linear. Problem # 3 involves determining how to source a fixed quantity from a menu of vendors with differing fixed- ordering charges and per-unit prices; it is a mixed integer model. All are sufficiently small that they can be easily optimized with standard math programming software (such as Excel's standard Solver).


1974 ◽  
pp. 90-123
Author(s):  
Michael Grigoriadis
Keyword(s):  

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