A Strong Cutting Plane/Branch-and-Bound Algorithm for Node Packing

1992 ◽  
Vol 43 (5) ◽  
pp. 443 ◽  
Author(s):  
G. L. Nemhauser ◽  
G. Sigismondi

1992 ◽  
Vol 43 (5) ◽  
pp. 443-457 ◽  
Author(s):  
G. L. Nemhauser ◽  
G. Sigismondi


2016 ◽  
Vol 5 (4) ◽  
pp. 148
Author(s):  
GEDE SURYAWAN ◽  
NI KETUT TARI TASTRAWATI ◽  
KARTIKA SARI

Companies which engaged in production activities such as Ramadhan Bakery would want optimal profit in their every production. The aim of this study was to find optimal profit and optimal combination of bread production (original chocolate bread, extra chocolate bread, rounding chocolate bread and mattress chocolate bread) that was produced by Ramadhan Bakery by applying Branch and Bound Algorithm method. Branch and Bound Algorithm is one method to solve Integer Programming’s problems other than Cutting Plane method. Compared with Cutting Plane method, Branch and Bound Algorithm method is more effective in determining the optimal value. As the result of this study showed that to get optimal profit, Ramadhan Bakery should produce 360 pcs of original chocolate bread, 300 pcs of extra chocolate bread, 306 pcs of rounding chocolate bread and 129 pcs of mattress chocolate bread with optimal profit amounts Rp. 1.195.624,00.. The profit will increase amounts 25,2 % than before.



Author(s):  
Bishaljit Paul ◽  
Sushovan Goswami ◽  
Dipu Mistry ◽  
Chandan Kumar Chanda


Author(s):  
Jan-Lucas Gade ◽  
Carl-Johan Thore ◽  
Jonas Stålhand

AbstractIn this study, we consider identification of parameters in a non-linear continuum-mechanical model of arteries by fitting the models response to clinical data. The fitting of the model is formulated as a constrained non-linear, non-convex least-squares minimization problem. The model parameters are directly related to the underlying physiology of arteries, and correctly identified they can be of great clinical value. The non-convexity of the minimization problem implies that incorrect parameter values, corresponding to local minima or stationary points may be found, however. Therefore, we investigate the feasibility of using a branch-and-bound algorithm to identify the parameters to global optimality. The algorithm is tested on three clinical data sets, in each case using four increasingly larger regions around a candidate global solution in the parameter space. In all cases, the candidate global solution is found already in the initialization phase when solving the original non-convex minimization problem from multiple starting points, and the remaining time is spent on increasing the lower bound on the optimal value. Although the branch-and-bound algorithm is parallelized, the overall procedure is in general very time-consuming.





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