scholarly journals Implicit dual control based on particle filtering and forward dynamic programming

Author(s):  
David S. Bayard ◽  
Alan Schumitzky
2005 ◽  
Vol 25 (3) ◽  
pp. 479-492 ◽  
Author(s):  
Franklina Maria Bragion de Toledo ◽  
André Luís Shiguemoto

In this paper, a case study is carried out concerning the lot-sizing problem involving a single item production planning in several production centers that do not present capacity constraints. Demand can be met with backlogging or not. This problem results from simplifying practical problems, such as the material requirement planning (MRP) system and also lot-sizing problems with multiple items and limited production capacity. First we propose an efficient implementation of a forward dynamic programming algorithm for problems with one single production center. Although this does not reduce its complexity, it has shown to be rather effective, according to computational tests. Next, we studied the problem with a production environment composed of several production centers. For this problem two algorithms are implemented, the first one is an extension of the dynamic programming algorithm for one production center and the second one is an efficient implementation of the first algorithm. Their efficiency are shown by computational testing of the algorithms and proposals for future research are presented.


PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0215449 ◽  
Author(s):  
Mohammad Moradi ◽  
Sama Goliaei ◽  
Mohammad-Hadi Foroughmand-Araabi

2018 ◽  
Vol 51 (31) ◽  
pp. 383-389 ◽  
Author(s):  
Lukas Engbroks ◽  
Daniel Görke ◽  
Stefan Schmiedler ◽  
Jochen Strenkert ◽  
Bernhard Geringer

2009 ◽  
Vol 26 (02) ◽  
pp. 307-317 ◽  
Author(s):  
WEN-HUA YANG

Consider a batch-sizing problem, where all jobs are identical or similar, and a unit processing time (p = 1) is specified for each job. To minimize the total completion time of jobs, partitioning jobs into batches may be necessary. Learning effect from setup repetition makes small-sized batches; on the contrary, job's learning effect results in large-sized batches. With their collaborative influence, we develop a forward dynamic programming (DP) algorithm to determine the optimal number of batches and their optimal integer sizes. The computation effort required by this DP algorithm is a polynomial function of job size.


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