Dynamic programming algorithms for multi-stage safety stock optimization

OR Spectrum ◽  
1997 ◽  
Vol 19 (4) ◽  
pp. 261-271 ◽  
Author(s):  
Stefan Minner
Keyword(s):  
Dynamic Programming ◽  
Safety Stock ◽  
Multi Stage ◽  
Programming Algorithms

EFFECTIVE DYNAMIC PROGRAMMING ALGORITHMS

2020 ◽  
pp. 3-11
Author(s):  
D. A. Karpov ◽  
V. I. Struchenkov
Keyword(s):  
Dynamic Programming ◽  
Optimal Solution ◽  
Optimization Method ◽  
Point Of View ◽  
Theoretical Point ◽  
Regular Grid ◽  
Multi Stage ◽  
Effective Dynamic ◽  
Programming Algorithms ◽  
Two Parameter

This article is devoted to the analysis of the possibilities of increasing the speed of dynamic programming algorithms in solving applied problems of large dimension. Dynamic programming is considered rather than as an optimization method, but as a methodology that allows developing, from a single theoretical point of view, algorithms for solving problems that can be formalized in the form of multi-stage (multi-step) processes in which similar tasks are solved at all steps. It is shown that traditional dynamic programming algorithms based on preliminary setting of a regular grid of states are ineffective, especially if the parameters defining the states are not integer. The problems are considered, in the solution of which it is advisable to build a set of states in the process of counting, moving from one stage to another. Additional conditions are formulated that must be satisfied by the problem so that deliberately hopeless states do not fall into sets of states at each step. This ensures the rejection of not only the paths leading to each of the states, as in traditional dynamic programming algorithms, but also the unpromising states themselves, which greatly increases the efficiency of dynamic programming. Examples of applied problems are given, for the solution of which traditional dynamic programming algorithms were previously proposed, but which can be more efficiently solved by the proposed algorithm with state rejection. As applied to two-parameter problems, the concrete examples demonstrate the effectiveness of the algorithm with rejecting states in comparison with traditional algorithms, especially with increasing the dimension of the problem. An applied problem is considered, in the solution of which dynamic programming is used to construct recurrent formulas for calculating the optimal solution without enumerating options at all.

Efficient Execution of Dynamic Programming Algorithms on Apache Spark

2020 ◽  
Author(s):  
Mohammad Mahdi Javanmard ◽  
Zafar Ahmad ◽  
Jaroslaw Zola ◽  
Louis-Noel Pouchet ◽  
Rezaul Chowdhury ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Apache Spark ◽  
Programming Algorithms ◽  
Efficient Execution

scholarly journals A dynamic alignment algorithm for imperfect speech and transcript

2010 ◽  
Vol 7 (1) ◽  
pp. 75-84 ◽  
Author(s):  
Ye Tao ◽  
Li Xueqing ◽  
Wu Bian
Keyword(s):  
Dynamic Programming ◽  
Boundary Detection ◽  
Multimedia Content ◽  
Alignment Algorithm ◽  
Optimal Alignment ◽  
Multi Stage ◽  
Sentence Level ◽  
Sentence Boundary ◽  
English Training ◽  
Dynamic Alignment

This paper presents a novel alignment approach for imperfect speech and the corresponding transcription. The algorithm gets started with multi-stage sentence boundary detection in audio, followed by a dynamic programming based search, to find the optimal alignment and detect the mismatches at sentence level. Experiments show promising performance, compared to the traditional forced alignment approach. The proposed algorithm has already been applied in preparing multimedia content for an online English training platform.

Time-Space Tradeoffs for Dynamic Programming Algorithms in Trees and Bounded Treewidth Graphs

2015 ◽  
pp. 349-360 ◽  
Author(s):  
Niranka Banerjee ◽  
Sankardeep Chakraborty ◽  
Venkatesh Raman ◽  
Sasanka Roy ◽  
Saket Saurabh
Keyword(s):  
Dynamic Programming ◽  
Bounded Treewidth ◽  
Time Space ◽  
Programming Algorithms
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