Mathematical Programming Approaches for Multi-Vehicle Motion Planning: Linear, Nonlinear, and Mixed Integer Programming

2011 ◽  
Author(s):  
Eric Diller
Keyword(s):  
Integer Programming ◽  
Mathematical Programming ◽  
Motion Planning ◽  
Mixed Integer Programming ◽  
Mixed Integer ◽  
Vehicle Motion

scholarly journals A New Decomposition-Based Pricing Technique For Solving Large-Scale Mixed IP with a Computer Technique

2016 ◽  
Vol 34 ◽  
pp. 5-20
Author(s):  
A Islam ◽  
M Babul Hasan ◽  
HK Das
Keyword(s):  
Integer Programming ◽  
Mathematical Programming ◽  
Mixed Integer Programming ◽  
Programming Language ◽  
Large Scale ◽  
Mixed Integer ◽  
Huge Number ◽  
Computer Technique

In this paper, we develop a new Decomposition-Based Pricing (DBP) procedure to filter the unnecessary decision ingredients from large scale mixed integer programming (MIP) problem, where the variables are in huge number will be abated and the complicacy of restrictions will be straightforward. We then develop a generalized computer technique corresponding to our new DBP method by using the programming language A Mathematical Programming Language (AMPL). A number of examples have been illustrated to demonstrate our method.GANIT J. Bangladesh Math. Soc.Vol. 34 (2014) 5-20

Mixed-integer programming in motion planning

2020 ◽  
Author(s):  
Daniel Ioan ◽  
Ionela Prodan ◽  
Sorin Olaru ◽  
Florin Stoican ◽  
Silviu-Iulian Niculescu
Keyword(s):  
Integer Programming ◽  
Motion Planning ◽  
Mixed Integer Programming ◽  
Mixed Integer

A test of knowledge-based forest operations scheduling procedures

1996 ◽  
Vol 26 (7) ◽  
pp. 1193-1202 ◽  
Author(s):  
Cristopher L. Brack ◽  
Peter L. Marshall
Keyword(s):  
Linear Programming ◽  
Integer Programming ◽  
Mathematical Programming ◽  
Expert Systems ◽  
Mixed Integer Programming ◽  
Mixed Integer ◽  
Forest Operations ◽  
Knowledge Based ◽  
Operations Scheduling ◽  
Trade Offs

Five knowledge-based approaches (three search routines and two expert systems) to forest operations scheduling were compared with mathematical programming (linear programming and mixed integer programming) and simulation approaches for two plantation forests in New South Wales, Australia. Strategies produced using these approaches were compared on the basis of scores for timber volume flow, scenic beauty, stand health, and water quality. Timber flow scores were highest for the linear programming strategies, but some of the strategies produced by the knowledge-based approaches scored almost as high. The timber flow scores for the mixed integer programming strategies were exceeded by some of the knowledge-based strategies, because of the approximations required to achieve mixed integer programming solutions for larger problems. The knowledge-based approaches could produce higher scoring strategies for the other criteria than the mathematical programming or simulation approaches. The multiple strategies produced by two of the search procedures, and the goal hierarchy incorporated into the expert systems, allow the user to make explicit trade-offs among strategies in terms of performance for the various criteria.

Non-blocking Supervisory Design Using Mixed Integer Programming

2009 ◽  
Vol 35 (2) ◽  
pp. 180-185
Author(s):  
Mi ZHAO ◽  
Zhi-Wu LI ◽  
Na WEI
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Mixed Integer
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