Solving Multiobjective Bilevel Transportation-Distribution Planning Problem by Modified NSGA II

2013 ◽  
Author(s):  
Liping Jia ◽  
Xinlei Feng ◽  
Guocheng Zou
Keyword(s):  
Planning Problem ◽  
Nsga Ii ◽  
Distribution Planning ◽  
Bilevel Transportation

A penalty-based scaling algorithm for the multi-period multi-product distribution planning problem

2016 ◽  
Vol 49 (4) ◽  
pp. 583-596 ◽  
Author(s):  
S. Ahmad Hosseini ◽  
Güvenç Şahin ◽  
Tonguç Ünlüyurt
Keyword(s):  
Product Distribution ◽  
Planning Problem ◽  
Distribution Planning

scholarly journals Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm

2018 ◽  
Vol 8 (11) ◽  
pp. 2253 ◽  
Author(s):  
Yang Xue
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Path Planning ◽  
Optimization Problems ◽  
Planning Problem ◽  
Nsga Ii ◽  
Multi Objective ◽  
Mutation Operators ◽  
Comparison Results ◽  
Path Planning Problem

In many areas, such as mobile robots, video games and driverless vehicles, path planning has always attracted researchers’ attention. In the field of mobile robotics, the path planning problem is to plan one or more viable paths to the target location from the starting position within a given obstacle space. Evolutionary algorithms can effectively solve this problem. The non-dominated sorting genetic algorithm (NSGA-II) is currently recognized as one of the evolutionary algorithms with robust optimization capabilities and has solved various optimization problems. In this paper, NSGA-II is adopted to solve multi-objective path planning problems. Three objectives are introduced. Besides the usual selection, crossover and mutation operators, some practical operators are applied. Moreover, the parameters involved in the algorithm are studied. Additionally, another evolutionary algorithm and quality metrics are employed for examination. Comparison results demonstrate that non-dominated solutions obtained by the algorithm have good characteristics. Subsequently, the path corresponding to the knee point of non-dominated solutions is shown. The path is shorter, safer and smoother. This path can be adopted in the later decision-making process. Finally, the above research shows that the revised algorithm can effectively solve the multi-objective path planning problem in static environments.

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