scholarly journals Ant Colony Optimization and Genetic Algorithm for Fuzzy Stochastic Production-Distribution Planning

2018 ◽  
Vol 8 (11) ◽  
pp. 2042 ◽  
Author(s):  
Umit Sami SAKALLI ◽  
Irfan ATABAS
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Ant Colony Optimization ◽  
Heuristic Algorithms ◽  
Ant Colony ◽  
Route Optimization ◽  
Test Problems ◽  
Np Hard ◽  
Distribution Planning ◽  
Production Distribution

In this paper, a tactical Production-Distribution Planning (PDP) has been handled in a fuzzy and stochastic environment for supply chain systems (SCS) which has four echelons (suppliers, plants, warehouses, retailers) with multi-products, multi-transport paths, and multi-time periods. The mathematical model of fuzzy stochastic PDP is a NP-hard problem for large SCS because of the binary variables which determine the transportation paths between echelons of the SCS and cannot be solved by optimization packages. In this study, therefore, two new meta-heuristic algorithms have been developed for solving fuzzy stochastic PDP: Ant Colony Optimization (ACO) and Genetic Algorithm (GA). The proposed meta-heuristic algorithms are designed for route optimization in PDP and integrated with the GAMS optimization package in order to solve the remaining mathematical model which determines the other decisions in SCS, such as procurement decisions, production decisions, etc. The solution procedure in the literature has been extended by aggregating proposed meta-heuristic algorithms. The ACO and GA algorithms have been performed for test problems which are randomly generated. The results of the test problem showed that the both ACO and GA are capable to solve the NP-hard PDP for a big size SCS. However, GA produce better solutions than the ACO.

Ant Colony Optimization for Factory Layout

2012 ◽  
Vol 591-593 ◽  
pp. 758-761
Author(s):  
Xiu Zeng ◽  
Qian Li Ma
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Heuristic Algorithms ◽  
Mean Value ◽  
Ant Colony ◽  
Logistics Cost ◽  
The Mean ◽  
Factory Layout ◽  
Np Problem

Factory layout is NP problem[1]. There are many methods to solve it ,such as engineering diagram, flow chart method, various heuristic algorithms, SA( simulated annealing) and GA(genetic algorithm) [2].ACO (ant colony optimization) is used to solve it in this paper. The logistics costs exist between two workshops that are treated as pheromone that guides ants to search the best solution. Smaller logistics cost is, stronger the two workshops of relation is. In the process of optimization theworkshop with low logistics cost is more likely to be chosen, which minimizes the system logistics cost. Compared with GA, ACO has the advantage in speed. The mean value of the solution, the best solution, the worst solution is better too. More the number of workshop is, more obvious the superiority is.

scholarly journals Wireless Sensor Network Route Optimization Based on Improved Ant Colony-Genetic

2015 ◽  
Vol 11 (9) ◽  
pp. 4 ◽  
Author(s):  
Wei Liu ◽  
Yongfeng Cui ◽  
Zhongyuan Zhao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Optimization Problem ◽  
Ant Colony ◽  
Route Optimization ◽  
Wireless Sensor ◽  
Multi Objective Optimization ◽  
Mutation Operation

The objective of this paper is focuses on route optimization, for a given wireless sensor network. We detail the significance of route optimization problem and the corresponding mathematical model. After analyzing the complex multi-objective optimization problem, Ant Colony Optimization (ACO) algorithm was introduced to search the best route. Inspired by Genetic Algorithm (GA), we embed two operations into ACO to refine it. First, every ant after achieving sink will be regarded as an individual such as that in GA. The crossover operation will be applied and then, the generated new ants will replace the weaker parents. Second, we designed a mutation operation for ants selecting next nodes to visit. Experimental results demonstrate that the proposed combination algorithm has significant enhancements than both GA and ACO. The lifetime of WSN can be extended and the coverage speed can be accelerated.

scholarly journals Ant Colony Optimization with Three Stages for Independent Test Cost Attribute Reduction

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zilong Xu ◽  
Hong Zhao ◽  
Fan Min ◽  
William Zhu
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Heuristic Algorithms ◽  
Information Gain ◽  
Attribute Reduction ◽  
Ant Colony ◽  
Test Cost ◽  
Cost Attribute ◽  
Three Stages ◽  
Filtration Stage

Minimal test cost attribute reduction is an important problem in cost-sensitive learning. Recently, heuristic algorithms including the information gain-based algorithm and the genetic algorithm have been designed for this problem. However, in many cases these algorithms cannot find the optimal solution. In this paper, we develop an ant colony optimization algorithm to tackle this problem. The attribute set is represented as a graph with each vertex corresponding to an attribute and weight of each edge to pheromone. Our algorithm contains three stages, namely, the addition stage, the deletion stage, and the filtration stage. In the addition stage, each ant starts from the initial position and traverses edges probabilistically until the stopping criterion is satisfied. The pheromone of the traveled path is also updated in this process. In the deletion stage, each ant deletes redundant attributes. Two strategies, called the centralized deletion strategy and the distributed deletion strategy, are proposed. Finally, the ant with minimal test cost is selected to construct the reduct in the filtration stage. Experimental results on UCI datasets indicate that the algorithm is significantly better than the information gain-based one. It also outperforms the genetic algorithm on medium-sized dataset Mushroom.

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