scholarly journals A scalable parallel genetic algorithm for the Generalized Assignment Problem

2015 ◽  
Vol 46 ◽  
pp. 98-119 ◽  
Author(s):  
Yan Y. Liu ◽  
Shaowen Wang
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Parallel Genetic Algorithm ◽  
Generalized Assignment Problem ◽  
Generalized Assignment

scholarly journals A New Genetic Algorithm with Agent-Based Crossover for Generalized Assignment Problem

2019 ◽  
Vol 48 (3) ◽  
pp. 389-400 ◽  
Author(s):  
Murat DÖRTERLER
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Dominant Gene ◽  
Minimum Cost ◽  
Fertility Rate ◽  
Generalized Assignment Problem ◽  
Agent Based ◽  
Local Searches ◽  
Generalized Assignment ◽  
Feasible Solutions

Generalized assignment problem (GAP) considers finding minimum cost assignment of n tasks to m agents provided each task should be assigned to one agent only. In this study, a new Genetic Algorithm (GA) with some new methods is proposed to solve GAPs. The agent-based crossover is based on the concept of dominant gene in genotype science and increases fertility rate of feasible solutions. The solutions are classified as infeasible, feasible and mature with reference to their conditions. The new local searches provide not only feasibility in high diversity but high profitability for the solutions. A solution is not given up through maturation-based replacement until it reaches its best.  Computational results show that the agent-based crossover has much higher fertility rate compared to classical crossover. Also, the proposed GA creates either optimal or approximately optimal solutions.

The equilibrium generalized assignment problem and genetic algorithm

2012 ◽  
Vol 218 (11) ◽  
pp. 6526-6535 ◽  
Author(s):  
Linzhong Liu ◽  
Haibo Mu ◽  
Yubo Song ◽  
Haiyan Luo ◽  
Xiaojing Li ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Generalized Assignment Problem ◽  
Generalized Assignment

An Optimizing Approach for Multi Constraints Reassignment Problem of Human Resources

2016 ◽  
Vol 6 (4) ◽  
pp. 1907 ◽  
Author(s):  
Tkatek Said ◽  
Abdoun Otman ◽  
Abouchabaka Jaafar ◽  
Rafalia Najat
Keyword(s):  
Genetic Algorithm ◽  
Human Resources ◽  
Assignment Problem ◽  
Complex Problem ◽  
Second Step ◽  
Hard Problem ◽  
Optimal Assignment ◽  
Generalized Assignment Problem ◽  
Generalized Assignment ◽  
Np Hard Problem

<span lang="EN-US">This paper presents an effective approach to optimize the reassignment of Human Resources in the enterprise that is formed by several units of productions to take into consideration the human characteristics. This approach consists of two steps; the first step is to formalize the studied problem that is practically take the form of the generalized assignment problem (GAP) known as NP-hard problem. Additionally, the variables in the formulation of our problem are interlinked by certain constraints. These two proprieties can to justify the important complexity of this problem. The second step is focused to solve this complex problem by using the genetic algorithm. We present the experimentally result for justifying the validity of the proposed approach. So, the solution obtained allowed us to get an optimal assignment of personnel that can lead to improve the average productivity or ratability or at least ensure its equilibration within sites of enterprise.</span>

scholarly journals An Optimizing Approach for Multi Constraints Reassignment Problem of Human Resources

2016 ◽  
Vol 6 (4) ◽  
pp. 1907
Author(s):  
Tkatek Said ◽  
Abdoun Otman ◽  
Abouchabaka Jaafar ◽  
Rafalia Najat
Keyword(s):  
Genetic Algorithm ◽  
Human Resources ◽  
Assignment Problem ◽  
Complex Problem ◽  
Second Step ◽  
Hard Problem ◽  
Optimal Assignment ◽  
Generalized Assignment Problem ◽  
Generalized Assignment ◽  
Np Hard Problem

<span lang="EN-US">This paper presents an effective approach to optimize the reassignment of Human Resources in the enterprise that is formed by several units of productions to take into consideration the human characteristics. This approach consists of two steps; the first step is to formalize the studied problem that is practically take the form of the generalized assignment problem (GAP) known as NP-hard problem. Additionally, the variables in the formulation of our problem are interlinked by certain constraints. These two proprieties can to justify the important complexity of this problem. The second step is focused to solve this complex problem by using the genetic algorithm. We present the experimentally result for justifying the validity of the proposed approach. So, the solution obtained allowed us to get an optimal assignment of personnel that can lead to improve the average productivity or ratability or at least ensure its equilibration within sites of enterprise.</span>

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