Efficient genetic algorithms for optimal assignment of tasks to teams of agents

Younas, Irfan; Kamrani, Farzad; Bashir, Maryam; Schubert, Johan

doi:10.1016/j.neucom.2018.07.008

Cited by 19 publications

(13 citation statements)

References 24 publications

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“…Indeed, we are interested to focus on the work in treating certain term constrained assignment problem solving by genetic algorithms. The common point focuses on the use of conventional stop criteria 1917 based on the number of maximum iterations and secondly on the application of crossovers and mutation operators adapted and reconstructed for the problem studied, we cite for example I.Younas [18], J.Park [19] .Xilin [20], and El Moudani [21]. Table 6 shows the results corresponding to number of displacements according to α.…”

Section: Resultsmentioning

confidence: 99%

An Optimizing Approach for Multi Constraints Reassignment Problem of Human Resources

Tkatek

Abdoun

Abouchabaka

et al. 2016

IJECE

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<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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Section: Resultsmentioning

confidence: 99%

An Optimizing Approach for Multi Constraints Reassignment Problem of Human Resources

Tkatek

Abdoun

Abouchabaka

et al. 2016

IJECE

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“…Analysis of bat algorithm. Exploration (diversification) and exploitation (intensification) are two important components of a metaheuristic and there is a need to maintain an appropriate balance between them to find a near global optimum [39,40]. Exploration property of an algorithm helps to explore unknown and new regions of the search space by generating diverse set of solutions focusing on the search at global level [41,42].…”

Section: Selection Of Non-dominant Solutions Needs O (N Logmentioning

confidence: 99%

Many-objective BAT algorithm

2020

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In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. The challenging part in MaOPs is to get the Pareto approximation (PA) with high diversity and good convergence. In Literature, in order to solve the issue of diversity and convergence in MaOPs, many approaches are proposed using different multi objective evolutionary algorithms (MOEAs). Moreover, to get better results, the researchers use the sets of reference points to differentiate the solutions and to model the search process, it further evaluates and selects the non-dominating solutions by using the reference set of solutions. Furthermore, this technique is used in some of the swarm-based evolutionary algorithms. In this paper, we have used some effective adaptations of bat algorithm with the previous mentioned approach to effectively handle the many objective problems. Moreover, we have called this algorithm as many objective bat algorithm (MaOBAT). This algorithm is a biologically inspired algorithm, which uses echolocation power of micro bats. Each bat represents a complete solution, which can be evaluated based on the problem specific fitness function and then based on the dominance relationship, non-dominated solutions are selected. In proposed MaOBAT, dominance rank is used as dominance relationship (dominance rank of a solution means by how many other solutions a solution dominated). In our proposed strategy, dynamically allocated set of reference points are used, allowing the algorithm to have good convergence and high diversity pareto fronts (PF). The experimental results show that the proposed algorithm has significant advantages over several state-of-the-art algorithms in terms of the quality of the solution.

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“…We show that if the size of the problem is large, then standard crossover operators cannot efficiently find near-optimal solutions within a reasonable time. In general, the efficiency of the genetic algorithm depends on the choice of genetic operators (selection, crossover, and mutation) and the associated parameters [23]. Based on the major equipment person-job safe matching model characteristics, we use genetic algorithms to solve the matching model.…”

Section: Safe Matching Decision-makingmentioning

confidence: 99%

Exploring the Optimal Safety Person–job Matching Method of Major Equipment Based on Human Reliability

Zhang

et al. 2019

Applied Sciences

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Under the background of intelligent manufacturing, this paper aims to develop a model for person–job safe matching that optimizes safety with consideration of major equipment operator competency and task complexity. Safe matching cost is minimized in the developed model and is measured by the equipment downtime, production defect rate, and operator labor costs oriented by human factors. Human reliability is calculated with the goal of best value individual competency and best admit task complexity with a hierarchical structure. The 0-1 integer programming person–job matching model minimizes the human factor safety and wage costs and satisfies the requirements of the production order, budget and operator quantity requirement. An improved genetic algorithm is designed to solve the model. The computational results of the proposed model based on a case study for a large iron and steel company evidently demonstrated its effectiveness. A new integrated model provides more realistic matches for person–job assignment.

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Efficient genetic algorithms for optimal assignment of tasks to teams of agents

Cited by 19 publications

References 24 publications

An Optimizing Approach for Multi Constraints Reassignment Problem of Human Resources

An Optimizing Approach for Multi Constraints Reassignment Problem of Human Resources

Many-objective BAT algorithm

Exploring the Optimal Safety Person–job Matching Method of Major Equipment Based on Human Reliability

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