Optimization Approach to Multi-Objective Person-Job Fit Job Assignment Problem

Asawarungsaengkul, Krisada; Laoraksakiat, Wanida

doi:10.1109/ri2c48728.2019.8999938

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Recently, E. Jain, K. Dahiya and V. Verma worked on a priority based unbalanced time minimization assignment problem in [23] and they solved it using an iterative algorithm. K. Asawarungsaengkul and W. Laoraksakiat in [24] solved the same problem with five objectives and proposed goal programming and fuzzy multi-objective optimization for the solution.…”

Section: Literature Surveymentioning

confidence: 99%

Person-Job Allocation Optimization using Genetic Algorithm with Mutated-Crossover (GAMC)

Naz¹,

Tanweer²,

Dilshad³

et al. 2022

JISR-C

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The Job Allocation/Assignment Problem has been a pivot of research for numerous well-known researchers around the world. However, as this problem is considered to be NP-complete, it is still not possible to find a deterministic number of steps that solve the said problem within a polynomial time. In this paradigm, the research community is focused to develop and design algorithms that produce results with minimum computational steps. As a result, without any doubt, there is a great opportunity to examine the effectiveness of previously developed algorithms and compare them with solutions developed by other research scientists. In this paper, we have proposed a new variant of the standard Genetic Algorithm (GA) referred to as the Genetic Algorithm with Mutated-Crossover (GAMC) to solve this problem, implement it and analyze the results. The key point in this approach is to present the idea of Mutated-Crossover to avoid infeasible children generated by the crossover used in standard GA that has provided optimal results with lesser computational steps.

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Section: Literature Surveymentioning

confidence: 99%