Military Human Resource Planning through Flow Network Modeling

Abdessameud, Oussama Mazari; Utterbeeck, Filip Van; Guerry, Marie-Anne

doi:10.1080/10429247.2021.1900660

Cited by 7 publications

(5 citation statements)

References 39 publications

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“…Specifically, about the research object dimension, this section focuses on whether the studies involve multi-level organizations; furthermore, concerning the objective function dimension, this section examines the number of objective functions and whether they are set from an organizational or individual development perspective. [11] multi-level organization/HRP in government administration organizational perspective/single-objective: minimize organizational risk cut plane method Abdessameud (2021) [12] multi-level organization/military mobility planning organizational perspective/single-objective: minimize the deviation of the current state of the human resources strategy from the target state of the human resources strategy.…”

Section: Related Workmentioning

confidence: 99%

“…Jaillet et al [11] first proposed a method for assessing the riskiness of an organization's current human resource situation and solved this problem based on the cut-plane method to minimize the organizational risk factor. Abdessameud et al [12] conducted a study on the HRP planning problem in the Belgian army by constructing an army personnel flow network and setting an objective function from an organizational point of view to reach the target state of the organizational structure by controlling the personnel flow. This study modeled this process as a mixed-integer programming problem and solved it based on the CPLEX solver.…”

Section: Related Workmentioning

confidence: 99%

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Research of a Multi-Level Organization Human Resource Network Optimization Model and an Improved Late Acceptance Hill Climbing Algorithm

Huang,

Li,

et al. 2023

Mathematics

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Complex hierarchical structures and diverse personnel mobility pose challenges for many multi-level organizations. The difficulty of reasonable human resource planning in multi-level organizations is mainly caused by ignoring the hierarchical structure. To address the above problems, firstly, a multi-level organization human resource network optimization model is constructed by representing the turnover situation of multi-level organizations in a dimensional manner as a multi-level network. Secondly, we propose an improved late acceptance hill climbing based on tabu and retrieval strategy (TR-LAHC) and designed two intelligent optimization operators. Finally, the TR-LAHC algorithm is compared with other classical algorithms to prove that the algorithm provides the best solution and can effectively solve the personnel mobility planning problem in multi-level organizations.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Research of a Multi-Level Organization Human Resource Network Optimization Model and an Improved Late Acceptance Hill Climbing Algorithm

Huang,

Li,

et al. 2023

Mathematics

View full text Add to dashboard Cite

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“…Artificial intelligence is commonly used in several areas of HRM: HR planning (Mazari Abdessameud et al, 2021), talent management (Black and van Esch, 2021), recruitment Biased intelligent decision support systems processes (Allal-Ch erif et al, 2021), candidates' selection and maintenance (Malik et al, 2022). These systems help companies cut the cost linked to the multilevel recruitment process and prepare the company for turnover by suggesting a potential replacement for a vacancy before it occurs.…”

Section: Conformist Behaviour Towards Disciplinary Decisions Made By ...mentioning

confidence: 99%

Fired by an algorithm? Exploration of conformism with biased intelligent decision support systems in the context of workplace discipline

Bartosiak

Modliński

2022

CDI

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PurposeThe importance of artificial intelligence in human resource management has grown substantially. Previous literature discusses the advantages of AI implementation at a workplace and its various consequences, often hostile, for employees. However, there is little empirical research on the topic. The authors address this gap by studying if individuals oppose biased algorithm recommendations regarding disciplinary actions in an organisation.Design/methodology/approachThe authors conducted an exploratory experiment in which the authors evaluated 76 subjects over a set of 5 scenarios in which a biased algorithm gave strict recommendations regarding disciplinary actions at a workplace.FindingsThe authors’ results suggest that biased suggestions from intelligent agents can influence individuals who make disciplinary decisions.Social implicationsThe authors’ results contribute to the ongoing debate on applying AI solutions to HR problems. The authors demonstrate that biased algorithms may substantially change how employees are treated and show that human conformity towards intelligent decision support systems is broader than expected.Originality/valueThe authors’ paper is among the first to show that people may accept recommendations that provoke moral dilemmas, bring adverse outcomes, or harm employees. The authors introduce the problem of “algorithmic conformism” and discuss its consequences for HRM.

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“…Ali et al [24] established a mathematical model for capacity planning in 5G networks and proposed a discrete fireworks algorithm to solve it. Typical optimization methods such as the tabu search algorithm [25,26], satisfactory decision-making methods [27], exhaustive search, integer programming [28], constraint programming [29] and graph theory method [30] can also be applied for the task planning problem. However, because these are not the focus of this study, they will not be elaborated on here.…”

Section: Introductionmentioning

confidence: 99%

An FW–GA Hybrid Algorithm Combined with Clustering for UAV Forest Fire Reconnaissance Task Assignment

Tian

Hou

2023

Mathematics

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The assignment of tasks for unmanned aerial vehicles (UAVs) during forest fire reconnaissance is a highly complex and large-scale problem. Current task allocation methods struggle to strike a balance between solution speed and effectiveness. In this paper, a two-phase centralized UAV task assignment model based on expectation maximization (EM) clustering and the multidimensional knapsack model (MKP) is proposed for the forest fire reconnaissance task assignment. The fire situation information is acquired using the sensors carried by satellites at first. Then, the EM algorithm based on the Gaussian mixture model (GMM) is applied to get the initial position of every UAV. In the end, the MKP is applied for UAV task assignment based on the initial positions of the UAVs. An improved genetic algorithm (GA) based on the fireworks algorithm (FWA) is proposed for faster iteration speed. A simulation was carried out against the background of forest fires in Liangshan Prefecture, Sichuan Province, and the simulation’s results demonstrate that the task assignment model can quickly and effectively address task allocation problems on a large scale. In addition, the FW–GA hybrid algorithm has great advantages over the traditional GA, particularly in solving time, iteration convergence speed, and solution effectiveness. It can reduce up to 556% of the iteration time and increase objective function value by 1.7% compared to the standard GA. Furthermore, compared to the GA–SA algorithm, its solving time is up to 60 times lower. This paper provides a new idea for future large-scale UAV task assignment problems.

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Military Human Resource Planning through Flow Network Modeling

Abstract: with details of the nature of the infringement. We will investigate the claim and if justified, we will take the appropriate steps.

Cited by 7 publications

References 39 publications

Research of a Multi-Level Organization Human Resource Network Optimization Model and an Improved Late Acceptance Hill Climbing Algorithm

Research of a Multi-Level Organization Human Resource Network Optimization Model and an Improved Late Acceptance Hill Climbing Algorithm

Fired by an algorithm? Exploration of conformism with biased intelligent decision support systems in the context of workplace discipline

An FW–GA Hybrid Algorithm Combined with Clustering for UAV Forest Fire Reconnaissance Task Assignment

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