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Optimization Method to Address Psychosocial Risks through Adaptation of the Multidimensional Knapsack Problem

Author

Listed:
  • Marta Lilia Eraña-Díaz

    (Research Center in Engineering and Applied Sciences, Autonomous University of Morelos State (UAEM), Morelos, Cuernavaca 62209, Mexico)

  • Marco Antonio Cruz-Chávez

    (Research Center in Engineering and Applied Sciences, Autonomous University of Morelos State (UAEM), Morelos, Cuernavaca 62209, Mexico)

  • Fredy Juárez-Pérez

    (México National Technological/Alamo Temapache Technological Institute, Veracruz 92730, Mexico)

  • Juana Enriquez-Urbano

    (Research Center in Engineering and Applied Sciences, Autonomous University of Morelos State (UAEM), Morelos, Cuernavaca 62209, Mexico)

  • Rafael Rivera-López

    (Computation and Systems Department, National Technological Institute of Mexico/Veracruz Technological Institute, Veracruz 91860, Mexico)

  • Mario Acosta-Flores

    (Research Center in Engineering and Applied Sciences, Autonomous University of Morelos State (UAEM), Morelos, Cuernavaca 62209, Mexico)

Abstract

This paper presents a methodological scheme to obtain the maximum benefit in occupational health by attending to psychosocial risk factors in a company. This scheme is based on selecting an optimal subset of psychosocial risk factors, considering the departments’ budget in a company as problem constraints. This methodology can be summarized in three steps: First, psychosocial risk factors in the company are identified and weighted, applying several instruments recommended by business regulations. Next, a mathematical model is built using the identified psychosocial risk factors information and the company budget for risk factors attention. This model represents the psychosocial risk optimization problem as a Multidimensional Knapsack Problem (MKP). Finally, since Multidimensional Knapsack Problem is NP-hard, one simulated annealing algorithm is applied to find a near-optimal subset of factors maximizing the psychosocial risk care level. This subset is according to the budgets assigned for each of the company’s departments. The proposed methodology is detailed using a case of study, and thirty instances of the Multidimensional Knapsack Problem are tested, and the results are interpreted under psychosocial risk problems to evaluate the simulated annealing algorithm’s performance (efficiency and efficacy) in solving these optimization problems. This evaluation shows that the proposed methodology can be used for the attention of psychosocial risk factors in real companies’ cases.

Suggested Citation

  • Marta Lilia Eraña-Díaz & Marco Antonio Cruz-Chávez & Fredy Juárez-Pérez & Juana Enriquez-Urbano & Rafael Rivera-López & Mario Acosta-Flores, 2021. "Optimization Method to Address Psychosocial Risks through Adaptation of the Multidimensional Knapsack Problem," Mathematics, MDPI, vol. 9(10), pages 1-23, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:10:p:1126-:d:555605
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    References listed on IDEAS

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    1. Eva Gorgenyi-Hegyes & Robert Jeyakumar Nathan & Maria Fekete-Farkas, 2021. "Workplace Health Promotion, Employee Wellbeing and Loyalty during Covid-19 Pandemic—Large Scale Empirical Evidence from Hungary," Economies, MDPI, vol. 9(2), pages 1-22, April.
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