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

Marta Lilia Eraña-Díaz, Marco Antonio Cruz-Chávez, Fredy Juárez-Pérez, Juana Enriquez-Urbano, Rafael Rivera-López and Mario Acosta-Flores
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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

Mathematics, 2021, vol. 9, issue 10, 1-23

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.

Keywords: optimization method; mapping; MKP benchmark; simulated annealing algorithm; psychosocial risks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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