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

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1126
Author(s):  
Marta Lilia Eraña-Díaz ◽  
Marco Antonio Cruz-Chávez ◽  
Fredy Juárez-Pérez ◽  
Juana Enriquez-Urbano ◽  
Rafael Rivera-López ◽  
...  
Keyword(s):  
Risk Factors ◽  
Simulated Annealing ◽  
Knapsack Problem ◽  
Simulated Annealing Algorithm ◽  
Psychosocial Risk Factors ◽  
Psychosocial Risk ◽  
Multidimensional Knapsack Problem ◽  
Optimal Subset ◽  
Multidimensional Knapsack ◽  
A Company

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.

scholarly journals Simulated Annealing Algorithm for the Multiple Choice Multidimensional Knapsack Problem

2021 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Simulated Annealing ◽  
Open Source ◽  
Knapsack Problem ◽  
Simulated Annealing Algorithm ◽  
Multiple Choice ◽  
Optimal Solutions ◽  
Multidimensional Knapsack Problem ◽  
Multidimensional Knapsack ◽  
Benchmark Instances ◽  
Annealing Algorithm

The multiple choice multidimensional knapsack problem (MCMK) is a harder version of the 0/1 knapsack problem, and is ever more complex than the 0/1 multidimensional knapsack problem. In MCMK, there are several groups of items. The objective is to maximize the value (profit) by choosing exactly 1 item from each group such that all the constraints are satisfied. It is difficult and NP-hard even to find a solution that does not violate all constraints. In this work, we present a simulated annealing based algorithm with open source C++ code to find good solutions to the multidimensional multiple choice knapsack problem. In all of the benchmark instances we used, the algorithm is able to find optimum (or close) solutions, thereby proving that the algorithm is suitable for solving larger instances for which optimal solutions are unknown.

Suicide Attempts Among Depressed Inpatients with Depressive Disorder in a Malaysian Sample

Crisis ◽  
2011 ◽  
Vol 32 (5) ◽  
pp. 283-287 ◽  
Author(s):  
L. F. Chan ◽  
T. Maniam ◽  
A. S. Shamsul
Keyword(s):  
Risk Factors ◽  
Suicidal Ideation ◽  
Suicide Attempt ◽  
Depressive Disorder ◽  
Suicide Attempts ◽  
Psychosocial Risk Factors ◽  
Psychosocial Risk ◽  
Life Event ◽  
Axis I ◽  
A Current

Background: Depressed inpatients constitute a high-risk population for suicide attempts. Aims: To describe the interactions of clinical and psychosocial risk factors influencing suicide attempts among a Malaysian sample of depressed inpatients. Methods: Seventy-five subjects were diagnosed with a depressive disorder according to the Structured Clinical Interview for DSM-IV Axis I Disorders-Clinical Version (SCID-CV). Data on suicide attempts, suicidal ideation (Scale for Suicidal Ideation, SSI), depression severity (Beck’s Depression Inventory, BDI), recent life-event changes (Social Readjustment Rating Scale, SRRS), sociodemographic and other relevant clinical factors were collected. Results: A third of the subjects presented after a current suicide attempt. Significant factors for a current suicide attempt were race, religion, recent life-event changes, suicidal ideation, and alcohol use disorder. Independent predictive risk factors for a current suicide attempt were Chinese race, recent marital separation, major mortgage or loans, and being newly diagnosed with depression. Any recent change in personal habits was shown to be a protective factor against current suicide attempt. Age and gender were nonsignificant factors. Conclusions: The findings are generally consistent with existing studies and highlight the role of psychosocial risk factors.

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