Multiobjective personnel assignment exploiting workers' sensitivity to risk

Every year 2.3 million people die worldwide due to occupational illnesses and accidents at work. By analyzing the workers' behavior when in the presence of risks managers could assign tasks to those workers who appear to be the most sensitive to risk being assigned and are thus more likely to exert more caution in the presence of that risk. This paper presents a novel multiobjective formulation of the personnel assignment problem, maximizing workers' sensitivity to risk, while minimizing cost and dislike for the task assigned. A worker's sensitivity to risk for a task is quantified by a new measure, carefulness, which stems from the worker's behavior and various human factors that affect the interaction with the risk. The problem is solved using a mixed evolutionary and multicriteria decision making methodology. An approximation of the Pareto front is first generated through the nondominated sorting genetic algorithm II. A hybrid decisional approach then exploits the technique for order of preference by similarity to ideal solution in order to select the Pareto-optimal solution that represents the nearest compromise to the decision-maker's preferences. These preferences are derived through a fuzzy version of the analytic hierarchy process. The proposed framework was tested in four real-world scenarios related to manufacturing companies. The results show a significant increase in overall carefulness and a strong decrease in the dislike for the task assigned, with a modest increase in cost. The framework thus improves the work climate and reduces the risk occurrence and/or the impact on the workers' health

Artificial bee colony optimization to reallocate personnel to tasks improving workplace safety

Worldwide, just under 5,800 people go to work every day and do not return because they die on the job. The groundbreaking Industry 4.0 paradigm includes innovative approaches to improve the safety in the workplace, but Small and Medium Enterprises (SMEs)—which represent 99% of the companies in the EU—are often unprepared to the high costs for safety. A cost-effective way to improve the level of safety in SMEs may be to just reassign employees to tasks, and assign hazardous tasks to the more cautious employees. This paper presents a multi-objective approach to reallocate the personnel of a company to the tasks in order to maximize the workplace safety, while minimizing the cost, and the time to learn the new tasks assigned. Pareto-optimal reallocations are first generated using the Non-dominated Sorting artificial Bee Colony (NSBC) algorithm, and the best one is then selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The approach was tested in two SMEs with 11 and 25 employees, respectively

Multiobjective Personnel Assignment Exploiting Workers’ Sensitivity to Risk

Every year 2.3 million people die worldwide due to occupational illnesses and accidents at work. By analyzing the workers' behavior when in the presence of risks managers could assign tasks to those workers who appear to be the most sensitive to risk being assigned and are thus more likely to exert more caution in the presence of that risk. This paper presents a novel multiobjective formulation of the personnel assignment problem, maximizing workers' sensitivity to risk, while minimizing cost and dislike for the task assigned. A worker's sensitivity to risk for a task is quantified by a new measure, carefulness, which stems from the worker's behavior and various human factors that affect the interaction with the risk. The problem is solved using a mixed evolutionary and multicriteria decision making methodology. An approximation of the Pareto front is first generated through the nondominated sorting genetic algorithm II. A hybrid decisional approach then exploits the technique for order of preference by similarity to ideal solution in order to select the Pareto-optimal solution that represents the nearest compromise to the decision-maker's preferences. These preferences are derived through a fuzzy version of the analytic hierarchy process. The proposed framework was tested in four real-world scenarios related to manufacturing companies. The results show a significant increase in overall carefulness and a strong decrease in the dislike for the task assigned, with a modest increase in cost. The framework thus improves the work climate and reduces the risk occurrence and/or the impact on the workers' health