Francesco Pistolesi

Classifying Workers into Risk Sensibility Profiles: A Neural Network Approach

In this paper we propose a neural network-based classifier to associate a worker with his/her risk sensibility profile. The basic idea behind the risk sensibility profile is that risks are preventable by appropriate actions that decrease their injurious potential. Also, some criticality factors have been shown to be connected with risk perception and risk propensity. Mapping workers into risk sensibility profiles means to measure how safely workers interact with the risks they are exposed to, by considering the preventing actions they perform, and their criticality factors. The main advantages of the proposed classification consist in: (i) supporting the selection of the most suitable worker to safely perform a given task, (ii) tailoring the safety training to each workers need, to effectively decrease the probability of injury. The proposed neural classifier was trained by using interviews we collected within some volunteer shoe factories. Workers were asked to indicate the preventive actions they would perform if exposed to one or more risks, among a set of proposed actions. Also, workers answered questions to associate a value with each criticality factor. Two typical tasks of the footwear industry, characterized by one and two risks, respectively, were considered to validate and test the classifier.

Efficient energy dispatching in smart microgrids using an integration of fuzzy AHP and TOPSIS assisted by linear programming

Energy dispatching in smart (micro)grids must take into account more conflicting objectives (or criteria), such as power reliability and quality, proper handling of the electricity demand, and cost decrease. The choice of the best alternative in energy dispatching decisions can be dealt with as a multi-criteria optimization and decision making problem. To this aim, we propose the use of linear programming to generate the possible alternatives, and an integration of fuzzy analytic hierarchy process (AHP) and the tecnique for order of preference by similarity to ideal solution (TOPSIS) to select the best alternative. In particular, fuzzy AHP and TOPSIS are used, respectively, to prioritize the criteria and to evaluate the alternatives with respect to four conflicting criteria, namely, environmental impact, cost of the energy, distance of supply, and load level of power lines.

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.

EMOGA: A Hybrid Genetic Algorithm With Extremal Optimization Core for Multiobjective Disassembly Line Balancing

In a world where products get obsolescent ever more quickly, discarded devices produce million tons of electronic waste. Improving how end-of-life products are dismantled helps reduce this waste, as resources are conserved and fed back into the supply chain, thereby promoting reuse and recycling. This paper presents the Extremal MultiObjective Genetic Algorithm (EMOGA), a hybrid nature-inspired optimization technique for a multiobjective version of the disassembly line balancing problem. The aim is to minimize the number of workstations, and to maximize profit and disassembly depth, when dismounting products in disassembly lines. EMOGA is a Pareto-based genetic algorithm hybridized with a module based on extremal optimization, which uses a tailored mutation operator and a continuous relaxation-based seeding technique. The experiments involved the disassembly of a hammer drill and a microwave oven. Performance evaluation was carried out by comparing EMOGA to various efficient algorithms. The results showed that EMOGA is faster or gets closer to the Pareto front, or both, in all comparisons.

An Integrated Optimization System for Safe Job Assignment Based on Human Factors and Behavior

Industrial safety has been deeply improved in the past years, thanks to increasingly sophisticated technologies. Nevertheless, 2.3 million people yearly die worldwide due to occupational illnesses and accidents at work. Human factors can be profitably used for safety improvement because of their influence on the workers’ behavior. This paper presents an integrated optimization system to help companies assign each task to the most suitable worker, minimizing cost, while maximizing expertise and safety. The system is made of three modules. A neural module computes each workers caution for every task on the basis of some human factors and the workers behavior. To solve the multiobjective job assignment problem, an evolutionary module approximates the Pareto front through the nondominated sorting genetic algorithm II. Pareto-optimal solutions then form the alternatives of a multicriteria decision-making problem, and the best is selected by a decision module jointly based on the analytic hierarchy process and the technique for order of preference by similarity to ideal solution. Validation was carried out involving two footwear companies, where personnel was recruited and reassigned to tasks, respectively. Comparing the worker-task assignment proposed by the system to the one suggested/used by the management, noteworthy low-cost improvement in safety is shown in both scenarios, with low or no decrease in expertise. The proposed system can, thus, contribute to get safer workplaces where risks are less likely and/or less harmful.

Solving the environmental economic dispatch problem with prohibited operating zones in microgrids using NSGA-II and TOPSIS

This paper presents a multi-objective optimization framework for the environmental economic dispatch problem in microgrids. Besides classic constraints, also prohibited operating zones and ramp-rate limits of the generators are here considered. Pareto-optimal solutions are generated through the NSGA-II algorithm with customized constraint handling. The optimal solution is selected with TOPSIS. Simulations carried out on a prototype microgrid showed the effectiveness of the proposed framework in handling scenarios with Pareto fronts having up to four discontinuities.

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.

Human factors-based many-objective personnel recruitment for safety-critical work environments

In spite of many improvements in industrial safety of the last decades, nowadays four people per minute die in the world for occupational illnesses and accidents at work. Besides equipping machines with the most advanced technologies, industrial safety has become more and more interested in human factors in recent years, since many accidents at work are proven to be blamed on dangerous behaviours of workers. Recruiting workers with proper risk perception and caution can increase how safely they will deal with the task assigned, thus reducing devastating events. This paper presents a many-objective optimization framework for personnel recruitment in safety-critical work environments. Four objectives are considered: cost and learning time (which are minimized), and risk perception and caution (which are maximized). A neural network-based module computes each candidates risk perception and caution for every single task he/she applies for. Pareto optimal solutions are generated using the Multi-Objective Particle Swarm Optimizer based on hypervolume (MOPSOhv). The best personnel recruitment is selected by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The effectiveness of the proposed framework was validated on two real-world recruitment processes involving 100 and 300 candidates, respectively.