A neural system for faithful color reproduction in industrial printing processes

Printing is a widespread industrial process. Manufacturers of colored products are expected to maintain high levels of color quality to perfectly satisfy the customers’ requirements. The rendering of colors is visually checked by experienced workers, who may though show different color sensitiveness, depending, e.g., on perceptual, cognitive and cultural aspects. This often results in products that fail to faithfully reproduce what the customer asked for, with negative consequences for companies, as well as huge financial losses. This paper describes a neural network-based system to objectively check how faithfully colors are reproduced by an industrial printing process. The system considers a master color, then compares it to a copy, and returns an objective degree of color fidelity of the copy to the master. The neural system was trained and tested in a real-world case study by using a huge quantity of color pairs taken from the L*a*b* color space. Highly accurate results were achieved. The strengths of the system are that it can measure the difference of colors in a way that is incredibly close to that perceived by the human eye, and the fact that it can do that canceling the color distortion phenomena that may occur in the human vision

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

Profiling risk sensibility through association rules

In the last recent years several approaches to risk assessment and risk management have been adopted to reduce the potential for specific risks in working environments. A safety culture has also developed to let workers acquire knowledge and understanding of risks and safety. Notwithstanding, risks still exist in every workplace. One effective way to improve workers' sensibility to risk, i.e., their ability to effectively assess and control the risks they are exposed to, is risk management training. Unfortunately, people may perceive risks in different ways depending on subjective assessment of the characteristics and severity of the considered risks, and may have tendencies to either take or avoid actions that they feel are risky. Therefore, the knowledge of how workers assess each of the risks they may be exposed to in the workplace is a key factor to conceive effective custom risk management training. In this paper we present a novel approach, based on association rules, to workers' profiling with respect to risk perception and risk propensity in order to provide each of them with specific customized risk management training

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 worker's 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

A Linear Programming-Driven MCDM Approach for Multi-Objective Economic Dispatch in Smart Grids

This paper presents a novel approach to deal with the multi-objective economic dispatch problem in smart grids as a multi-criteria decision making (MCDM) problem, whose decision alternatives are dynamically generated. Four objectives are considered: emissions, energy cost, distance of supply, and load balancing. Objectives are preliminarily preference-ranked through a fuzzy version of the analytic hierarchy process (AHP), and then classified into two categories of importance. The more important objectives form the objective function of a linear programming (LP) problem, whose solution (driving solution) drives the generation of Pareto-optimal alternative configurations of power output of the generators. The technique for order of preference by similarity to ideal solution (TOPSIS) is used to automatically select the most suitable power output configuration, according to initial preferences, derived with fuzzy AHP. The effectiveness of our approach is validated by comparing it to the weighted sum (WS) method, by simulating 40 different operating scenarios on a prototype smart microgrid

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 candidate’s 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

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 worker’s caution for every task on the basis of some human factors and the worker’s 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

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 the integration of fuzzy AHP and 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

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 eectiveness of the proposed framework in handling scenarios with Pareto fronts having up to four discontinuities