The Sustainable Role of Human Factor in I4.0 scenarios

Abstract The ageing of working population is the byproduct of the global recognized trend of the general population ageing. The decline of elder human operators' capabilities is a main subject concerning industrial engineering and management in the ongoing 4th Industrial revolution and the introduced new technologies. In this paper, the concept of human factor sustainability inside manufacturing line is explored. It is discussing the theoretical fundamentals of a complexity based states loop to be tested inside 4.0 frame. This is pointing on advanced ICT technologies for ageing workforce management in manufacturing lines. The paper starts with a systematic literature review on the ageing workforce inside industries highlights the human capabilities deterioration, knowledge and experience management of ageing workers. The review is used as the key trace of the modified human factor sustainability concept including Physical, Behavioural, Mental and Psychosocial dimensions. Those are related with the age factor while discussing about traits and entropy based information probability. Furthermore, the proposed formula of Human Factor (HF) probability with a context based application is discussed. Finally, some conclusion remarks will be given, and the future agenda will be proposed based on the collaborative work scenarios

A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector

The increasing availability of data, gathered by sensors and intelligent machines, is chang-ing the way decisions are made in the manufacturing sector. In particular, based on predictive approach and facilitated by the nowadays growing capabilities of hardware, cloud-based solutions, and new learning approaches, maintenance can be scheduled—over cell engagement and resource monitoring—when required, for minimizing (or managing) unexpected equipment failures, improving uptime through less aggressive maintenance schedules, shortening unplanned downtime, reducing excess (direct and indirect) cost, reducing long-term damage to machines and processes, and improve safety plans. With access to increased levels of data (and over learning mechanisms), companies have the capability to conduct statistical tests using machine learning algorithms, in order to uncover root causes of problems previously unknown. This study analyses the maturity level and contributions of machine learning methods for predictive maintenance. An upward trend in publications for predictive maintenance using machine learning techniques was identified with the USA and China leading. A mapping study—steady set until early 2019 data—was employed as a formal and well-structured method to synthesize material and to report on pervasive areas of research. Type of equipment, sensors, and data are mapped to properly assist new researchers in positioning new research activities in the domain of smart maintenance. Hence, in this paper, we focus on data-driven methods for predictive maintenance (PdM) with a comprehensive survey on applications and methods until, for the sake of commenting on stable proposal, 2019 (early included). An equal repartition between evaluation and validation studies was identified, this being a symptom of an immature but growing research area. In addition, the type of contribution is mainly in the form of models and methodologies. Vibrational signal was marked as the most used data set for diagnosis in manufacturing machinery monitoring; furthermore, supervised learning is reported as the most used predictive approach (ensemble learning is growing fast). Neural networks, followed by random forests and support vector machines, were identified as the most applied methods encompassing 40% of publications, of which 67% related to deep neural network with long short-term memory predominance. Notwithstanding, there is no robust approach (no one reported optimal performance over different case tests) that works best for every problem. We finally conclude the research in this area is moving fast to gather a separate focused analysis over the last two years (whenever stable implementations will appear)

the role of human fatigue in the uncertainty of measurement

Abstract Risk of human error in measurement and testing is the result of the causal combination of factors and events that are involved in the process. This paper presents how to model technical and human errors and how these could interact in order to influences the reliability of measurement/test. Human errors were designed according with a System Dynamics approach with factors and states those are part of human's state and ability to handle with the process and procedures and instruments. Technical errors were related to the environment, its organization and suitability with standards. Human and Technical factors have been therefore integrated in order to predict states affecting the consistency of measure and uncertainty in range. Optimal combination of factors - based on a System Dynamics simulation and expert judgments - has been proposed according with a sampling analysis

Hybrid Genetic Bees Algorithm applied to Single Machine Scheduling with Earliness and Tardiness Penalties

This paper presents a hybrid Genetic-Bees Algorithm based optimised solution for the single machine scheduling problem. The enhancement of the Bees Algorithm (BA) is conducted using the Genetic Algorithm's (GA's) operators during the global search stage. The proposed enhancement aims to increase the global search capability of the BA gradually with new additions. Although the BA has very successful implementations on various type of optimisation problems, it has found that the algorithm suffers from weak global search ability which increases the computational complexities on NP-hard type optimisation problems e.g. combinatorial/permutational type optimisation problems. This weakness occurs due to using a simple global random search operation during the search process. To reinforce the global search process in the BA, the proposed enhancement is utilised to increase exploration capability by expanding the number of fittest solutions through the genetical variations of promising solutions. The hybridisation process is realised by including two strategies into the basic BA, named as â\u80\u9creinforced global searchâ\u80\u9d and â\u80\u9cjumping functionâ\u80\u9d strategies. The reinforced global search strategy is the first stage of the hybridisation process and contains the mutation operator of the GA. The second strategy, jumping function strategy, consists of four GA operators as single point crossover, multipoint crossover, mutation and randomisation. To demonstrate the strength of the proposed solution, several experiments were carried out on 280 well-known single machine benchmark instances, and the results are presented by comparing to other well-known heuristic algorithms. According to the experiments, the proposed enhancements provides better capability to basic BA to jump from local minima, and GBA performed better compared to BA in terms of convergence and the quality of results. The convergence time reduced about 60% with about 30% better results for highly constrained jobs

Evolutionary Computing and Swarm Intelligence algorithms for JSSP: Genetic Algorithm vs. Ant Colony Optimization techniques

To effectively manage and control the execution of production process, a correct scheduling activity must be performed. In any manufacturing environment, resources utilization, production rate, customer service level can be switched across the definition of suitable jobs’ sequence and tasks’ allocation. Being a NP complete and highly constrained problem, the resolution of the Job Shop Scheduling Problem (JSSP) is recognized as a key point to the factory optimization process. In recent years, a great number of multi-objective meta-heuristics has been proposed to evaluate the quality of a scheduling solution and obtain sets of compromising solutions. Powerful methods for running these kinds of optimization problems have been inspired by research on evolutionary theory and swarm intelligence approach. The cooperative behaviour that emerges from the organization of multi agent systems is the inspiring source of the two implemented approaches. The pursuit of optimal solution, on both benchmark and real-world job shop problem, has been successful tested for Genetic Algorithms (GA) and Ant Colony Optimization (ACO) techniques. This work starts with analysis on optimization methods for JSSP. Across the implementation of a new Genetic Algorithm and an improved model based on ant’s way, the performance of the two meta-heuristic approaches has been evaluated and compared. Similarity/dissimilarity of evolutionary and swarm intelligent approaches has pointed out. The logic, the parameters, the representation schemes and operators used in these two approaches have been widely discussed during this paper. A guide to the implementation of GAs and ACO approach to JSSP was performed

A Methodological Framework to Assess Mental Fatigue in Assembly Lines with a Collaborative Robot

In the current manufacturing assembly lines, collaboration between human and robot plays a significant role in the final output of the assembly, be it performance, quality or overall reliability. In that regard, smooth collaboration between human and robot is required to minimize the probabilities of human-system error, potential loss of performance and quality, and minimize the risk of decision making mistakes. Mental fatigue, and more importantly the cognitive load, of human operators is a crucial aspect in decision making, potential of error during the task and the overall flow of the assembly process. This paper reports about the development of a methodological framework to assess mental fatigue during a collaborative assembly task. In this framework, general complexity of the process and assembly task is investigated, as knowledge of the dynamic and static complexity can be helpful in reducing mental fatigue and cognitive load. We validated the applicability of the proposed frame in a real based collaborative assembly process

Mapping Uncertainty Sources Affecting Circularity: A Holonic Approach

Pursuing sustain ability, the traditional 'take- make-use-dispose' economy model is moving towards a circular approach by introducing the reverse flow for recovery of End- of-Life (EOL) or End-of-Use (EOU) products. This circularity of parts and information between industry and market has generated the Closed-Loop Supply Chain (CLSC). Circularity enhances reuse/recycle of parts and materials and products. However, it increases complexity in the supply as it inputs uncertainty sources regarding the returning and outgoing state of products, the required recovery processes, the expected consumer behavior and the unstable market trends. In this paper, an investigation of the uncertainties affecting CLSC and remanufacturing was selected. Authors aim at mapping, and reporting in connectivity, the sources of uncertainty affecting remanufacturing systems in order to identify the connections and causality relations between them. A holonic representation of the CLSC was elaborated for potential interdependencies assessment. Thus, a map of the uncertainties is then designed in the form of a labyrinth connecting the uncertainty drivers with the corresponding entities/holons: markEt, Consumer, Management, pRoduct and Process. The map is configured as a decision-support tool for application in remanufacturing context. The map allows to control the mutual effects between uncertainty sources in remanufacturing business environments

Design and optimization of a facility layout problem in virtual environment

Productivity improvement, due to high competition on global marketplace, requires a concurrent/reverse engineering approach to layout design, simulation and optimization in as short as possible times. Besides, a facility layout problem can be viewed as a combinatorial optimization problem solvable through heuristic methodologies. Designers, engineers and managers needs automated tools to effectively analyze layout and define an optimal configuration. Central to success is the integration of multi objective mathematical procedures with robust design techniques and virtual representation/validation in stereoscopic real scale. Flow analysis, plant design and optimal 3-D layout representation, with virtual environment validation, are the objects in our facility layout approach. In a Virtual Reality environment, using Axiomatic Design, it is possible to analyze alternative design configurations with little efforts and short time, obtaining improvements in communication, savings in changes and assuring design integration with computer tools. Encouraged by the big interest on Virtual Reality abilities, the paper presents an innovative robust design application based on a Rectangle-Packing placement optimization procedure with Virtual Reality environment validation of a real flow shop production process

Design and Optimization of a Facility Layout Problem in Virtual Environment

Productivity improvement, due to high competition on global marketplace, requires a concurrent/reverse engineering approach to layout design, simulation and optimization in as short as possible times. Besides, a facility layout problem can be viewed as a combinatorial optimization problem solvable through heuristic methodologies. Designers, engineers and managers needs automated tools to effectively analyze layout and define an optimal configuration. Central to success is the integration of multi objective mathematical procedures with robust design techniques and virtual representation/validation in stereoscopic real scale. Flow analysis, plant design and optimal 3-D layout representation, with virtual environment validation, are the objects in our facility layout approach. In a Virtual Reality environment, using Axiomatic Design, it is possible to analyze alternative design configurations with little efforts and short time, obtaining improvements in communication, savings in changes and assuring design integration with computer tools. Encouraged by the big interest on Virtual Reality abilities, the paper presents an innovative robust design application based on a Rectangle-Packing placement optimization procedure with Virtual Reality environment validation of a real flow shop production process