A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Designing electronic collaborative learning environments

Electronic collaborative learning environments for learning and working are in vogue. Designers design them according to their own constructivist interpretations of what collaborative learning is and what it should achieve. Educators employ them with different educational approaches and in diverse situations to achieve different ends. Students use them, sometimes very enthusiastically, but often in a perfunctory way. Finally, researchers study them and—as is usually the case when apples and oranges are compared—find no conclusive evidence as to whether or not they work, where they do or do not work, when they do or do not work and, most importantly, why, they do or do not work. This contribution presents an affordance framework for such collaborative learning environments; an interaction design procedure for designing, developing, and implementing them; and an educational affordance approach to the use of tasks in those environments. It also presents the results of three projects dealing with these three issues

MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,

Assessing organizations collaboration readiness: a behavioral approach

Dissertation presented at the Faculty of Sciences and Technology of the New University of Lisbon to obtain the degree of Doctor in Electrical Engineering, specialty of Robotics and Integrated ManufacturingThis thesis presents an approach for assessing organizations‘ readiness to collaborate. This assessment is based in three fundamental aspects, namely (1) on collaboration preparedness, which aims at assessing whether a partner has adequate collaboration-related character traits; (2) on competencies fitness which is predominantly aimed at assessing how well an organization is able to use its competencies in a collaboration context; and (3) on willingness to collaborate, which is a concept applied to assess whether an organization is, or is not, really interested to engage in concrete collaboration opportunities. The proposed approach contributes to the formation of improved collaborative networks, increasing their likelihood of success. The principal characteristic of the model lies in the fact that it follows a behavioral perspective. As such, collaboration preparedness is based on the idea of the organizations‘ character, traits and behavioral patterns. Competencies fitness is in turn based on the so-called soft competencies, exploring the performance influences/effects of the soft competencies on the hard ones in a collaboration context. Finally, willingness to collaborate is based on the organization‘s planned behavior, attitudes and intentions that are perceived in/from a partner. The work involved in the conceptualization of readiness to collaborate includes the utilization of text data mining to discover the behavioral aspects, namely the collaboration-related organization‘s traits which are relevant for assessing collaboration readiness. Bayesian belief networks are proposed as a way to deal with the underlying uncertainty in assessing collaboration readiness. A soft versus hard competencies dichotomy is used to develop the concept of competencies fitness, proposing the adjusted competencies profile and the fitness level, as the way to assess whether a partner‘s competencies fit in a collaboration opportunity. The Theory of the Planned Behavior is adapted from social sciences and used in organizations in collaboration contexts. Various modeling experiments were performed to assist in the development of this readiness concept. The validation through some cases of partnerships is proposed to evaluate the underlying collaboration readiness assessment model

A new model for solution of complex distributed constrained problems

In this paper we describe an original computational model for solving different types of Distributed Constraint Satisfaction Problems (DCSP). The proposed model is called Controller-Agents for Constraints Solving (CACS). This model is intended to be used which is an emerged field from the integration between two paradigms of different nature: Multi-Agent Systems (MAS) and the Constraint Satisfaction Problem paradigm (CSP) where all constraints are treated in central manner as a black-box. This model allows grouping constraints to form a subset that will be treated together as a local problem inside the controller. Using this model allows also handling non-binary constraints easily and directly so that no translating of constraints into binary ones is needed. This paper presents the implementation outlines of a prototype of DCSP solver, its usage methodology and overview of the CACS application for timetabling problems

Analysing teamwork in higher education: an empirical study on the antecedents and consequences of team cohesiveness

Uno de los factores más importantes del trabajo en equipo es la cohesión entre sus miembros. Sin embargo, escasos trabajos analizan sus antecedentes y consecuencias. El presente estudio utiliza el modelo Input-Process-Output para analizar el impacto de factores individuales y de la tarea sobre la cohesión del equipo, así como la influencia de la cohesión del equipo sobre la eficacia del mismo. En base a una encuesta a 160 alumnos que realizaron trabajos en grupo, los resultados muestran que el grado de cooperación y el comportamiento colaborativo tienen una influencia positiva en la cohesión del equipo, mientras que la carga de trabajo y la complejidad de la tarea tienen una influencia negativa en la misma. Además, la cohesión del equipo influye positivamente en el aprendizaje percibido, la satisfacción con el trabajo en equipo y la calidad esperada. Finalmente, tanto el aprendizaje percibido como la calidad esperada predicen la satisfacción con el trabajo en equipo.One of the most important components of effective teamwork is cohesiveness. However, few empirical studies on the antecedents and consequences of group cohesiveness exist. In response to this gap, the current study draws on the Input-Process-Output model of team effectiveness to investigate the impact of individual and task factors on team cohesiveness, as well as the influence of team cohesiveness on students’ perceived learning, satisfaction with teamwork, and expected quality in the outcome. Based on a survey of 160 undergraduate students who worked in groups, the findings show that cooperativeness and collaborative behaviour have a positive influence on team cohesiveness, while workload and task complexity have a negative influence on it. Additionally, team cohesiveness is positively related to perceived learning, satisfaction with teamwork, and expected quality. Finally, both perceived learning and expected quality predict satisfaction with teamwork

Overview on agent-based social modelling and the use of formal languages

Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft

Resilience and Controllability of Dynamic Collective Behaviors

The network paradigm is used to gain insight into the structural root causes of the resilience of consensus in dynamic collective behaviors, and to analyze the controllability of the swarm dynamics. Here we devise the dynamic signaling network which is the information transfer channel underpinning the swarm dynamics of the directed interagent connectivity based on a topological neighborhood of interactions. The study of the connectedness of the swarm signaling network reveals the profound relationship between group size and number of interacting neighbors, which is found to be in good agreement with field observations on flock of starlings [Ballerini et al. (2008) Proc. Natl. Acad. Sci. USA, 105: 1232]. Using a dynamical model, we generate dynamic collective behaviors enabling us to uncover that the swarm signaling network is a homogeneous clustered small-world network, thus facilitating emergent outcomes if connectedness is maintained. Resilience of the emergent consensus is tested by introducing exogenous environmental noise, which ultimately stresses how deeply intertwined are the swarm dynamics in the physical and network spaces. The availability of the signaling network allows us to analytically establish for the first time the number of driver agents necessary to fully control the swarm dynamics