The world is on the verge of the fourth industrial revolution that will considerably influence society and human life. Today human being is surrounded by technological advancement and every day we face new sophisticated technological systems that affect our daily lives. The business environment is being influenced by Industry 4.0 significantly and a massive transformation in labour market can be observed. The digital economy has become a disruptive factor in several sectors and it has shown a major impact on the logistic industry in terms of workforce transformation. The question that arises is that to what extent the logistic sector is ready for the digital transformation in Industry 4.0 and what factors should be considered by industry players, governments and multi-stakeholders in order to simplify workforce transformation. This study followed a qualitative approach using Grounded Theory to explain the phenomenon of workforce transformation within the logistic sector in Industry 4.0. Furthermore, a literature review was used to explain the role of human resource management in simplification of this process .The findings show that there is a lack of adequate awareness about the impact of the digital transformation on labour. Furthermore, it discusses the role of human resource management as an agent of change in Industry 4.0. The current research presents recommendations for different stakeholders on how to prepare the current and future workforce for the upcoming changes.This study is significant in the sense that it will add to the existing literature and provide practitioners with vital information that can be used to simplify the digital transformation of logistic industry by preparing labor market.
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Cooperation is more likely when individuals can choose their interaction partner. However, partner choice may be detrimental in unequal societies, in which individuals differ in available resources and productivity, and thus in their attractiveness as interaction partners. Here we experimentally examine this conjecture in a repeated public goods game. Individuals (n = 336), participating in groups of eight participants, are assigned a high or low endowment and a high or low productivity factor (the value that their cooperation generates), creating four unique participant types. On each round, individuals are either assigned a partner (assigned partner condition) or paired based on their self-indicated preference for a partner type (partner choice condition). Results show that under partner choice, individuals who were assigned a high endowment and high productivity almost exclusively interact with each other, forcing other individuals into less valuable pairs. Consequently, pre-existing resource differences between individuals increase. These findings show how partner choice in social dilemmas can amplify resource inequality.
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Assigning gates to flights considering physical, operational, and temporal constraints is known as the Gate Assignment Problem. This article proposes the novelty of coupling a commercial stand and gate allocation software with an off-the-grid optimization algorithm. The software provides the assignment costs, verifies constraints and restrictions of an airport, and provides an initial allocation solution. The gate assignment problem was solved using a genetic algorithm. To improve the robustness of the allocation results, delays and early arrivals are predicted using a random forest regressor, a machine learning technique and in turn they are considered by the optimization algorithm. Weather data and schedules were obtained from Zurich International Airport. Results showed that the combination of the techniques result in more efficient and robust solutions with higher degree of applicability than the one possible with the sole use of them independently.