“Biosim” is a simulation software which works to simulate the harvesting system.This system is able to design a model for any logistic problem with the combination of several objects so that the artificial system can show the performance of an individual model. The system will also describe the efficiency, possibility to be chosen for real life application of that particular model. So, when any one wish to setup a logistic model like- harvesting system, in real life he/she may be noticed about the suitable prostitution for his plants and factories as well as he/she may get information about the least number of objects, total time to complete the task, total investment required for his model, total amount of noise produced for his establishment in advance. It will produce an advance over view for his model. But “Biosim” is quite slow .As it is an object based system, it takes long time to make its decision. Here the main task is to modify the system so that it can work faster than the previous. So, the main objective of this thesis is to reduce the load of “Biosim” by making some modification of the original system as well as to increase its efficiency. So that the whole system will be faster than the previous one and performs more efficiently when it will be applied in real life. Theconcept is to separate the execution part of ”Biosim” form its graphical engine and run this separated portion in a third generation language platform. C++ is chosenhere as this external platform. After completing the proposed system, results with different models have been observed. The results show that, for any type of plants of fields, for any number of trucks, the proposed system is faster than the original system. The proposed system takes at least 15% less time “Biosim”. The efficiency increase with the complexity of than the original the model. More complex the model, more efficient the proposed system is than original “Biosim”.Depending on the complexity of a model, the proposed system can be 56.53 % faster than the original “Biosim”.

This research thesis aims to explore the interplay between the recent introduction of generative artificial intelligence at work and professional role identity. As the public introduction of generative artificial intelligence shook up people’s lives in November 2022, we have reasons to think that an exploration of workers’ professional role identity regardinggenerative artificial intelligence is relevant. Due to the recent studies related to the massive introduction of artificial intelligence in the content creators’ field, we chose to explore how their role and their identity are evolving. Organizations face challenges in managing artificial intelligence systems and their impact on professional role identity, which, however, has received limited scholarly attention and is not well-understood yet. While prior research has studied various aspects related to artificial intelligence and has suggested that artificial intelligence can enhance productivity, efficiency, and prosperity, it has to a large extent neglected the influence of artificial intelligence on professional role identities. Therefore, this research aims to contribute to artificial intelligence literature by providing empirical insights into the evolution of work and identity.  To address the abovementioned gap, we build on previous research and conduct a qualitative study. We gathered important insights through interviews with several content creators, including photographers and designers from different countries, to discuss their experiences and elaborate a more comprehensive approach to the potential consequences of the introduction of generative artificial intelligence at work. Taking inspiration from a phenomenological approach, clarifications have been brought forward on the reasons exposed by content creators to introduce generative artificial intelligence in their work process and the consequences of such a choice. A broader perspective has been borrowed in order to question the legitimacy of clients and peers regarding the integration of generative artificial intelligence at work. More than adding a layer on the benefits of the introduction of generative artificial intelligence at work, our thesis sheds light on what we call “a dual motion” for workers’role identity, highlighting both interactions between generative artificial intelligence at work and professional role identity, challenging and enhancing each other. Additionally, this research explores manners content creators can enrich their work with generative artificial intelligence. This thesis gains perspective on this subject and aims to expose practical implications for workers to inform and broaden people’s minds about generative artificial intelligence.