There are positive effects of adopting Groundstar as a basic customer handling service. With the demand for flight efficiency, airport parking and the handling of cargo and passengers growing daily, the software will help streamline these activities by digitizing data and helping share it across various departments. Much in a way, the airport implements technology-based solutions that promote increased utilization of spaces as well as providing services that encourage customers to park at the airport in future. Hence, the ground software is effective because it helps to increase communication by improvising smart customer and cargo handling in the airport.

Groundstar also manages a wealth of data from managing airplanes between landing and take-off as well as displaying flight information for a passenger to movement of passenger trams (Reza, 2007, p. 138). The data is available on the 24/7 hour basis. Additionally, the multi-agent embedded in the software creates a new level of a new of campus intelligence. A pre-determined roles and responsibilities for each department encourage easier communicating between each of the department. According to Xiao and Zhang (2009, p. 2958), the agent-based model embedded in the software produces high-quality simulation for complex and large-scale system behaviour. The large-scale complex system creates a highly suitable and efficient delivery of the required components (Farschtschi, 2011). Ground duties are time-consuming and usually very complex. With the optimal solutions being transmitted automatically from various points, it becomes possible to coordinate between different points (Ghosh, 2010, p. 445).

Groundstar makes travelling effective by reducing travelling options for customers. The software oversees the concentration of fleet management by incorporating detailed information while still maximizing network paths (Reza, 2007, p. 139). Besides, the software reduces infeasibility associated with complex constrains at the airport. Delays that might result to overcrowding and crew connections will be reduced (Reza et al., 2007).

Considering the operational profitability of the aircraft depends on achieving the highest possible occupancy, Groundstar ensures that more customers are handled on a daily basis. The Aircraft generates more revenue if the flight flies on time, as compared to the lack of software, which does not passengers. The occupancy level of the aircraft is an important key figure, which translates to better annual sales. Positive results on the passenger foot room means that there will be more revenues for upgrading the software. Hence, it is clear that adapting the software suite not only optimizes the technical environment of the airport but also the organizational levels.

Besides, given that the airport’s gates are expensive resources for the air transportation, Groundstar can be considered effective because it improves airport gates, which is an effective factor in principle management of the company. Clearly, the objective of the Groundstar is optimizing activities in a weighted approach, which helps to evaluate the fitness of the airport process. Likewise, the ground star is effective because it increases the interaction of the software systems and help to ease the different pressures on the ground. Hummel (2001, p. 435) also notes that an effective software process reduces potential errors attributed to the data entry and translation between software systems.

However, workers can easily or wrongly interpret the output of the system, which can potentially lead to errors, and poor performance of the systems. Nonetheless, Groundstar reduces these errors by ensuring that the tickets, checking-in processing and the passing of the security system are effective. Likewise, the software is designed to enforce systems of automated management. They include creating effective models of governance that are autonomous and the automatic in nature by ensuring of the software process.

Groundstar is also effective in improving that flight processes. Critical components such as the check-in discounted for security reasons for the business logic are in place to improve the logic flow of passengers and cargo. The approach helps to streamline passenger travel group as well as ensure that actives are streamlined. Another common issue is the reduction of wait time for companies working closely with the company owning the Groundstar. For instance, travel companies that help ferry passengers from and to the airport via the road. The concept of arrival loungers reduces real-time information where transportation helps arriving customers to be less stressed and wondering when the immediate transportation will arrive (Lake, 2004).

Clearly, the technology plays an important role in helping passenger processing. However, although the system could be effective if there are no obstacles that challenge the key widespread implementation of the system. The airport management and airlines might have different airports and oversee that the common use system serves passengers effectively. Most importantly, the information is beneficial to the airport management. Hence, in the future, Groundstar will be challenged by the desire to curb congestion. In particular, there should be components that oversee the bypass of the terminal for the common solution. However, having Groundstar increases the priorities on the common use systems that passenger should have. As noted, the system is effective because it reduces self-service cafes that are beneficial for the airport management while still increasing the service and revenue of the typical flight and boarding of information.

Conclusively, the software components are designed to streamline the modelling and the transform the airport network flows. In summary, a primary research that analyses the influence of the software to various airport operations will help register the validity of the system.

References

Abdi, M. and Sharma, S. (2008). Information system for flight disruption management.International Journal of Information Management, 28(2), pp.136-144.

Farschtschi1, Y., Moeller, D., Widemann, M., Wittmann, J., & Vakilzadian, H. (2011). GLOBAL OPTIMIZATION OF INTERDEPENDENT TURNAROUND PROCESSES AT AIRPORTS. University of Applied Sciences Berlin, Faculty of Ecological Informatics, 167-189. Retrieved April 4, 2011.

Ghosh, S. (2010). Models in software engineering. Berlin: Springer.

Hummel, J. (2001). Effective Visual Basic. Boston: Addison-Wesley.

Lake, J. (2004). Border and transportation security. Washington, D.C.: Congressional Research Service, Library of Congress.

Reza Abdi, M. and Sharma, S. (2007). Strategic/tactical information management of flight operations in abnormal conditions through Network Control Centre. International Journal of Information Management, 27(2), pp.119-138.

Wenzel, A., Scheurer, L., Künzi, R., Fritschy, J., Mohler, H. and Benke, D. (1995). Distribution of NMDA receptor subunit proteins NR2A, 2B, 2C and 2D in rat brain.NeuroReport, 7(1), pp.45-48.

Xiao, Z. and Zhang, S. (2009). Reinforcement Learning Model Based on Regret for Multi-Agent Conflict Games. Journal of Software, 19(11), pp.2957-2967.
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Posted on May 26, 2016Author TutorCategories Question, Questions