Evolutionary dynamic optimisation of airport security lane schedules

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Reducing costs whilst maintaining passenger satisfaction is an important problem for airports. One area this can be applied is the security lane checks at the airport. However, reducing costs through reducing lane openings typically increases queue length and hence passenger dissatisfaction. This paper demonstrates that evolutionary methods can be used to optimise airport security lane schedules such that passenger dissatisfaction and staffing costs can be minimised. However, it is shown that these schedules typically over-fit the forecasts of passenger arrivals at security such that in actuality significant passenger delays can occur with deviations from the forecast. Consequently, this paper further demonstrates that dynamic evolutionary re-optimisation of these schedules can significantly mitigate this over-fitting problem with much reduced passenger delays

Robustness and evolutionary dynamic optimisation of airport security schedules

Reducing security lane operations whilst minimising passenger waiting times in unforseen circumstances is important for airports. Evolutionary methods can design optimised schedules but these tend to over-fit passenger arrival forecasts resulting in lengthy waiting times for unforeseen events. Dynamic re-optimisation can mitigate for this issue but security lane schedules are an example of a constrained problem due to the human element preventing major modifications. This paper postulates that for dynamic re-optimisation to be more effective in constrained circumstances consideration of schedule robustness is required. To reduce over-fitting a simple methodology for evolving more robust schedules is investigated. Random delays are introduced into forecasts of passenger arrivals to better reflect actuality and a range of these randomly perturbed forecasts are used to evaluate schedules. These steps reduced passenger waiting times for actual events for both static and dynamic policies with minimal increases in security operations

Memetic Algorithms for Business Analytics and Data Science: A Brief Survey

This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018