WB
W.J. Breedveld
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With a rising number of airline passengers, airport ground support vehicles are under increased pressure to deliver their services on time. In this thesis, we apply two methods for vehicle routing problems not yet explored for the aircraft ground handling problem: Hybrid Genetic Search (HGS) and Iterated Local Search (ILS) for the aircraft ground handling problem at Schiphol airport, and compare them against an established method. We show that both HGS and ILS are capable of solving realistic instances within a realistic amount of time available were such an algorithm to be deployed. We also show a method to revise a schedule in the case of disruptions and show this method can easily be performed without large changes to the solver. Our results also show ILS to perform better on small instances and HGS to perform better as the problem difficulty is increased. This thesis shows HGS and ILS are well suited to be used for airport ground support vehicle scheduling at airports, and can be used as a basis to build these systems. It provides algorithms which could be used in these systems, also in case of scheduling disruptions. This thesis can serve as the starting point for further investigating the usage of HGS and ILS, now that their effectiveness has been shown, using potentially new search methods. We also highlight other potential research avenues in comparing the results for airports of different shapes and sizes and with additional constraints, which may be explored using the methods in this thesis as a basis.
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With a rising number of airline passengers, airport ground support vehicles are under increased pressure to deliver their services on time. In this thesis, we apply two methods for vehicle routing problems not yet explored for the aircraft ground handling problem: Hybrid Genetic Search (HGS) and Iterated Local Search (ILS) for the aircraft ground handling problem at Schiphol airport, and compare them against an established method. We show that both HGS and ILS are capable of solving realistic instances within a realistic amount of time available were such an algorithm to be deployed. We also show a method to revise a schedule in the case of disruptions and show this method can easily be performed without large changes to the solver. Our results also show ILS to perform better on small instances and HGS to perform better as the problem difficulty is increased. This thesis shows HGS and ILS are well suited to be used for airport ground support vehicle scheduling at airports, and can be used as a basis to build these systems. It provides algorithms which could be used in these systems, also in case of scheduling disruptions. This thesis can serve as the starting point for further investigating the usage of HGS and ILS, now that their effectiveness has been shown, using potentially new search methods. We also highlight other potential research avenues in comparing the results for airports of different shapes and sizes and with additional constraints, which may be explored using the methods in this thesis as a basis.
Bachelor thesis
(2024)
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W.J. Breedveld, E. Demirović, M.L. Flippo, I.C.W.M. Marijnissen, I.M. Olkhovskaia
This paper looks at the different parts of the Critical Path and Resource Utilization (CPRU) heuristic for use in the Resource Constraint Project Scheduling Problem, with variable resources (RCPSP-t programming problem). RCPSP-t has many real-world instances such as in hospitals or manufacturing. Optimizing the solution generation for these instances may improve the efficiency of these industries. CPRU was split into parts and different versions were compared against VSIDS a more modern heuristic to determine if problem-specific heuristics perform better than generally good ones. A new adaptation of CPRU that adapts the research utilisation score by calculating the fraction used based on the amount of resources available given scheduled activities instead of looking at the resources of the problem instance. From the results, we concluded that CPRU may perform better for large instances of RCPSP- t compared to VSIDS in terms of generating good schedules within a few iterations. We also found that CPRU generates better schedules given a small time limit. We did not find evidence that the CPRU adaptation improved performance compared to CPRU in terms of schedule and time limits.
...
This paper looks at the different parts of the Critical Path and Resource Utilization (CPRU) heuristic for use in the Resource Constraint Project Scheduling Problem, with variable resources (RCPSP-t programming problem). RCPSP-t has many real-world instances such as in hospitals or manufacturing. Optimizing the solution generation for these instances may improve the efficiency of these industries. CPRU was split into parts and different versions were compared against VSIDS a more modern heuristic to determine if problem-specific heuristics perform better than generally good ones. A new adaptation of CPRU that adapts the research utilisation score by calculating the fraction used based on the amount of resources available given scheduled activities instead of looking at the resources of the problem instance. From the results, we concluded that CPRU may perform better for large instances of RCPSP- t compared to VSIDS in terms of generating good schedules within a few iterations. We also found that CPRU generates better schedules given a small time limit. We did not find evidence that the CPRU adaptation improved performance compared to CPRU in terms of schedule and time limits.