OPTIMAL REASSIGNMENT OF FLIGHTS TO AIRPORT BAGGAGE UNLOADING CAROUSELS IN RESPONSE TO TEMPORARY MALFUNCTIONS

Being able to efficiently reassign outbound flights to baggage unloading carousels (BUCs) following temporary malfunctions is very important for airport operators. This study proposes an optimization model with a heuristic to solve the carousel reassignment problem. The objective is to minimize the total disturbance and overlapping time caused by the reassignment of outbound flights. A heuristic is developed to efficiently solve large-sized instances. The proposed approach is then applied to solve real-world instances of the problem at a major international airport in Taiwan. The computation time is about two minutes. The objective value obtained with the heuristic is more than 15% better than that obtained by the manual approach currently used by the operator. The improvement is gained mostly from the reduction in total temporal disturbance and overlapping time. The proposed approach could assist the operator in reassigning outbound flights to BUCs in response to malfunctions

Robustness Algorithms for the Airport Gate Assignment Problem

Assigning commercial flights to available airport gates can have a major impact on the efficiency of flight schedules as well as on the level of passenger satisfaction with the service. These assignments also depend on the service requirements of flights and the capacity of stand facilities. Unexpected changes also called perturbations, like those due to air traffic delays, severe weather conditions, or equipment failures, may disrupt the initial assignments and increase the difficulty of maintaining smooth operations, which will detrimentally affect costumer satisfaction. The provision of solutions which reduce the potential detrimental effect of perturbations in the stands already assigned on the day of operation is desirable and some approaches are presented here, and compare between them to help identify their performance and trends

An Evolutionary Algorithm and operators for the Airport Baggage Sorting Station Problem

Taking into consideration the constraints and objectives to appropriately assigning the available airport resources throughout the period of time an airport provides its services can greatly affect the quality of service which airlines and airports provide to their customers. The appropriate assignments can help airlines and airports to keep to published schedules, by minimising changes in these schedules, reducing delays and considering customers preferences when assigning the resources. Given the expected increases in civil air traffic, and the variety of resources, the complexities of resource scheduling and assignment continue to increase. For this reason, as well as the dynamic nature of the problems, scheduling and assignment are becoming increasingly more difficult.An Evolutionary Algorithm (EA) is presented together with some different operators, which are used to find good solutions to the Airport Baggage Sorting Station Assignment Problem (ABSSAP) for when there are not sufficient resources up to when the number of resources is sufficient to full fill the demand on these resources. The contributions of these different operators are studied and compared to other approaches, giving insights into how the appropriate choice may depend upon the specifics of the problem at the time

Robustness Algorithms for the Airport Gate Assignment Problem

Assigning commercial flights to available airport gates can have a major impact on the efficiency of flight schedules as well as on the level of passenger satisfaction with the service. These assignments also depend on the service requirements of flights and the capacity of stand facilities. Unexpected changes also called perturbations, like those due to air traffic delays, severe weather conditions, or equipment failures, may disrupt the initial assignments and increase the difficulty of maintaining smooth operations, which will detrimentally affect costumer satisfaction. The provision of solutions which reduce the potential detrimental effect of perturbations in the stands already assigned on the day of operation is desirable and some approaches are presented here, and compare between them to help identify their performance and trends

Constructive and evolutionary algorithms for airport baggage sorting station and gate assignment problems

Correct assignment of airport resources can greatly affect the quality of service which airlines and airports provide to their customers. Good assignments can help airlines and airports to keep to published schedules, by minimising changes in these schedules and reducing delays. Given the expected increases in civil air traffic, the complexities of resource scheduling and assignment continue to increase. For this reason, as well as the dynamic nature of the problems, scheduling and assignment are becoming increasingly more difficult. The assignment of baggage sorting stations to flights is one of the resource assignment problems at an airport, and like many other real world optimisation problems, it naturally has several objectives, which conflict with each other. A model of the problem is presented, different approaches to obtaining good solutions are looked at and studied to gain an insight into their qualities. Furthermore, algorithms are studied to improve the already good solutions obtained by the approaches considered and their performance is studied where some characteristics of the problem change, such as the number of baggage sorting stations or the topology of the airport. Changes to the flight schedule on the day of operation may invalidate previous assignments of flights to resources. These perturbations may not only affect the disrupted flights but also other flights already assigned. Some existing approaches are looked at, and others are suggested to take account of these potential perturbations at the time the assignments are generated with the aim of mitigating their detrimental effect on the day of operation. The constructive search algorithms and robustness methods are potentially important in a wider variety of problems other than the Airport Baggage Sorting Station Assignment Problem (ABSSAP). By way of illustration, the same techniques are applied to the widely studied Airport Gate Assignment Problem (AGAP)

Constructive and evolutionary algorithms for airport baggage sorting station and gate assignment problems

Correct assignment of airport resources can greatly affect the quality of service which airlines and airports provide to their customers. Good assignments can help airlines and airports to keep to published schedules, by minimising changes in these schedules and reducing delays. Given the expected increases in civil air traffic, the complexities of resource scheduling and assignment continue to increase. For this reason, as well as the dynamic nature of the problems, scheduling and assignment are becoming increasingly more difficult. The assignment of baggage sorting stations to flights is one of the resource assignment problems at an airport, and like many other real world optimisation problems, it naturally has several objectives, which conflict with each other. A model of the problem is presented, different approaches to obtaining good solutions are looked at and studied to gain an insight into their qualities. Furthermore, algorithms are studied to improve the already good solutions obtained by the approaches considered and their performance is studied where some characteristics of the problem change, such as the number of baggage sorting stations or the topology of the airport. Changes to the flight schedule on the day of operation may invalidate previous assignments of flights to resources. These perturbations may not only affect the disrupted flights but also other flights already assigned. Some existing approaches are looked at, and others are suggested to take account of these potential perturbations at the time the assignments are generated with the aim of mitigating their detrimental effect on the day of operation. The constructive search algorithms and robustness methods are potentially important in a wider variety of problems other than the Airport Baggage Sorting Station Assignment Problem (ABSSAP). By way of illustration, the same techniques are applied to the widely studied Airport Gate Assignment Problem (AGAP)

Resource allocation optimization problems in the public sector

This dissertation consists of three distinct, although conceptually related, public sector topics: the Transportation Security Agency (TSA), U.S. Customs and Border Patrol (CBP), and the Georgia Trauma Care Network Commission (GTCNC). The topics are unified in their mathematical modeling and mixed-integer programming solution strategies. In Chapter 2, we discuss strategies for solving large-scale integer programs to include column generation and the known heuristic of particle swarm optimization (PSO). In order to solve problems with an exponential number of decision variables, we employ Dantzig-Wolfe decomposition to take advantage of the special subproblem structures encountered in resource allocation problems. In each of the resource allocation problems presented, we concentrate on selecting an optimal portfolio of improvement measures. In most cases, the number of potential portfolios of investment is too large to be expressed explicitly or stored on a computer. We use column generation to effectively solve these problems to optimality, but are hindered by the solution time and large CPU requirement. We explore utilizing multi-swarm particle swarm optimization to solve the decomposition heuristically. We also explore integrating multi-swarm PSO into the column generation framework to solve the pricing problem for entering columns of negative reduced cost. In Chapter 3, we present a TSA problem to allocate security measures across all federally funded airports nationwide. This project establishes a quantitative construct for enterprise risk assessment and optimal resource allocation to achieve the best aviation security. We first analyze and model the various aviation transportation risks and establish their interdependencies. The mixed-integer program determines how best to invest any additional security measures for the best overall risk protection and return on investment. Our analysis involves cascading and inter-dependency modeling of the multi-tier risk taxonomy and overlaying security measurements. The model selects optimal security measure allocations for each airport with the objectives to minimize the probability of false clears, maximize the probability of threat detection, and maximize the risk posture (ability to mitigate risks) in aviation security. The risk assessment and optimal resource allocation construct are generalizable and are applied to the CBP problem. In Chapter 4, we optimize security measure investments to achieve the most cost-effective deterrence and detection capabilities for the CBP. A large-scale resource allocation integer program was successfully modeled that rapidly returns good Pareto optimal results. The model incorporates the utility of each measure, the probability of success, along with multiple objectives. To the best of our knowledge, our work presents the first mathematical model that optimizes security strategies for the CBP and is the first to introduce a utility factor to emphasize deterrence and detection impact. The model accommodates different resources, constraints, and various types of objectives. In Chapter 5, we analyze the emergency trauma network problem first by simulation. The simulation offers a framework of resource allocation for trauma systems and possible ways to evaluate the impact of the investments on the overall performance of the trauma system. The simulation works as an effective proof of concept to demonstrate that improvements to patient well-being can be measured and that alternative solutions can be analyzed. We then explore three different formulations to model the Emergency Trauma Network as a mixed-integer programming model. The first model is a Multi-Region, Multi-Depot, Multi-Trip Vehicle Routing Problem with Time Windows. This is a known expansion of the vehicle routing problem that has been extended to model the Georgia trauma network. We then adapt an Ambulance Routing Problem (ARP) to the previously mentioned VRP. There are no known ARPs of this magnitude/extension of a VRP. One of the primary differences is many ARPs are constructed for disaster scenarios versus day-to-day emergency trauma operations. The new ARP also implements more constraints based on trauma level limitations for patients and hospitals. Lastly, the Resource Allocation ARP is constructed to reflect the investment decisions presented in the simulation.Ph.D

Integrated and joint optimisation of runway-taxiway-apron operations on airport surface

Airports are the main bottlenecks in the Air Traffic Management (ATM) system. The predicted 84% increase in global air traffic in the next two decades has rendered the improvement of airport operational efficiency a key issue in ATM. Although the operations on runways, taxiways, and aprons are highly interconnected and interdependent, the current practice is not integrated and piecemeal, and overly relies on the experience of air traffic controllers and stand allocators to manage operations, which has resulted in sub-optimal performance of the airport surface in terms of operational efficiency, capacity, and safety. This thesis proposes a mixed qualitative-quantitative methodology for integrated and joint optimisation of runways, taxiways, and aprons, aiming to improve the efficiency of airport surface operations by integrating the operations of all three resources and optimising their coordination. This is achieved through a two-stage optimisation procedure: (1) the Integrated Apron and Runway Assignment (IARA) model, which optimises the apron and runway allocations for individual aircraft on a pre-tactical level, and (2) the Integrated Dynamic Routing and Off-block (IDRO) model, which generates taxiing routes and off-block timing decisions for aircraft on an operational (real-time) level. This two-stage procedure considers the interdependencies of the operations of different airport resources, detailed network configurations, air traffic flow characteristics, and operational rules and constraints. The proposed framework is implemented and assessed in a case study at Beijing Capital International Airport. Compared to the current operations, the proposed apron-runway assignment reduces total taxiing distance, average taxiing time, taxiing conflicts, runway queuing time and fuel consumption respectively by 15.5%, 15.28%, 45.1%, [58.7%, 35.3%, 16%] (RWY01, RWY36R, RWY36L) and 6.6%; gated assignment is increased by 11.8%. The operational feasibility of this proposed framework is further validated qualitatively by subject matter experts (SMEs). The potential impact of the integrated apron-runway-taxiway operation is explored with a discussion of its real-world implementation issues and recommendations for industrial and academic practice.Open Acces