Equitable Optimization Research Articles

Equitable optimization of US airline route networks

Restructuring route networks (i.e., modifying the graph of origin-destination pairs) remains a promising alternative for reducing the airline industry's environmental impact. However, there exists a fundamental trade-off between emissions from flight and airport accessibility, since flights connecting underserved, low-accessibility communities tend to possess high CO2/seat-mile ratios. Thus, this work develops an open-source analytical framework and methodology that restructures U.S. airline route networks to simultaneously minimize emissions and maximize airport accessibility. To achieve this goal, this paper designs a metric to quantify airport accessibility and combines it with an open-source system-wide emissions estimation methodology. This facilitates the creation of a mixed-integer linear optimization model that returns revised flight frequencies and aircraft allotment. Using United Airlines 2019 Q3 data as a case study, this model is able to construct an alternative route network with a 25% reduction on the total number of flights, 4.4% decrease in the average emissions per seat-mile and a 17.6% improvement in the spread of the airports' accessibility scores, all while satisfying historic passenger demand.

Equitable Optimization for Multicast Communication

Multicast communication is characterized by the multiplicity of streams defining different groups, where each stream has multiple sources. A multicast communication tends to flood the network with a large number of flows that can overload some nodes and unload others. This imbalance in the load distribution weakens network performance and could produce bottlenecks around overloaded nodes. We propose in this article an approach based on a combination of a flow approach and a multi-agent optimization to resolve the load balancing issue of multicast communication. We use ordered weighted average (OWA), a multi-criteria optimization method, to balance the degree of the nodes, ensuring a balanced load distribution across the network. The experiments conducted on a series of networks show that our approach provides a better equitable load assignment.

Hybrid Algorithm Edge Detected DICOM Image Enhancement and Analysis based on Genetic Algorithm for Evolution and Best Fit Value

The segmentation of a DICOM standard medical image is a necessary technique which is essential for feature extraction, object edge detection and classification of the segments of the image. The DICOM image is partitioned based on the Hybrid ACO-CPM algorithm, based on the edges in the image, for analysis. The edges are seen as the boundaries within the image which differentiates different regions in the image. The factors that links to the boundary discontinuities that co-exists between the pixels of DICOM image, like texture, intensity and gradient are rendered redundant and are taken care with the application of the Hybrid ACO-CPM algorithm. DICOM image features correspond to that of meta-heuristic characteristics, which are considered during the application of Hybrid ACO-CPM algorithm. The results obtained from this non-deterministic behavior needs to be optimized over a large space called as the search space, wherein the lists of all possible solutions are provided. Each solution is to be marked as a value fit to be termed problematic and needs to be synthesized for an optimized solution. Among various techniques that provide solutions in obtaining an equitable optimization solution, Genetic Algorithms (GA) corroborates as one of the persuasive techniques in a large search space. In this paper we propose an efficient and effective workflow based on a methodology, that provides an overview of the image enhancement and object classification for a DICOM image using Genetic Algorithm (GA). The edge detected medical standard DICOM image obtained from the Hybrid ACO-CPM algorithm is modified with respect to critical edge data. With the application of GA methodology, the process of enhancing the image ultimately suffices by rendering an image suitable for a specific application with an improved visual quality of the segmented image. A Figure-of-Merit is constructed to differentiate between the image metrics and their best fit values obtained for the images with respect to the Ant Colony Optimization (ACO) algorithm and proposed Hybrid ACO-CPM algorithm, upon enhancing the images using GA

A novel crossover operator based on variable importance for evolutionary multi-objective optimization with tree representation

Selecting reliable predictors has always been crucial in classification. Especially decision trees are very popular for solving supervised variable selection and classification problems. When variable selection has to be performed with regard to acquisition costs, which have to be paid whenever the respective variable is extracted for a new observation, the problem of balancing the predictive power of the model against its costs describes a multi-objective optimization problem which can be solved with meta-heuristics such as evolutionary multi-objective algorithms. In this paper, we present a non-hierarchical evolutionary multi-objective tree learner (NHEMOtree) based on genetic programming using a binary decision tree representation to handle multi-objective optimization problems with equitable optimization criteria. This tree learner is applied to a multi-objective classification problem from medicine as well as to simulated data to evaluate its performance relative to two wrapper approaches based on either NSGA-II or SMS-EMOA with bitstring representation and CART as the enclosed classification algorithm. Moreover, a novel crossover operator based on a multi-objective variable importance measure is introduced. Using this crossover operator, NHEMOtree can be improved.

Equitable Aircraft Noise-Abatement Departure Procedures

This paper deals with the optimization of aircraft noise-abatement departure procedures. A multicriteria optimization strategy is presented, where the fairness of the optimal trajectories is assessed vis-a-vis the different noise-sensitive locations around the airport of study. This equitable optimization is formulated as the minimization of the maximum noise-annoyance deviation regarding all considered locations. This strategy is complemented with an iterative lexicographic optimization algorithm which, in turn, guarantees the Pareto efficiency condition of the final solution. Aircraft operating costs are also considered by neglecting the marginal benefits of noise reduction below a certain threshold value. An application example is shown (as an illustrative case) based on a departure of runway 02 at Girona airport in Catalonia, Spain. The results show the feasibility of this technique, which is intended to be used by procedure designers or airport authorities.

A multi-criteria approach to fair and efficient bandwidth allocation

In systems which serve many users there is a need to respect some fairness rules while looking for the overall efficiency. This applies among others to network design where a central issue is how to allocate bandwidth to flows efficiently and fairly. The so-called max–min fairness is widely used to meet these goals. However, allocating the bandwidth to optimize the worst performance may cause a large worsening of the overall throughput of the network. In this paper we show how the concepts of mult-criteria equitable optimization can effectively be used to generate various fair and efficient allocation schemes. We introduce a multi-criteria model equivalent to equitable optimization and we develop a corresponding reference point procedure to generate fair and efficient bandwidth allocations. Our analysis is focused on the nominal network design for elastic traffic that is currently the most significant traffic of IP networks. The procedure is tested on a sample network dimensioning problem for elastic traffic and its abilities to model various preferences are demonstrated.