Quadratic and Higher-Order Unconstrained Binary Optimization of Railway Dispatching Problem for Quantum Computing

The consequences of disruptions in railway traffic are the primary cause of passengers' dissatisfaction. Hence, appropriate dispatching decisions are necessary (e.g., by assigning the order of trains), given the numerous restrictions of traffic nature. The latter is perceived as an NP-hard problem. This paper outlines QUBO (quadratic unconstrained binary optimization) and HOBO (higher-order binary optimization) representations for dispatching problems of railway traffic management. Specifically, we consider minimal span between trains, minimal stay on stations, station/track occupation, and rolling stock circulation. The main result is the hybrid algorithm to deal with disturbances in rail traffic on single-, double- and multi-track lines; the demonstrative model illustrates the issue briefly. This algorithm can solve railway dispatching problems using the quantum annealer or any other QUBO-based optimization device

Learning Abstract Visual Reasoning via Task Decomposition: A Case Study in Raven Progressive Matrices

One of the challenges in learning to perform abstract reasoning is that problems are often posed as monolithic tasks, with no intermediate subgoals. In Raven Progressive Matrices (RPM), the task is to choose one of the available answers given a context, where both contexts and answers are composite images featuring multiple objects in various spatial arrangements. As this high-level goal is the only guidance available, learning is challenging and most contemporary solvers tend to be opaque. In this study, we propose a deep learning architecture based on the transformer blueprint which, rather than directly making the above choice, predicts the visual properties of individual objects and their arrangements. The multidimensional predictions obtained in this way are then directly juxtaposed to choose the answer. We consider a few ways in which the model parses the visual input into tokens and several regimes of masking parts of the input in self-supervised training. In experimental assessment, the models not only outperform state-of-the-art methods but also provide interesting insights and partial explanations about the inference. The design of the method also makes it immune to biases that are known to exist in some RPM benchmarks.Comment: 12 pages, 3 figure

Application of a Hybrid Algorithm Based on Quantum Annealing to Solve a Metropolitan Scale Railway Dispatching Problem

We address the applicability of quantum-classical hybrid solvers for practical railway dispatching/conflict management problems, with a demonstration on real-life metropolitan-scale network traffic. The railway network includes both single-and double segments and covers all the requirements posed by the operator of the network. We build a linear integer model for the problem and solve it with D-Wave's quantum-classical hybrid solver as well as with CPLEX for comparison. The computational results demonstrate the readiness for application and benefits of quantum-classical hybrid solvers in the a realistic railway scenario: they yield acceptable solutions on time; a critical requirement in a dispatching situation. Though they are heuristic they offer a valid alternative and outperform classical solvers in some cases

URBAN PUBLIC TRANSPORT WITH THE USE OF ELECTRIC BUSES – DEVELOPMENT TENDENCIES

Summary. The programing documents of the European Union determine the direction of transport systems development, including large cities and agglomerations. The context of these actions which aim to transform into ecologically clean and sustainable transport system is a significant reduction of greenhouse gas emissions. Assuming that public transport will significantly reduce the use of combustion-powered buses, studies on urban logistic enabling the use of electric buses for public transport are needed. The article presents the variants and scenarios for electric buses implementation in urban public transport, as well as the decision algorithm to support electric bus implementation based on technological, organisational, economic and ecological variables

Metaheuristic Design Patterns: New Perspectives for Larger-Scale Search Architectures

Design patterns capture the essentials of recurring best practice in an abstract form. Their merits are well established in domains as diverse as architecture and software development. They offer significant benefits, not least a common conceptual vocabulary for designers, enabling greater communication of high-level concerns and increased software reuse. Inspired by the success of software design patterns, this chapter seeks to promote the merits of a pattern-based method to the development of metaheuristic search software components. To achieve this, a catalog of patterns is presented, organized into the families of structural, behavioral, methodological and component-based patterns. As an alternative to the increasing specialization associated with individual metaheuristic search components, the authors encourage computer scientists to embrace the ‘cross cutting' benefits of a pattern-based perspective to optimization algorithms. Some ways in which the patterns might form the basis of further larger-scale metaheuristic component design automation are also discussed

Approximating geometric crossover in semantic space

We propose a crossover operator that works with genetic programming trees and is approximately geometric crossover in the semantic space. By defining semantic as program's evaluation profile with respect to a set of fitness cases and constraining to a specific class of metric-based fitness functions, we cause the fitness landscape in the semantic space to have perfect fitness-distance correlation. The proposed approximately geometric semantic crossover exploits this property of the semantic fitness landscape by an appropriate sampling. We demonstrate also how the proposed method may be conveniently combined with hill climbing. We discuss the properties of the methods, and describe an extensive computational experiment concerning logical function synthesis and symbolic regression