Un algoritmo meta-euristico per la progettazione di reti stradali urbane di grandi dimensioni

ItIn questa nota si propone un modello di ottimizzazione ed un algoritmo per la risoluzione del problema della progettazione delle reti stradali urbane. Tale problema consiste nell’ottimizzare la configurazione di una rete di trasporto urbana intervenendo solo sui sensi di marcia e sulle intersezioni, senza prevedere la possibilità di costruire nuove infrastrutture. Nella nota si formula un modello di ottimizzazione non lineare vincolata ed un algoritmo risolutivo basato sulla Scatter Search per la soluzione del problema. Modello ed algoritmo sono sperimentati su una rete di dimensione reale, fornendo tempi di calcolo ragionevoli nonostante la complessità del problema e la dimensione della rete test

Competitive percolation strategies for network recovery

Restoring operation of critical infrastructure systems after catastrophic events is an important issue, inspiring work in multiple fields, including network science, civil engineering, and operations research. We consider the problem of finding the optimal order of repairing elements in power grids and similar infrastructure. Most existing methods either only consider system network structure, potentially ignoring important features, or incorporate component level details leading to complex optimization problems with limited scalability. We aim to narrow the gap between the two approaches. Analyzing realistic recovery strategies, we identify over- and undersupply penalties of commodities as primary contributions to reconstruction cost, and we demonstrate traditional network science methods, which maximize the largest connected component, are cost inefficient. We propose a novel competitive percolation recovery model accounting for node demand and supply, and network structure. Our model well approximates realistic recovery strategies, suppressing growth of the largest connected component through a process analogous to explosive percolation. Using synthetic power grids, we investigate the effect of network characteristics on recovery process efficiency. We learn that high structural redundancy enables reduced total cost and faster recovery, however, requires more information at each recovery step. We also confirm that decentralized supply in networks generally benefits recovery efforts.Comment: 14 pages, 6 figure

A Data-driven Resilience Framework of Directionality Configuration based on Topological Credentials in Road Networks

Roadway reconfiguration is a crucial aspect of transportation planning, aiming to enhance traffic flow, reduce congestion, and improve overall road network performance with existing infrastructure and resources. This paper presents a novel roadway reconfiguration technique by integrating optimization based Brute Force search approach and decision support framework to rank various roadway configurations for better performance. The proposed framework incorporates a multi-criteria decision analysis (MCDA) approach, combining input from generated scenarios during the optimization process. By utilizing data from optimization, the model identifies total betweenness centrality (TBC), system travel time (STT), and total link traffic flow (TLTF) as the most influential decision variables. The developed framework leverages graph theory to model the transportation network topology and apply network science metrics as well as stochastic user equilibrium traffic assignment to assess the impact of each roadway configuration on the overall network performance. To rank the roadway configurations, the framework employs machine learning algorithms, such as ridge regression, to determine the optimal weights for each criterion (i.e., TBC, STT, TLTF). Moreover, the network-based analysis ensures that the selected configurations not only optimize individual roadway segments but also enhance system-level efficiency, which is particularly helpful as the increasing frequency and intensity of natural disasters and other disruptive events underscore the critical need for resilient transportation networks. By integrating multi-criteria decision analysis, machine learning, and network science metrics, the proposed framework would enable transportation planners to make informed and data-driven decisions, leading to more sustainable, efficient, and resilient roadway configurations.Comment: 103rd Transportation Research Board (TRB) Annual Meetin

A turning restriction design problem in urban road networks

Turning restriction is one of the commonest traffic management techniques and an effective low cost traffic improvement strategy in urban road networks. However, the literature has not paid much attention to the turning restriction design problem (TRDP), which aims to determine a set of intersections where turning restrictions should be implemented. In this paper, a bi-level programming model is proposed to formulate the TRDP. The upper level problem is to minimize the total travel cost from the viewpoint of traffic managers, and the lower level problem is to depict travelers' route choice behavior based on stochastic user equilibrium (SUE) theory. We propose a branch and bound method (BBM), based on the sensitivity analysis algorithm (SAA), to find the optimal turning restriction strategy. A branch strategy and a bound strategy are applied to accelerate the solution process of the TRDP. The computational experiments give promising results, showing that the optimal turning restriction strategy can obviously reduce system congestion and are robust to the variations of both the dispersion parameter of the SUE problem and the level of demand. © 2010 Elsevier B.V. All rights reserved.postprin

Mixed network design using hybrid scatter search

This research proposes a bi-level model for the mixed network design problem (MNDP). The upper level problem involves redesigning the current road links’ directions, expanding their capacity, and determining signal settings at intersections to optimize the reserve capacity of the whole system. The lower level problem is the user equilibrium traffic assignment problem. By proving that the optimal arc flow solution of the bi-level problem must exist in the boundary of capacity constraints, an exact line search method called golden section search is embedded in a scatter search method for solving this complicated MNDP. The algorithm is then applied to some real cases and finally, some conclusions are drawn on the model's efficiency.postprin

Analyzing the Performance of a Hybrid Heuristic for Solving a Bilevel Location Problem under Different Approaches to Tackle the Lower Level

The problem addressed here is a combinatorial bilevel programming problem called the uncapacitated facility location problem with customer’s preferences. A hybrid algorithm is developed for solving a battery of benchmark instances. The algorithm hybridizes an evolutionary algorithm with path relinking; the latter procedure is added into the crossover phase for exploring the trajectory between both parents. The proposed algorithm outperforms the evolutionary algorithm already existing in the literature. Results show that including a more sophisticated procedure for improving the population through the generations accelerates the convergence of the algorithm. In order to support the latter statement, a reduction of around the half of the computational time is obtained by using the hybrid algorithm. Moreover, due to the nature of bilevel problems, if feasible solutions are desired, then the lower level must be solved for each change in the upper level’s current solution. A study for illustrating the impact in the algorithm’s performance when solving the lower level through three different exact or heuristic approaches is made

Some metaheuristics for the urban network design problem

Orientadores: Rodrigo Silva Lima, Celso CavellucciDissertação (mestrado profissional) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: Neste projeto propomos um modelo de otimização para o design da malha urbana através da utilização das meta-heurísticas GRASP e Algoritmo Genético, implementados no software Mathematica. A otimização utiliza ruas já existentes no espaço de pesquisa e visa estudar a melhor orientação para as mesmas. A proposta de utilização de meta-heurísticas dá-se em função da complexidade do problema, bem como a dificuldade para obtermos a melhor solução de forma determinística. O problema de design da malha urbana foi analisado de forma estocástica, a partir da distribuição de probabilidade Logit Multinomial. A escolha das meta-heurísticas foi baseada na praticidade de implementação e por serem bem difundidas na literatura. Comparamos os resultados de uma instância, com variação dos parâmetros, de modo a verificar o processo mais eficiente e estudar o melhor ajuste dos mesmos para o problema em questãoAbstract: In this project, we propose an optimization model for the urban network design using the metaheuristics GRASP and Genetic Algorithm, implemented in the software Mathematica. The optimization uses streets that already exists on the research space and seeks to study the best orientation for them. The proposal of metaheuristics use is due to the complexity of the problem, as well as the difficulty to obtain the best solution deterministically. The urban network design problem was studied in a stochastic manner, using a Multinomial Logit distribution. The choice of the metaheuristics was based on the simplicity to implement, and for being well known in the literature. We compared the results of the instance, with variation of the parameters, so we can verify the most efficient process and study the best fit of them, for the problem in handMestradoMatematica Aplicada e ComputacionalMestre em Matemática Aplicada e Computaciona