68 results found
Anastasiadis E, Angeloudis P, Ainalis D, et al., 2021, On the selection of charging facility locations for EV-based ride-hailing services: a computational case study, Sustainability, Vol: 13, ISSN: 2071-1050
The uptake of Electric Vehicles (EVs) is rapidly changing the landscape of urban mobility services. Transportation Network Companies (TNCs) have been following this trend by increasing the number of EVs in their fleets. Recently, major TNCs have explored the prospect of establishing privately owned charging facilities that will enable faster and more economic charging. Given the scale and complexity of TNC operations, such decisions need to consider both the requirements of TNCs and local planning regulations. Therefore, an optimisation approach is presented to model the placement of CSs with the objective of minimising the empty time travelled to the nearest CS for recharging as well as the installation cost. An agent based simulation model has been set in the area of Chicago to derive the recharging spots of the TNC vehicles, and in turn derive the charging demand. A mathematical formulation for the resulting optimisation problem is provided alongside a genetic algorithm that can produce solutions for large problem instances. Our results refer to a representative set of the total data for Chicago and indicate that nearly 180 CSs need to be installed to handle the demand of a TNC fleet of 3000 vehicles.
Whyte J, Mijic A, Myers RJ, et al., 2020, A research agenda on systems approaches to infrastructure, Journal of Civil Engineering and Environmental Systems, Vol: 37, Pages: 214-233, ISSN: 1029-0249
At a time of system shocks, significant underlying challenges are revealed in current approaches to delivering infrastructure, including that infrastructure users in many societies feel distant from nature. We set out a research agenda on systems approaches to infrastructure, drawing on ten years of interdisciplinary work on operating infrastructure, infrastructure interventions and lifecycles. Research insights and directions on complexity, systems integration, data-driven systems engineering, infrastructure life-cycles, and the transition towards zero pollution are summarised. This work identifies a need to better understand the natural and societal impacts of infrastructure interventions under uncertainty. We argue for a change in current approaches to infrastructure: starting from the natural environment and its resources, encompassing societal use of infrastructure and the supporting infrastructure assets and services. To support such proposed new systems approaches to infrastructure, researchers need to develop novel modelling methods, forms of model integration, and multi-criteria indicators.
Escribano Macias J, Goldbeck N, Hsu P-Y, et al., 2020, Endogenous stochastic optimisation for relief distribution assisted with unmanned aerial vehicles, OR SPECTRUM, Vol: 42, Pages: 1089-1125, ISSN: 0171-6468
Karamanis R, Anastasiadis E, Stettler M, et al., 2020, Vehicle redistribution in ride-sourcing markets using convex minimum cost flows, Publisher: arXiv
Ride-sourcing platforms often face imbalances in the demand and supply ofrides across areas in their operating road-networks. As such, dynamic pricingmethods have been used to mediate these demand asymmetries through surge pricemultipliers, thus incentivising higher driver participation in the market.However, the anticipated commercialisation of autonomous vehicles couldtransform the current ride-sourcing platforms to fleet operators. The absenceof human drivers fosters the need for empty vehicle management to address anyvehicle supply deficiencies. Proactive redistribution using integer programmingand demand predictive models have been proposed in research to address thisproblem. A shortcoming of existing models, however, is that they ignore themarket structure and underlying customer choice behaviour. As such, currentmodels do not capture the real value of redistribution. To resolve this, weformulate the vehicle redistribution problem as a non-linear minimum cost flowproblem which accounts for the relationship of supply and demand of rides, byassuming a customer discrete choice model and a market structure. Wedemonstrate that this model can have a convex domain, and we introduce an edgesplitting algorithm to solve a transformed convex minimum cost flow problem forvehicle redistribution. By testing our model using simulation, we show that ourredistribution algorithm can decrease wait times up to 50% and increase vehicleutilization up to 8%. Our findings outline that the value of redistribution iscontingent on localised market structure and customer behaviour.
Hsu P-Y, Aurisicchio M, Angeloudis P, et al., 2020, Understanding and visualizing schedule deviations in construction projects using fault tree analysis, Engineering, Construction and Architectural Management, Vol: 27, Pages: 2501-2522, ISSN: 0969-9988
Delays in construction projects are both disruptive and expensive. Thus, potential causes of schedule deviation need to be identified and mitigated. In previous research, delay factors were predominantly identified through surveys administered to stakeholders in construction projects. Such delay factors are typically considered individually and presented at the same level without explicitly examining their sequence of occurrence and inter-relationships. In reality, owing to the complex structure of construction projects and long execution time, non-conformance to schedule occurs by a chain of cascading events. An understanding of these linkages is important not only for minimising the delays but also for revealing the liability of stakeholders. To explicitly illustrate the cause–effect and logical relationship between delay factors and further identify the primary factors which possess the highest significance toward the overall project schedule delay, the fault tree analysis (FTA) method, a widely implemented approach to root cause problems in safety-critical systems, has been systematically and rigorously executed.
Karamanis R, Anastasiadis E, Angeloudis P, et al., 2020, Assignment and pricing of shared rides in ride-sourcing using combinatorial double auctions, IEEE Transactions on Intelligent Transportation Systems, Pages: 1-12, ISSN: 1524-9050
Transportation Network Companies employ dynamic pricing methods at periods of peak travel to incentivise driver participation and balance supply and demand for rides. Surge pricing multipliers are commonly used and are applied following demand and estimates of customer and driver trip valuations. Combinatorial double auctions have been identified as a suitable alternative, as they can achieve maximum social welfare in the allocation by relying on customers and drivers stating their valuations. A shortcoming of current models, however, is that they fail to account for the effects of trip detours that take place in shared trips and their impact on the accuracy of pricing estimates. To resolve this, we formulate a new shared-ride assignment and pricing algorithm using combinatorial double auctions. We demonstrate that this model is reduced to a maximum weighted independent set model, which is known to be APX-hard. A fast local search heuristic is also presented, which is capable of producing results that lie within 10% of the exact approach for practical implementations. Our proposed algorithm could be used as a fast and reliable assignment and pricing mechanism of ride-sharing requests to vehicles during peak travel times.
Yu J, Stettler MEJ, Angeloudis P, et al., 2020, Urban network-wide traffic speed estimation with massive ride-sourcing GPS traces, Transportation Research Part C: Emerging Technologies, Vol: 112, Pages: 136-152, ISSN: 0968-090X
The ability to obtain accurate estimates of city-wide urban traffic patterns is essential for the development of effective intelligent transportation systems and the efficient operation of smart mobility platforms. This paper focuses on the network-wide traffic speed estimation, using trajectory data generated by a city-wide fleet of ride-sourcing vehicles equipped with GPS-capable smartphones. A cell-based map-matching technique is proposed to link vehicle trajectories with road geometries, and to produce network-wide spatio-temporal speed matrices. Data limitations are addressed using the Schatten p-norm matrix completion algorithm, which can minimize speed estimation errors even with high rates of data unavailability. A case study using data from Chengdu, China, demonstrates that the algorithm performs well even in situations involving continuous data loss over a few hours, and consequently, addresses large-scale network-wide traffic state estimation problems with missing data, while at the same time outperforming other data recovery techniques that were used as benchmarks. Our approach can be used to generate congestion maps that can help monitor and visualize traffic dynamics across the network, and therefore form the basis for new traffic management, proactive congestion identification, and congestion mitigation strategies.
Escribano-Macias JJ, Angeloudis P, Han K, 2020, Optimal design of Rapid evacuation strategies in constrained urban transport networks, Transportmetrica A: Transport Science, Vol: 16, Pages: 1079-1110, ISSN: 2324-9935
Large-scale evacuations constitute common life-saving exercises that are activated in many disaster response campaigns. Their effectiveness is often inhibited by traffic congestion, disrupted and imperfect coordination mechanisms, and the poor state of the underlying transportation networks. To address this problem, this paper presents a hybrid simulation-optimisation methodology to optimise evacuation response strategies through demand staging and signal phasing. We introduce a pre-planning model that evaluates evacuation policies, using a low-level dynamic traffic assignment model that captures the effects of congestion, queuing and vehicle spillback. Optimal strategies are determined using derivative-free optimisation algorithms, applied to an evacuation problem based on a benchmark dataset. The effects of varying the number of activated paths and the frequency of departure under different network conditions are observed. Our analysis indicates that combined departure time scheduling and signal phasing is a promising method to improve evacuation efficiency when compared to a worst-case benchmark scenario.
Chow AHF, Kuo YH, Angeloudis P, et al., 2020, Dynamic modelling and optimisation of transportation systems in the connected era, Transportmetrica B, ISSN: 2168-0566
Escribano Macias J, Angeloudis P, Ochieng W, 2020, Optimal hub selection for rapid medical deliveries using unmanned aerial vehicles, Transportation Research Part C: Emerging Technologies, Vol: 110, Pages: 56-80, ISSN: 0968-090X
Unmanned Aerial Vehicles (UAVs) are being increasingly deployed in humanitarian response operations. Beyond regulations, vehicle range and integration with the humanitarian supply chain inhibit their deployment. To address these issues, we present a novel bi-stage operational planning approach that consists of a trajectory optimisation algorithm (that considers multiple flight stages), and a hub selection-routing algorithm that incorporates a new battery management heuristic. We apply the algorithm to a hypothetical response mission in Taiwan after the Chi-Chi earthquake of 1999 considering mission duration and distribution fairness. Our analysis indicates that UAV fleets can be used to provide rapid relief to populations of 20,000 individuals in under 24 h. Additionally, the proposed methodology achieves significant reductions in mission duration and battery stock requirements with respect to conservative energy estimations and other heuristics.
Goldbeck N, Angeloudis P, Ochieng W, 2020, Optimal supply chain resilience with consideration of failure propagation and repair logistics, Transportation Research Part E: Logistics and Transportation Review, Vol: 133, Pages: 1-20, ISSN: 1366-5545
The joint optimisation of investments in capacity and repair capability of production and logistics systems at risk of being damaged is an important aspect of supply chain resilience that is not sufficiently addressed by state-of-the-art modelling approaches. Furthermore, logistical issues of procuring repair resources impact speed of recovery but are not considered in most existing models. This paper presents a novel multi-stage stochastic programming model that optimizes pre-disruption investment decisions, as well as post-disruption dynamic adjustment of supply chain operations and allocation of repair resources. A case study demonstrates how the method can quantify the effects of pooling repair resources.
Hsu P-Y, Aurisicchio M, Angeloudis P, 2019, Risk-averse supply chain for modular construction projects, Automation in Construction, Vol: 106, Pages: 1-12, ISSN: 0926-5805
The traditional in-situ construction method is currently being replaced by modular building systems, that take advantage of modern manufacturing, transportation, and assembly methods. This transformation poses a challenge to construction supply chains, which have, thus far, been concentrated on raw material transportation only. A mathematical model is conceived in this study for the design and optimisation of risk-averse logistics configurations for modular construction projects under operational uncertainty. The model considers the manufacturing, storage, and assembly stages, along with the selection of optimal warehouse locations. Using robust optimisation, the model accounts for common causes of schedule deviations in construction sites, including inclement weather, late deliveries, labour productivity fluctuations and crane malfunctions. A school dormitory construction project is used as a case study, demonstrating that the proposed model outperforms existing techniques in settings with multiple sources of uncertainty.
Goldbeck N, Angeloudis P, Ochieng WY, 2019, Resilience assessment for interdependent urban infrastructure systems using dynamic network flow models, Reliability Engineering and System Safety, Vol: 188, Pages: 62-79, ISSN: 0951-8320
Critical infrastructure systems are becoming increasingly interdependent, which can exacerbate the impacts of disruptive events through cascading failures, hindered asset repairs and network congestion. Current resilience assessment methods fall short of fully capturing such interdependency effects as they tend to model asset reliability and network flows separately and often rely on static flow assignment methods. In this paper, we develop an integrated, dynamic modelling and simulation framework that combines network and asset representations of infrastructure systems and models the optimal response to disruptions using a rolling planning horizon. The framework considers dependencies pertaining to failure propagation, system-of-systems architecture and resources required for operating and repairing assets. Stochastic asset failure is captured by a scenario tree generation algorithm whereas the redistribution of network flows and the optimal deployment of repair resources are modelled using a minimum cost flow approach. A case study on London's metro and electric power networks shows how the proposed methodology can be used to assess the resilience of city-scale infrastructure systems to a local flooding incident and estimate the value of the resilience loss triangle for different levels of hazard exposure and repair capabilities.
Achurra-Gonzalez PE, Angeloudis P, Goldbeck N, et al., 2019, Evaluation of port disruption impacts in the global liner shipping network, Journal of Shipping and Trade, Vol: 4, Pages: 1-21, ISSN: 2364-4575
The global container shipping network is vital to international trade. Current techniques for its vulnerability assessment are constrained due to the lack of historical disruption data and computational limitations due to typical network sizes. We address these modelling challenges by developing a new framework, composed by a game-theoretic attacker-defender model and a cost-based container assignment model that can identify systemic vulnerabilities in the network. Given its focus on logic and structure, the proposed framework has minimal input data requirements and does not rely on the presence of extensive historical disruption data. Numerical implementations are carried in a global-scale liner network where disruptions occur in Europe’s main container ports. Model outputs are used to establish performance baselines for the network and illus-trate the differences in regional vulnerability levels and port criticality rankings with different disruption magnitudes and flow diversion strategies. Sensitivity analysis of these outputs identifies network compo-nents that are more susceptible to lower levels of disruption which are more common in practice and to assess the effectiveness of component-level interventions seeking to increase the resilience of the system.
Achurra-Gonzalez PE, Novati M, Foulser-Piggott R, et al., 2019, Modelling the impact of liner shipping network perturbations on container cargo routing: Southeast Asia to Europe application, Accident Analysis & Prevention, Vol: 123, Pages: 399-410
Understanding how container routing stands to be impacted by different scenarios of liner shipping network perturbations such as natural disasters or new major infrastructure developments is of key importance for decision-making in the liner shipping industry. The variety of actors and processes within modern supply chains and the complexity of their relationships have previously led to the development of simulation-based models, whose application has been largely compromised by their dependency on extensive and often confidential sets of data. This study proposes the application of optimisation techniques less dependent on complex data sets in order to develop a quantitative framework to assess the impacts of disruptive events on liner shipping networks. We provide a categorization of liner network perturbations, differentiating between systemic and external and formulate a container assignment model that minimises routing costs extending previous implementations to allow feasible solutions when routing capacity is reduced below transport demand. We develop a base case network for the Southeast Asia to Europe liner shipping trade and review of accidents related to port disruptions for two scenarios of seismic and political conflict hazards. Numerical results identify alternative routing paths and costs in the aftermath of port disruptions scenarios and suggest higher vulnerability of intra-regional connectivity.
Liu Q, Hu S, Angeloudis P, et al., 2019, Simulation and Evaluation of CAVs Behavior in an Isolated Signalized Intersection Equipped with Dynamic Wireless Power Transfer System, IEEE Intelligent Transportation Systems Conference (IEEE-ITSC), Publisher: IEEE, Pages: 2207-2212, ISSN: 2153-0009
Hsu P-Y, Angeloudis P, Aurisicchio M, 2018, Optimal logistics planning for modular construction using two-stage stochastic programming, AUTOMATION IN CONSTRUCTION, Vol: 94, Pages: 47-61, ISSN: 0926-5805
Haughton TW, Angeloudis P, Parpas P, et al., 2018, Optimal Component Modularisation of Process Plants for Modular Construction, EURO 2018
Ainalis D, Achurra-Gonzalez P, Gaudin A, et al., 2018, Ultra-Capacitor based kinetic energy recovery system for heavy goods vehicles, 15th International Symposium on Heavy Vehicle Transport Technology, Publisher: International Forum for Heavy Vehicle Transport & Technology
The Climate Change Act 2008 commits the UK to reduce the Greenhouse Gas emissions by 80% by 2050 relative to 1990 levels. While Heavy Goods Vehicles and buses contribute about 4% of the total Greenhouse Gas emissions in the UK, these emissions only decrease by 10% between 1990 and 2015. Urban areas are particularly susceptible to emissions and can have a significant impact upon the health of residents. For Heavy Goods Vehicles, braking losses are one of the most significant losses. A Kinetic Energy Recovery System can help reduce these emissions, and increase fuel efficiency by up to 30 %. This paper describes an InnovateUK funded project aimed at evaluating the technical and economic feasibility of a retrofitted Kinetic Energy Recovery System on Heavy Goods Vehicles through an operational trial, controlled emissions and fuel tests, and numerical modelling. A series of preliminary results using a numerical vehicle model is compared with operational data, along with simulations comparing the fuel efficiency of a Heavy Goods Vehicle with and without the KERS.
Karamanis R, Angeloudis P, Sivakumar A, et al., 2018, Market dynamics between public transport and competitive ride-sourcing providers, 7th Symposium of the European Association for Research in Transportation, Publisher: hEART
Escribano Macias J, Angeloudis P, Ochieng W, 2018, AIAA Integrated Trajectory-Location-Routing for Rapid Humanitarian Deliveries using Unmanned Aerial Vehicles, 2018 Aviation Technology, Integration, and Operations Conference
Achurra Gonzalez PE, Angeloudis P, Zavitsas K, et al., 2017, Attacker-defender modelling of vulnerability in maritime logistics corridors, Advances in Shipping Data Analysis and Modeling: Tracking and Mapping Maritime Flows in the Age of Big Data, Editors: Ducruet, ISBN: 9781351985093
Hsu P-Y, Aurisicchio M, Angeloudis P, 2017, Supply chain design for modular construction projects, 25th Annual Conference of the International Group for Lean Construction (IGLC), Publisher: IGLC, ISSN: 2309-0979
The construction sector is currently undergoing a shift from stick-built construction techniques to modular building systems. If construction supply chains are to support this transformation, they need to be modified and strengthened using an adapted logistics system. The aim of this study is to establish a mathematical model for the logistics of modular construction covering the three common tiers of operations: manufacturing, storage and construction. Previous studies have indicated that construction site delays constitute the largest cause of schedule deviations. Using the model outlined in this paper we seek to determine how factory manufacturing and inventory management should be adapted to variations in demand on the construction site. We propose a Mixed Integer Linear Programming model that captures construction scenarios with demands for modular products that are either foreseeable or abruptly disrupted. The use of the model is illustrated through a case study of bathroom pods for a building project. The model outputs include supply chain configurations that reduce total costs across a range of scenarios. The model could serve as a decision support tool for modular construction logistics.
Nikhalat-Jahromi H, Angeloudis P, Bell MGH, et al., 2017, Global LNG trade: A comprehensive up to date analysis, Maritime Economics & Logistics, Vol: 19, Pages: 160-181
Karamanis R, Niknejad A, Angeloudis P, 2017, A Fleet Sizing Algorithm for Autonomous Car Sharing, Transportation Research Board 96th Annual Meeting
Goldbeck N, Angeloudis P, 2017, Civil Engineering: Unlocking the potential of future cities through sustainable and resilient infrastructure, Defining the Urban: Interdisciplinary and Professional Perspectives, Editors: Iossifova, Gasparatos, Doll, Publisher: Routledge, ISBN: 978-1472449498
Anvari B, Angeloudis P, Ochieng WY, 2016, A multi-objective GA-based optimisation for holistic Manufacturing, transportation and Assembly of precast construction, Automation in Construction, Vol: 71, Pages: 226-241, ISSN: 0926-5805
Resource scheduling of construction proposals allows project managers to assess resource requirements, provide costs and analyse potential delays. The Manufacturing, transportation and Assembly (MtA) sectors of precast construction projects are strongly linked, but considered separately during the scheduling phase. However, it is important to evaluate the cost and time impacts of consequential decisions from manufacturing up to assembly. In this paper, a multi-objective Genetic Algorithm-based (GA-based) searching technique is proposed to solve unified MtA resource scheduling problems (which are equivalent to extended Flexible Job Shop Scheduling Problems). To the best of the authors' knowledge, this is the first time that a GA-based optimisation approach is applied to a holistic MtA problem with the aim of minimising time and cost while maximising safety. The model is evaluated and compared to other exact and non-exact models using instances from the literature and scenarios inspired from real precast constructions.
Nikhalat-Jahromi H, Bell MGH, Fontes DBMM, et al., 2016, Spot sale of uncommitted LNG from Middle East: Japan or the UK?, Energy Policy, Vol: 96, Pages: 717-725
Shang W, Han K, Ochieng W, et al., 2016, Agent-based day-to-day traffic network model with information percolation, Transportmetrica A-Transport Science, Vol: 13, Pages: 38-66, ISSN: 2324-9935
This paper explores the impact of travel information sharing on road networks using a two-layer, agent-based, day-to-day traffic network model. The first layer (cyber layer) represents a conceptual communication network where travel information is shared among drivers. The second layer (physical layer) captures the day-to-day evolution in a traffic network where individual drivers seek to minimize their own travel costs by making route choices. A key hypothesis in this model is that instead of having perfect information, the drivers form individual groups, among which travel information is shared and utilized for routing decisions. The formation of groups occurs in the cyber layer according to the notion of percolation, which describes the formation of connected clusters (groups) in a random graph. We apply the novel notion of percolation to capture the disaggregated and distributed nature of travel information sharing. We present a numerical study on the convergence of the transport network, when a range of percolation rates are considered. The findings suggest a positive correlation between the percolation rate and the speed of convergence, which is validated through statistical analysis. A sensitivity analysis is also presented which shows a bifurcation phenomenon with regard to certain model parameters.
Angeloudis P, Greco L, Bell MGH, 2016, Strategic maritime container service design in oligopolistic markets, Transportation Research Part B: Methodological: an international journal, Vol: 90, Pages: 22-37, ISSN: 0191-2615
This paper considers the maritime container assignment problem in a market setting with two competing firms. Given a series of known, exogenous demands for service between pairs of ports, each company is free to design liner services connecting a subset of the ports and demand, subject to the size of their fleets and the potential for profit. The model is designed as a three-stage complete information game: in the first stage, the firms simultaneously invest in their fleet; in the second stage, they individually design their services and solve the route assignment problem with respect to the transport demand they expect to serve, given the fleet determined in the first stage; in the final stage, the firms compete in terms of freight rates on each origin–destination movement. The game is solved by backward induction. Numerical solutions are provided to characterize the equilibria of the game.
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