Alessio Trivella

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Trivella

First Name

Alessio

ORCID

0000-0002-2614-5051

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Publications1 - 10 of 48

Improved dual reoptimization policies and bounds for energy real option problems with irreversible decisions
Trivella, Alessio; Nadarajah, Selvaprabu; Corman, Francesco (2020)
Real options models with irreversible decisions are used to manage energy investments and operations but optimizing their flexibility under realistic state-variable dynamics is typically intractable. Least squares Monte Carlo (LSM) is popular for such problems while a known forecast-based reoptimization heuristic is not well suited to handle irreversible decisions. We develop a dual reoptimization framework that overcomes this issue and provides a new policy and a dual bound. We show that dual reoptimization outperforms forecast-based reoptimization and LSM on energy production and fleet upgrade applications.
Modeling uncertainty dynamics in public transport optimization
Trivella, Alessio; Corman, Francesco (2019)
Public transport networks such as bus and railway networks are highly complex systems. In fact, multiple sources of uncertainty including fluctuating passenger demand, variable road and traffic conditions, weather, and technical failures affect the network performance and reliability. These uncontrollable, stochastic factors follow intricate dynamics in space and time that makes it difficult to incorporate them into important decision-making processes of traffic management. For example, understanding how delays evolve (fade out absorbed by available buffer times,remain the same, or propagate through the network) is critical to undertake correct rescheduling actions for vehicles in the presence of delays or disruptions. Moreover, the number of stochastic factors is usually very large due to the many moving units or network links, which poses further modeling challenges. Goal of this paper is twofold. First, we review the existing stochastic models of the uncertainty employed in the public transport optimization literature, underlying their merits and shortcomings. Second, we define a roadmap for modeling high-dimensional uncertainties in public transport networks in a sound manner, with the goal of incorporating this uncertainty into stochastic optimization approaches.
Train Trajectory Optimization with Consideration of Parametric Uncertainty
Trivella, Alessio (2019)
Online Program: 17th Swiss Operations Research Days
Multi-scale design optimization of modern offshore wind farms
Cazzaro, Davide; Trivella, Alessio; Corman, Francesco; et al. (2021)
SSRN
The traditional optimization of the layout of a wind farm consisted in arranging the wind turbines inside a designated area. In contrast, the 2021 tender from the UK government, Offshore Wind Leasing Round 4 ("UK Round-4") and upcoming bids only specify large regions where the wind farm can be built. This leads to the new challenge to select the shape and area of the wind farm, out of a larger region, to maximize its profitability. We introduce this problem as the "wind farm area selection problem" and present a novel optimization framework to efficiently solve it. Specifically, our framework combines three scales of design: (i) on a macro-scale, choosing the approximate location of the wind farm out of larger regions, (ii) on a meso-scale, generating the optimal shape of the wind farm, and (iii) on a micro-scale, choosing the exact position of the turbines within the shape. In particular, we propose a new constructive heuristic to choose the best shape of a wind farm at the meso-scale, which is scarcely studied in literature. Moreover, while macro and micro-scales have already been investigated, our framework is the first to integrate them. We perform a detailed computational analysis using real-life data and constraints from the recent UK Round-4 tender. Compared to the best rectangular-shaped wind farm at the same location, our results show that optimizing the shape increases profitability by 1.2% on average and up to 2.9%, corresponding to 50 and 112 million Euro respectively.
A review of real-time railway and metro rescheduling models using learning algorithms
Jusup, Matej; Trivella, Alessio; Corman, Francesco (2021)
Planning railway and metro systems includes the critical step of finding a schedule for the trains. Although buffer times and running supplements are added to the schedule to make operations re- silient to minor disturbances, they do not protect against all possible events that may lead to con- flicts during everyday operations. Thus, real-time train rescheduling models are needed to restore fea- sibility using actions such as retiming, reordering, rerouting, overtaking or cancelling of trains. Un- fortunately, despite many rescheduling models that have been developed in the literature, only a few can learn actions from past, simulated, or ongoing events and cope with disturbances and disruptions’ stochastic nature. However, the last decade’s ex- pansion of learning algorithms is gaining momen- tum in the train rescheduling literature by bring- ing novel and promising ideas. This paper aims to review the state-of-the-art learning algorithms ap- plied to the real-time railway and metro reschedul- ing, identifying challenges and opportunities while making a parallel with other areas where learning algorithms led to breakthroughs.
Enhancing the interaction of railway timetabling and line planning with infrastructure awareness
Fuchs, Florian; Trivella, Alessio; Corman, Francesco (2021)
SSRN
Planning a railway system is done in multiple stages that are typically intractable to optimize in an integrated manner. This work develops a novel iterative approach to tackle two of these stages jointly: line planning and timetabling. Compared to existing approaches that iteratively ban a whole conflicting line plan when the timetable is found infeasible, our method can accurately identify the smallest set of incompatible services. Besides, by efficiently exploiting the available railway infrastructure, our method accounts for all the possible routing options of trains, a feature commonly neglected to reduce complexity but that helps gaining timetable feasibility. Using real data from a railway company in Switzerland, we find that our approach is (i) practical for solving real-life instances, (ii) an order of magnitude faster than existing benchmarks, and (iii) able to solve more instances. Our insights shed light on the necessity of considering infrastructure and banning conflicts rather than line plans in the joint line planning and timetabling problem.
A stochastic programming approach for scheduling extra metro trains to serve passengers from uncertain delayed high-speed railway trains
Long, Sihui; Meng, Lingyun; Luan, Xiaojie; et al. (2020)
Journal of Advanced Transportation
The metro system is an important component of the urban transportation system due to the large volume of transported passengers. Hub stations connecting metro and high-speed railway (HSR) networks are particularly critical in this system. When HSR trains are delayed due to a disruption on the HSR network, passengers of these trains arriving at the hub station at night may fail to get their last metro connection. The metro operator can thus decide to schedule extra metro trains at night to serve passengers from delayed HSR trains. In this paper, we consider the extra metro train scheduling problem in which the metro operator decides how many extra metro trains to dispatch and their schedules. The problem is complex because (i) the arrival of delayed HSR trains is usually uncertain, and (ii) the operator has to minimize operating costs (i.e., number of additional trains and operation-ending time) but maximize the number of served passengers, which are two conflicting objectives. In other words, the problem we consider is stochastic and biobjective. We formulate this problem as a two-stage stochastic program with recourse and use an epsilon-constrained method to find a set of nondominated solutions. We perform extensive numerical experiments using realistic instances based on the Beijing metro network and two HSR lines connected to this network. We find that our stochastic model outperforms out-of-sample a deterministic model that relies on forecasts of the delay by a range of 3–5%. Moreover, we show that our solutions are nearly optimal by computing a perfect information dual bound and obtaining average optimality gaps below 1%.
Train trajectory optimization for improved on-time arrival under parametric uncertainty
Wang, Pengling; Trivella, Alessio; Goverde, Rob M.P.; et al. (2020)
Transportation Research Part C: Emerging Technologies
In this paper we study the problem of computing train trajectories in an uncertain environment in which the values of some system parameters are difficult to determine. Specifically, we consider uncertainty in traction force and train resistance, and their impact on travel time and energy consumption. Our ultimate goal is to be able to control trains such that they will arrive on-time, i.e. within the planned running time, regardless of uncertain factors affecting their dynamic or kinematic performance. We formulate the problem as a Markov decision process and solve it using a novel numerical approach which combines: (i) an off-line approximate dynamic programming (ADP) method to learn the energy and time costs over iterations, and (ii) an on-line search process to determine energy-efficient driving strategies that respect the real-time time windows, more in general expressed as train path envelope constraints. To evaluate the performance of our approach, we conducted a numerical study using real-life railway infrastructure and train data. Compared to a set of benchmark driving strategies, the trajectories from our ADP-based method reduce the probability of delayed arrival, and at the same time are able to better use the available running time for energy saving. Our results show that accounting for uncertainty is relevant when computing train trajectories and that our ADP-based method can handle this uncertainty effectively.
An analysis of power peaks in stochastic models of railway traffic
Trivella, Alessio; Corman, Francesco (2022)
Abstract Book: 10th Symposium of the European Association for Research in Transport (hEART 2022)
Railway traffic flow can be modeled by a string of consecutive trains, each subject to random speed variations that are described by a stochastic process. Despite analogies with car-follower models, railways include specific features and a safety system that forces vehicles to decelerate towards a fixed lower speed if an absolute safety distance with the vehicle ahead is not respected. By simulating this dynamic system, we compute performance indicators focusing on energy consumption and the power peaks arising when multiple trains accelerate simultaneously. We study how different conditions of the system and assumptions on the stochastic processes, e.g., describing human drivers vs automated train operations (ATO), affect energy consumption and power peaks. Our results show that an ATO controller aware of precise distance and speed information can be effective at reducing energy consumption and smoothing the peaks, which are a major concern of operators.
The impact of socioeconomic and behavioural factors for purchasing energy efficient household appliances: A case study for Denmark
Baldini, Mattia; Trivella, Alessio; Wente, Jordan William (2018)
Energy Policy

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Alessio Trivella

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