Alessio Trivella

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Trivella

First Name

Alessio

ORCID

0000-0002-2614-5051

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Publications 1 - 10 of 48

Investigation of consumer's behaviour towards investments in household energy efficient appliances
Baldini, Mattia; Trivella, Alessio; Wente, Jordan W. (2018)
Proceedings of the 9th international conference on energy efficiency in domestic appliances and lighting (EEDAL '17)
Modeling soft unloading constraints in the multi-drop container loading problem
Bonet Filella, Guillem; Trivella, Alessio; Corman, Francesco (2021)
SSRN
The multi-drop container loading problem (MDCLP) requires loading a truck so that boxes can be unloaded at each drop-off point without rearranging other boxes to deliver later. However, modeling such unloading constraints as hard constraints, as done in the literature, considerably limits the flexibility to optimize the packing and utilize the vehicle capacity. We thus propose a more general approach that considers soft unloading constraints. Specifically, we penalize unnecessary relocations of boxes using penalty functions that depend on the volume and weight of the boxes to move as well as the type of move. Our goal is to maximize the total value of the loaded cargo including penalty functions due to violations of the unloading constraints. We provide a mixed-integer linear programming formulation for the MDCLP with soft unloading constraints, which can solve to optimality small scale instances but is intractable for larger ones. We thus propose a heuristic framework to solve large instances, which is based on a randomized extreme-point constructive phase and a subsequent improvement phase. The latter phase iteratively destroys regions in the packing space where high penalties originate, and reconstructs them. Extensive numerical experiments involving different penalties show that our approach significantly outperforms: (i) the hard unloading constraints approach, and (ii) a sequential heuristic that neglects unloading constraints first and evaluates the penalties afterwards. Our findings underscore the relevance of accounting for soft unloading constraints in the MDCLP.
Sustainable and efficient logistics
Trivella, Alessio; Gajda, Mikele; Giannotti, Paolo; et al. (2021)
Enhancing the interaction of railway timetabling and line planning with infrastructure awareness
Trivella, Alessio; Fuchs, Florian; Corman, Francesco (2021)
Train trajectory optimization in the presence ofexternal factors: The example of wind
Trivella, Alessio; Wang, Pengling; Corman, Francesco (2019)
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.
Socially responsible merchant operations: Comparison of shutdown-averse CVaR and anticipated regret policies
Trivella, Alessio; Nadarajah, Selvaprabu (2021)
Operations Research Letters
Commodity and energy production assets are managed as real options on market uncertainties. Social impacts of plant shutdowns incentivize balancing asset value with shutdown probability. We propose new shutdown-averse policies based on the popular dynamic conditional value-at-risk (CVaR). We analytically and numerically compare these policies to known shutdown-averse policies based on anticipated regret (AR). Our findings support the use of AR over CVaR to embed shutdown-aversion and the consideration of hybrid policies that are asymptotically time-consistent but easily interpretable.
The impact of wind on energy-efficient train control
Trivella, Alessio; Wang, Pengling; Corman, Francesco (2020)
EURO Journal on Transportation and Logistics
An energy-efficient train trajectory corresponds to the speed profile of a train between two stations that minimizes energy consumption while respecting the scheduled arrival time and operational constraints such as speed limits. Determining this trajectory is a well-known problem in the operations research and transport literature, but has so far been studied without accounting for stochastic variables like weather conditions or train load that in reality vary in each journey. These variables have an impact on the train resistance, which in turn affects the energy consumption. In this paper, we focus on wind variability and propose a train resistance equation that accounts for the impact of wind speed and direction explicitly on the train motion. Based on this equation, we compute the energy-efficient speed profile that exploits the knowledge of wind available before train departure, i.e., wind measurements and forecasts. Specifically, we: (i) construct a distance-speed network that relies on a new non-linear discretization of speed values and embeds the physical train motion relations updated with the wind data, and (ii) compute the energy-efficient trajectory by combining a line-search framework with a dynamic programming shortest path algorithm. Extensive numerical experiments reveal that our “wind-aware” train trajectories present different shape and reduce energy consumption compared to traditional speed profiles computed regardless of any wind information.
The multi-commodity network flow problem with soft transit time constraints
Trivella, Alessio; Corman, Francesco; Koza, David F.; et al. (2021)
Transportation Research Part E: Logistics and Transportation Review
The multi-commodity network flow problem (MCNF) consists in routing a set of commodities through a capacitated network at minimum cost and is relevant for routing containers in liner shipping networks. As commodity transit times are often a critical factor, the literature has introduced hard limits on commodity transit times. In practical contexts, however, these hard limits may fail to provide sufficient flexibility since routes with even tiny delays would be discarded. Motivated by a major liner shipping operator, we study an MCNF generalization where transit time restrictions are modeled as soft constraints, in which delays are discouraged using penalty functions of transit time. Similarly, early commodity arrivals can receive a discount in cost. We derive properties that distinguish this model from other MCNF variants and adapt a column generation procedure to efficiently solve it. Extensive numerical experiments conducted on realistic liner shipping instances reveal that the explicit consideration of penalty functions can lead to significant cost reductions compared to hard transit time deadlines. Moreover, the penalties can be used to steer the flow towards slower or faster configurations, resulting in a potential increase in operational costs, which generates a trade-off that we quantify under varying penalty functions.
Enhancing the interaction of railway timetabling and line planning with infrastructure awareness
Fuchs, Florian; Trivella, Alessio; Corman, Francesco (2022)
Transportation Research Part C: Emerging Technologies
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.

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Alessio Trivella

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