Publications

2025

1. Towards a Framework for Self-Evolving Products in Additive Manufacturing

   Jyrki Savolainen , Michele Urbani, and Heidi Piili

   IFAC-PapersOnLine, 2025

   11th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2025

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   In critical industrial applications, enhancing existing custom-designed products throughout their life cycles is crucial for improving user value while managing the costs of continuous design iterations. Additive manufacturing has demonstrated significant improvements in product performance through optimized designs, but implementing these improvements in scale requires excessive design efforts. In this paper, we address this gap by presenting a digital twin (DT)-based product design framework for additively manufactured parts that makes the design of the physical parts independently to adapt into their industrial application environments. The methodology is based on efficiently utilizing data in the intersection of application-specific DT model and the CAD (Computer-Aided Design). It is proposed that with the smart utilization of Evolutionary Algorithms (EA), most of the manual labor involved in drafting performance-improving revisions of design of part geometry could be eliminated. The proposition is evaluated from the business model point of view highlighting the potential for novel, mutually beneficial supplier-customer relationships in advanced industrial equipment solutions.

2. A Tool for the Implementation of Open Neural Network Exchange Models in Functional Mockup Units

   Michele Urbani, Michele Bolognese , Luca Pratticò , and 1 more author

   In Modelica Conferences , 2025

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3. Towards a Framework for Self-Evolving Products in Additive Manufacturing

   Jyrki Savolainen , Michele Urbani, and Heidi Piili

   IFAC-PapersOnLine, 2025

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4. A Multi-Objective Optimization of a District Heating Network: Integrated and Dynamic Decarbonization Solutions for the Case Study of Riva Del Garda (Italy)

   Amit Jain , Diego Viesi , Silvia Ricciuti , and 2 more authors

   Energies, 2025

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   This study explores the decarbonization of the district heating network in Riva del Garda. The existing system (baseline) was modeled in EnergyPLAN, and future configurations were optimized using a Multi-Objective Evolutionary Algorithm (MOEA) to minimize both CO2 emissions and annual costs. Nine decision variables were assessed under defined boundary conditions to generate alternative future scenarios grouped into five types. In Type A, a large deep geothermal cogeneration plant combined with a small biomass boiler achieved the only zero-emission solution, with lower annual costs than the baseline but high capital needs. Excluding deep geothermal cogeneration (Type B) led to dominance of the biomass boiler and waste heat recovery from the Alto Garda Power (AGP) plant; full decarbonization remained possible only with extensive biomass use at a higher cost. Removing biomass (Type C), the solar thermal plant, and the shallow geothermal heat pump enabled deep but costly decarbonization, including grid electricity dependence. Types D and E, dominated, respectively, by shallow geothermal heat pump and electric boiler, provided moderate emission reductions and further increase in costs. Across all types, thermal storage improved operational flexibility. These analyses were also extended to assess potential district heating network expansions within Riva del Garda and into the neighboring municipality of Arco.

2024

1. Designing Food Hubs for Territories of Proximity: Assessing the Spatial, Ecological, and Cultural Potentials of Places through Multi-Criteria Decision Support Systems

   Sara Favargiotti , Giulia Zantedeschi , Angelica Pianegonda , and 2 more authors

   Land, 2024

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   Logistics, distribution models, and landscapes of food production strongly influence the space of our cities and territories. In addition to the network of large-scale retail distribution that is diffused in urban and non-urban areas, with this contribution, we study the presence of new forms of the local and sustainable distribution of food (such as Alternative Food Networks, and community-supported agriculture). Studying and understanding how these distribution models can support and be integrated within a landscape planning and design approach is explored through the Analytic Hierarchy Process (AHP), a multi-criteria decision analysis method. Through the specific focus of a Food Hub localization, the aim is to demonstrate how distribution models can not only support but also integrate into landscape planning and design. The fundamental objectives for structuring and locating a Food Hub can be organized under three strategic objectives: pursuing the benefit of people, the planet, and profit. The choice of one distribution method over others, or what is the best location and condition for distribution centers, is the question we have tested with the collaboration of “L’Ortazzo” Association. The case study is a solidarity purchasing group located in the upper Valsugana valley area (Trentino Region, Italy), a supra-municipality reality involving about a hundred families that, currently, do not have a physical distribution center.

2023

1. COR

   A cooperative team orienteering optimisation model and a customized resolution metaheuristic

   Andrea Bendazzoli , Michele Urbani, Matteo Brunelli , and 1 more author

   Computers & Operations Research, 2023

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   Workforce routing optimisation is an essential management task to achieve customer satisfaction and minimise costs in service-providing companies. A typical problem for an energy-saving company (ESCo) is to optimise the provisioning of maintenance services for the contracted buildings through a fleet of heterogeneous maintenance staff. This problem is modelled as a cooperative orienteering problem with time windows, operator qualification, and synchronisation constraints. A novel insertion heuristic is proposed and embedded in an adaptive large neighbourhood search algorithm and it is tested against a state-of-the-art algorithm using real-world data. The comparative study demonstrates the potential of the heuristic and a sensitivity analysis shows its robustness, focusing on time window length variation, and the number of synchronisation requirements. We consider managerial insights supported by results and concerns, e.g., the employment of further technicians to increase the number of served facilities. The proposed model and algorithm apply to similar problems as well.

2. InfSci

   A numerical comparative study of uncertainty measures in the Dempster–Shafer evidence theory

   Michele Urbani, Gaia Gasparini , and Matteo Brunelli

   Information Sciences, 2023

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   We consider a wide range of measures of uncertainty that have been proposed within the Dempster–Shafer evidence theory. All these measures aim to quantify the uncertainty associated with a given basic probability assignment. As a preliminary step, we offer a study of the literature, which shows a recent resurgence of interest in the quantification of uncertainty in the evidence theory. Then, we compare a number of uncertainty measures by means of numerical simulations and analyze their similarities and differences using rank correlation coefficients, hierarchical clustering, and centrality analysis. The results show that uncertainty measures with similar formulations do not necessarily have similar numerical properties, and some original results are obtained. In particular, we demonstrate that numerical studies on uncertainty measures are necessary to obtain more insight and to enhance the interpretability of the values returned by the measures.

3. EJOR

   An approach for bi-objective maintenance scheduling on a networked system with limited resources

   Michele Urbani, Matteo Brunelli , and Antti Punkka

   European Journal of Operational Research, 2023

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   Preventive maintenance activities are often the cause of downtime of technical multi-component systems. To minimize maintenance costs and maximize productivity, maintenance tasks are often grouped and carried out simultaneously. We consider the problem of obtaining an optimal maintenance schedule when the multi-component system is also a networked system and can be modeled as a directed graph, where nodes represent machines or workers, and edges represent the exchange of material, information, or work between these nodes. To find efficient maintenance schedules, we formulate a bi-objective optimization problem, which considers the limited availability of maintenance personnel, and we propose an algorithm that finds a set of maintenance schedules, which are a good approximation of the Pareto front in terms of costs and productivity. Through sensitivity analysis we show the extent to which adding maintenance personnel improves system productivity at the expense of increased maintenance costs and idle time of some resources. Besides solving the Pareto-optimal schedules, we show how the developed model is useful in maintenance personnel planning, and we outline limitations and future developments of the present work.

4. A Multi-Policy Sequence-Based Selection Hyper-Heuristic for Multi-Objective Optimization

   Michele Urbani, and Francesco Pilati

   In Proceedings of the Companion Conference on Genetic and Evolutionary Computation , 2023

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   Selection hyper-heuristics (SSHH) are search strategies that can be successfully applied to multi-objective optimization (MOO) problems. They showed resilient results to different types of optimization problems, which may come at the cost of a longer resolution time than other metaheuristics. Learning a single selection rule in MOO might be limiting because the information from the multiple objectives might be unexploited. A novel approach named the multi-policy approach is proposed and discussed to further enhance the searching ability of sequence-based selection hyper-heuristics, together with an ad-hoc learning rule. The availability of a set of problem-specific low-level heuristics is assumed and the resolution of a specific class of combinatorial problems, i.e., vehicle routing problems (VRPs), is considered for a numerical example. The multi-policy approach showed the ability to learn different selection rules for each objective and to change them during the execution of the algorithm, i.e., when solutions are getting closer to the Pareto front. The availability of parallel computation can speed up the calculation since the methodology is suitable for parallelization. The proposed methodology was found to produce production-ready solutions and the approach is expected to be successfully extended to problems different from the VRP.

2021

1. JIntManuf

   Maintenance optimization for a multi-unit system with digital twin simulation: Example from the mining industry

   Jyrki Savolainen , and Michele Urbani

   Journal of Intelligent Manufacturing, 2021

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   Optimization of operations and maintenance (O&M) in the industry is a topic that has been largely studied in the literature. Many authors focused on reliability-based approaches to optimize O&M, but little attention has been given to study the influence of macroeconomic variables on the long-term maintenance policy. This work aims to optimize time-based maintenance (TBM) policy in the mining industry. The mine environment is reproduced employing a virtual model that resembles a digital twin (DT) of the system. The effect of maintenance decisions is replicated by a discrete event simulation (DES), whereas a model of the financial operability of the mine is realized through System Dynamics (SD). The simultaneous use of DES and the SD allows us to reproduce the environment with high-fidelity and to minimize the cost of O&M. The selected illustrative case example demonstrates that the proposed approach is feasible. The issues of using high dimensional simulation data from DT-models in managerial decision making is identified and discussed.

2020

1. IEEEAccess

   A Comparison of Maintenance Policies for Multi-Component Systems Through Discrete Event Simulation of Faults

   Michele Urbani, Matteo Brunelli , and Mikael Collan

   IEEE Access, 2020

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   Finding optimal maintenance policies for complex multi-component systems is a real-world challenge in the industry. This paper compares three maintenance policies for complex systems with non-identical components and economic dependencies in case of fault. Discrete event and Monte Carlo simulation are used to replicate fault occurrences, while a genetic algorithm is used to minimize the cost of maintenance by finding optimal groups of maintenance activities. Low total average maintenance cost and high average availability of the system are considered as desirable objectives and the capacity of the studied policies to achieve these goals is analyzed. None of the policies dominates the others (in a Pareto efficiency sense), thus making the policy choice context dependent and subject to decision makers’ preferences.

2. Maintenance-management in light of manufacturing 4.0

   Michele Urbani, Dario Petri , Matteo Brunelli , and 1 more author

   Technical, Economic and Societal Effects of Manufacturing 4.0: Automation, Adaption and Manufacturing in Finland and Beyond, 2020

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3. Additive Manufacturing Cases and a Vision for a Predictive Analytics and Additive Manufacturing Based Maintenance Business Model

   Michele Urbani, and Mikael Collan

   Technical, Economic and Societal Effects of Manufacturing 4.0: Automation, Adaption and Manufacturing in Finland and Beyond, 2020

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