Publications

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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