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GIULIA CASELLI

Assegnista di ricerca
Dipartimento di Scienze e Metodi dell'Ingegneria
CULTORE DELLA MATERIA
Dipartimento di Economia "Marco Biagi"

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Pubblicazioni

2023 - Application of Machine Learning Demand Forecasting Techniques in the Italian Processed Meat Industry [Abstract in Atti di Convegno]
Mucciarini, Mirko; Caselli, Giulia; Iori, Manuel; Lippi, Marco
abstract

2022 - Demand Forecasting Methods: A Case Study in the Italian Processed Meat Industry [Abstract in Atti di Convegno]
Mucciarini, Mirko; Caselli, Giulia; Iori, Manuel; Lippi, Marco
abstract

2022 - Mixed Integer Linear Programming for a Real-World Parallel Machine Scheduling Problem with Workforce and Precedence Constraints [Capitolo/Saggio]
Caselli, G.; Delorme, M.; Iori, M.; Magni, C. A.
abstract

In this work, we consider a real-world scheduling problem occurring in the engineering test laboratory of a multinational company producing hydraulic components for motion systems. Similar problems have been solved in the literature under the framework of resource constrained parallel machine scheduling problems. In our work, the tests on the hydraulic components are the jobs to be scheduled. Each job must be processed on a machine and requires an additional human resource to prepare the machine and supervise the job. Machine and workforce eligibility constraints are also included. Release and due dates are given for jobs. The aim is to minimize the total weighted tardiness. Each job has a processing time expressed in working days that depends on the machine and requires a fixed number of hours per day for its assigned worker. Moreover, precedence and contiguity relations between jobs must be respected. We propose a Mixed Integer Linear Programming formulation to model the problem and demonstrate its effectiveness on both real-world and randomly generated instances.

2022 - Mixed Integer Linear Programming for CO2 emissions minimization in a Waste Transfer Facility Location Problem [Relazione in Atti di Convegno]
Caselli, Giulia; Columbu, Giomaria; Iori, Manuel; Magni, Carlo Alberto
abstract

In this work, we solve a real-world facility location problem by means of a mixed integer linear programming model. The problem is faced by an Italian multi-utility company operating in the sector of waste management. The company works in several Italian regions to collect and treat the urban waste through a network of facilities. In this problem, a set of demand points is given with a predicted quantity of waste to be collected and a fixed number of visits required over a predetermined time horizon. The flow of different classes of recyclable waste must be optimized by deciding whether and where to open additional intermediate transfer facilities among a set of dedicated points. The aim is to minimize the CO2 emissions involved in the process, including emissions from the use of additional facilities and the transport of waste across the network. We provide a mathematical formulation for the problem, and use it to solve a real-world case study. An optimal solution is obtained with a significant reduction in CO2 emissions and a well-structured network, proving the efficacy of the model.

2021 - A Mathematical Formulation for Reducing Overcrowding in Hospitals' Waiting Rooms [Relazione in Atti di Convegno]
Caselli, G.; De Santis, D.; Delorme, M.; Iori, M.
abstract

The COVID-19 pandemic has triggered several new measures in public and private companies to limit the spread of the virus. One of the most effective measures was shown to be social distancing, but such measure is not easy to implement for every entity, especially for hospitals. In this work, we study the case of an Italian hospital whose goal is to find the best layout of outpatient services to reduce overcrowding in the waiting rooms. We propose an Integer Linear Programming model to identify the weekly optimal layout and we test it on a set of real data from the year 2019. The results obtained by our model reduce the overcrowding by 80% on average with respect to the results obtained with the configuration used by the hospital, but such results can only be obtained if the layout is allowed to change every week. We then study the case in which we force the layout to be fixed for two or three consecutive weeks and outline that both the computational time and the solution quality worsen significantly.

2021 - Integer Linear Programming for the Tutor Allocation Problem: A practical case in a British University [Articolo su rivista]
Caselli, Giulia; Delorme, Maxence; Iori, Manuel
abstract

In the Tutor Allocation Problem, the objective is to assign a set of tutors to a set of workshops in order to maximize tutors’ preferences. The problem is solved every year by many universities, each having its own specific set of constraints. In this work, we study the tutor allocation in the School of Mathematics at the University of Edinburgh, and solve it with an integer linear programming model. We tested the model on the 2019/2020 case, obtaining a significant improvement with respect to the manual assignment in use and we showed that such improvement could be maintained while optimizing other key metrics such as load balance among groups of tutors and total number of courses assigned. Further tests on randomly created instances show that the model can be used to address cases of broad interest. We also provide meaningful insights on how input parameters, such as the number of workshop locations and the length of the tutors’ preference list, might affect the performance of the model and the average number of preferences satisfied.