Abstract
We analyze the case study of Provincia di Brescia, an Italian public body managing libraries in the province of Brescia, in Northern Italy. The public body offers an interlibrary loan service organized as follows. Libraries are divided into established groups, each associated with a fixed route. According to a predetermined calendar, each library is visited by a courier a few times a week. The transportation of the items in the network is performed by an external logistics firm, selected through a reverse auction. To assess the current implementation of the service in terms of routing and transportation costs, we propose a two-phase approach based on machine learning and mixed-integer linear programming, and we evaluate it on the data of 2019. Then, we discuss its applicability in reality, and provide a few insights.
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Notes
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We point out that users’ names were all anonymized to guarantee their privacy.
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The virtual machine is hosted by the Centro Piattaforme Tecnologiche (CPT) of the University of Verona (https://cpt.univr.it/en).
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Appendices
A. The Interlibrary Loan Problem of Provincia di Brescia as a RVRP
Table 4 shows the main characteristics of the problem of optimizing the interlibrary loan service of Provincia di Brescia by following the taxonomy introduced by Lahyani et al. [13] to classify RVRP.
B. Statistical Analysis Performed on the Data of 2019
To evaluate the service not only in terms of routing costs, we performed a statistical analysis on the data of 2019. For each library in every cluster, we computed the average and median pickup/delivery copies and days of delay, the ratio between pickup/delivery copies and days of delay, and the ratio between the actual and the theoretical number of visits of a courier, as shown in Fig. 4.
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Raffaele, A., Zavatteri, M., Bazzoli, F., Gussago, M., Rizzi, R. (2023). A Two-Phase Approach to Evaluate and Optimize an Interlibrary Loan Service: The Case Study of Provincia di Brescia. In: Cosmi, M., Peirano, L., Raffaele, A., Samà, M. (eds) Operations Research and Data Science in Public Services. AIROYoung 2022. AIRO Springer Series, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-031-34546-3_2
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