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Curriculum Vitae (english)
Pubblicazioni

Giulia TRESCA

PhD student

Giulia Tresca was born in Barletta (BAT) in May 1996. She studied at Politecnico di Bari where she obtained the Bachelor Degree Computer Science and Automation Engineering in October 2018. In July 2020 she took her Master Degree in Information Systems with a thesis in “Smart Bin Packing Systems For Logistics 4.0 ​” in cooperation with the Public-private laboratory IoT 4.0 of Politecnico di Bari.

She is currently working toward the Ph.D. degree in the Department of Electrical and Information Engineering of the same university with one of the 3 scholarships funded by ISIRES Istituto Italiano Ricerca e Sviluppo – Organismo di Ricerca S.r.l. on the topic “Control Systems and optimization methodologies for Transport Management Systems”.


Publications

2021

  • Cavone, G., Carli, R., Troccoli, G., Tresca, G. & Dotoli, M. (2021) A MILP approach for the multi-drop container loading problem resolution in logistics 4.0 IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 687-692. doi:10.1109/MED51440.2021.9480359
    [BibTeX] [Abstract] [Download PDF]
    This paper addresses the multi-drop container loading problem (CLP), i.e., the problem of packing multiple bins -associated to multiple deliveries to one or more customers- into a finite number of transport units (TUs). Differently from the traditional CLP, the multi-drop CLP has been rarely handled in the literature, while effective algorithms to automatically solve this problem are needed to improve the efficiency and sustainability of internal logistics. To this aim, we propose a novel algorithm that solves a delivery-based mixed integer linear programming formulation of the problem. The algorithm efficiently determines the optimal composition of TUs by minimizing the unused space, while fulfilling a set of geometric and safety constraints, and complying with the delivery allocation. In particular, the proposed algorithm includes two steps: the first aims at clustering bins into groups to be compatibly loaded in various TUs; the latter aims at determining the optimal configuration of each group in the related TU. Finally, the proposed algorithm is applied to several realistic case studies with the aim of testing and analysing its effectiveness in producing stable and compact TU loading configurations in a short computation time, despite the high computational complexity of the multi-drop CLP. © 2021 IEEE.
    @CONFERENCE{Cavone2021687,
    author={Cavone, G. and Carli, R. and Troccoli, G. and Tresca, G. and Dotoli, M.},
    title={A MILP approach for the multi-drop container loading problem resolution in logistics 4.0},
    journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021},
    year={2021},
    pages={687-692},
    doi={10.1109/MED51440.2021.9480359},
    art_number={9480359},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113699292&doi=10.1109%2fMED51440.2021.9480359&partnerID=40&md5=734929101ce82724d3629ddb1e06f4a4},
    affiliation={Polytechnic of Bari, Department of Electrical and Information Engineering, Italy},
    abstract={This paper addresses the multi-drop container loading problem (CLP), i.e., the problem of packing multiple bins -associated to multiple deliveries to one or more customers- into a finite number of transport units (TUs). Differently from the traditional CLP, the multi-drop CLP has been rarely handled in the literature, while effective algorithms to automatically solve this problem are needed to improve the efficiency and sustainability of internal logistics. To this aim, we propose a novel algorithm that solves a delivery-based mixed integer linear programming formulation of the problem. The algorithm efficiently determines the optimal composition of TUs by minimizing the unused space, while fulfilling a set of geometric and safety constraints, and complying with the delivery allocation. In particular, the proposed algorithm includes two steps: the first aims at clustering bins into groups to be compatibly loaded in various TUs; the latter aims at determining the optimal configuration of each group in the related TU. Finally, the proposed algorithm is applied to several realistic case studies with the aim of testing and analysing its effectiveness in producing stable and compact TU loading configurations in a short computation time, despite the high computational complexity of the multi-drop CLP. © 2021 IEEE.},
    author_keywords={Container loading problem; Logistics; MILP; Multi-drop; Optimization},
    document_type={Conference Paper},
    source={Scopus},
    }