Optimal Network Design for Municipal Waste Management: Application to the Metropolitan City of Rome

Marco Boresta, Anna Livia Croella, Claudio Gentile, Laura Palagi, Diego Maria Pinto, Giuseppe Stecca () and Paolo Ventura
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Marco Boresta: Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti”, Consiglio Nazionale delle Ricerche, Via dei Taurini 19, 00185 Rome, Italy
Anna Livia Croella: Dipartimento di Ingegneria Informatica, Automatica, e Gestionale “A. Ruberti”, Università la Sapienza di Roma, 00185 Rome, Italy
Claudio Gentile: Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti”, Consiglio Nazionale delle Ricerche, Via dei Taurini 19, 00185 Rome, Italy
Laura Palagi: Dipartimento di Ingegneria Informatica, Automatica, e Gestionale “A. Ruberti”, Università la Sapienza di Roma, 00185 Rome, Italy
Diego Maria Pinto: Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti”, Consiglio Nazionale delle Ricerche, Via dei Taurini 19, 00185 Rome, Italy
Giuseppe Stecca: Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti”, Consiglio Nazionale delle Ricerche, Via dei Taurini 19, 00185 Rome, Italy
Paolo Ventura: Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti”, Consiglio Nazionale delle Ricerche, Via dei Taurini 19, 00185 Rome, Italy

Logistics, 2024, vol. 8, issue 3, 1-15

Abstract: Background: This work is part of the regional research project PIPER—Intelligent Platform for the Optimization of Recycling Operations, which aims to develop a network design model for waste collection in the metropolitan city of Rome, Italy. The goal is to enhance the strategic planning of treatment and disposal facilities by incorporating transshipment locations to improve the efficiency of waste collection. The motivation for this study stems from the stringent targets set by the European Green Deal to achieve near-zero emissions and the critical role of waste management in realizing circular economy practices. Methods: The problem is formulated as a mixed integer linear program (MILP) that includes constraints on vehicle allocation to shifts and driver requirements. An additional feature of the model is its ability to account for vehicle maintenance issues when deployed on consecutive shifts. Results: The model was tested against a real-world case study in Rome and demonstrated significant potential savings, with a reduction in total traveled distance exceeding 10%. The mathematical model was also used for evaluating strategic scenarios in a “what-if” analysis, allowing the municipal collection company to assess location options for depots, waste treatment facilities, and transshipment points, as well as to optimize the fleet composition and driver allocation during work shifts. Conclusions: This study presents a robust tool for strategic planning in waste collection operations, highlighting the benefits of using transshipment locations to improve efficiency. The findings indicate substantial potential for cost savings and operational improvements, making the model a valuable asset for municipal waste management planning.

Keywords: waste management; network design; fleet optimization; logistics (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2024
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