In today’s logistics distribution networks, competitiveness depends on the ability to move goods from supplier to customer while minimizing operating times and costs. A specific problem characterizing the macro-area of logistics distribution is so-called cross-docking. In cross-docking, products are not put away on shelves but pass through the terminal to be transferred immediately to another vehicle. In this context, this work focuses on the Truck Scheduling Problem (TSP) in zero-inventory terminals, where any intermediate storage of goods is prohibited. The aim of the thesis is therefore to build a model for a mixed-integer linear programming (MILP) problem applicable to a zero-inventory TSP. The zero-inventory constraint implies that, in the TSP, storage and handling costs related to the warehouse are eliminated and that delivery times along the logistics chain are reduced. The assignment of trucks to doors and their temporal sequence therefore become decisive, owing to the absence of an intermediate warehouse. The model, subsequently translated into MATLAB code, was applied first to small data instances and then to larger ones, considering three different performance measures: Processing Time, Flow Time, and Tardiness. The experimental results indicate that the outcomes are influenced mainly by the intertwining of the goods loads, the number of doors, and the breadth of the deadlines.
Nelle reti di distribuzione logistiche odierne la competitività dipende dalla capacità di mobilitare le merci dal fornitore al cliente, riducendo al minimo i tempi e i costi operativi. Un problema specifico caratterizzante la macroarea della distribuzione logistica è il cosiddetto cross-docking. Nel cross-docking i prodotti non vengono riposti a scaffale, ma attraversano il terminal per essere trasferiti nell'immediato su un altro mezzo. In questo contesto, il presente lavoro si concentra sul Truck Scheduling Problem (TSP) in terminal a inventario zero, dove è vietato qualsiasi stoccaggio intermedio della merce. Lo scopo della tesi è, quindi, di costruire un modello relativo ad un problema di programmazione lineare intera mista (MILP) applicabile a un TSP a inventario zero. Il vincolo dell'inventario zero implica che nel TSP vengano eliminati i costi di stoccaggio e di movimentazione legati al magazzino, e che i tempi di consegna lungo la catena logistica siano ridotti. L'assegnazione dei camion alle porte e la loro sequenza temporale diventano quindi determinanti a causa dell'assenza di un magazzino intermedio. Il modello, tradotto poi in codice MATLAB, è stato applicato dapprima a piccole istanze di dati e poi a istanze di dimensioni maggiori, considerando tre diverse misure di prestazione: il Processing Time, il Flow Time e la Tardiness. I risultati sperimentali indicano che i risultati sono influenzati soprattutto dall’intreccio dei carichi di merce, dal numero di porte e dall’ampiezza delle scadenze.
TECNICHE DI SCHEDULAZIONE IN TERMINAL DI CROSS-DOCKING MEDIANTE PROGRAMMAZIONE INTERA-MISTA LINEARE
GRECO, GERARDO
2025/2026
Abstract
In today’s logistics distribution networks, competitiveness depends on the ability to move goods from supplier to customer while minimizing operating times and costs. A specific problem characterizing the macro-area of logistics distribution is so-called cross-docking. In cross-docking, products are not put away on shelves but pass through the terminal to be transferred immediately to another vehicle. In this context, this work focuses on the Truck Scheduling Problem (TSP) in zero-inventory terminals, where any intermediate storage of goods is prohibited. The aim of the thesis is therefore to build a model for a mixed-integer linear programming (MILP) problem applicable to a zero-inventory TSP. The zero-inventory constraint implies that, in the TSP, storage and handling costs related to the warehouse are eliminated and that delivery times along the logistics chain are reduced. The assignment of trucks to doors and their temporal sequence therefore become decisive, owing to the absence of an intermediate warehouse. The model, subsequently translated into MATLAB code, was applied first to small data instances and then to larger ones, considering three different performance measures: Processing Time, Flow Time, and Tardiness. The experimental results indicate that the outcomes are influenced mainly by the intertwining of the goods loads, the number of doors, and the breadth of the deadlines.| File | Dimensione | Formato | |
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GRECO_tesi.pdf
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Descrizione: Documento di tesi definitivo
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https://hdl.handle.net/20.500.14253/110680