Optimization Methods for the Same-Day Delivery Problem

In the same-day delivery problem, requests with restricted time windows arrive during a given time horizon and it is necessary to decide which requests to serve and how to plan routes accordingly. We solve the problem with a dynamic stochastic method that invokes a generalized route generation function combined with an adaptive large neighborhood search heuristic. The heuristic is composed of destroying and repairing operators. The generalized route generation function takes advantage of sampled-scenarios, which are solved with the heuristic, to determine which decisions should be taken at any instant. Results obtained on different benchmark instances prove the effectiveness of the proposed method in comparison with a consensus function from the literature, with an average decrease of 10.7%, in terms of solution cost, and 24.5%, in terms of runtime

Partial Discharge alert system in medium voltage switchgear

Partial discharge (PD) is a well-known indicator of insulation problems in high voltage equipment. We report on experience collected during the development of a new online PD detection and alert system for air insulated switchgear (AIS) installed base. The approach taken to integrate the sensor with minimal retrofit effort and operational disruption is described. Results from a test setup including a line-up of panels and different reference PD sources in comparison to a commercial PD system are presented. The effect of cables connected to the switchgear is investigated by testing the system including additional capacitive load and using a simulation for a typical geometry. We also address the question regarding the design of an alert system to be used in connection with the continuous data acquisition

Ottimizzazione di sistemi di trasporto dinamici

Questa tesi di dottorato affronta problemi di ottimizzazione relativi ai sistemi di trasporto e mobilità. Lo studio si concentra su due argomenti in particolare: la pianificazione degli ordini in un sistema di Veicoli a Guida Autonoma (AGV), e la pianificazione dei percorsi per una flotta di veicoli in un sistema di consegne urgenti. Per quanto riguarda il primo argomento, la ricerca considera una revisione dettagliata della letteratura sulla pianificazione degli ordini in un sistema AGV, un modello matematico per formalizzare il problema e una raccolta delle sfide aperte e delle opportunità relativi al problema di pianificazione degli orini in un sistema AGV e delle sue varianti. Il secondo lavoro propone un nuovo algoritmo "branch-and-regret" per risolvere il problema delle consegne urgenti, noto nella letteratura come Same-Day Delivery Problem. L'obiettivo è massimizzare le richieste servite, e per farlo si utilizza un algoritmo in grado di incorporare scenari stocastici per anticipare eventi futuri e prendere decisioni su routing informate. I risultati computazionali mostrano le buone prestazioni dell'algoritmo, dimostrando la superiorità del "branch-and-regret" proposto a confronto con gli algoritmi presenti nella letteratura. In conclusione, questa tesi di dottorato apporta significativi contributi all'avanzamento della ricerca operativa nel campo dei trasporti, offrendo nuove prospettive e soluzioni innovative.This doctoral thesis explores optimization problems in the context of transportation and mobility systems. The study focuses on two specific topics: order scheduling in an Automated Guided Vehicles (AGVs) system and route planning for a fleet of vehicles in an urgent delivery system. Regarding the first topic, the research involves a detailed review of the surveys in the literature on scheduling AGVs, a mathematical model to formalize the problem and a collection of the challenges and opportunities in the context of scheduling AGVs and its variants. The work on the second topic proposes a novel branch-and-regret algorithm to solve the problem of urgent deliveries, known in the literature as Same-Day Delivery Problem. The aim is to maximize the served requests thanks to an algorithm able to incorporate sampled scenarios to anticipate future events and make informed routing decisions. The computational results show the good performance of the algorithm, demonstrating the superiority of the proposed branch-and-regret compared with state-of-the-art algorithms from the literature. In conclusion, this doctorate thesis makes significant contributions to the advancement of operations research in the field of transportation, providing new prospects and innovative solutions

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