Rich Vehicle Routing Problems: models, algorithms and applications.

I Vehicle Routing Problems (VRPs) sono una branca di problemi centrali nella Ricerca Operativa. Introdotto da Dantzig et al (1959), il problema mira a trovare le rotte ottimali per una flotta di veicoli per servire i clienti. Negli ultimi anni si è assistito a un incremento nell'applicazione dei modelli di ottimizzazione da parte di aziende e organizzazioni. Questo cambiamento di focus mira ad affrontare le complessità del mondo reale introducendo caratteristiche e vincoli innovativi. La famiglia di questi problemi estesi è chiamata Rich VRP (RVRPs). I RVRPs estendono le formulazioni tradizionali dei VRP incorporando vincoli specifici del problema che riflettono decisioni prese sia a livello tattico che operativo in contesti pratici. Questa tesi approfondisce lo studio dei RVRPs nel settore sanitario e logistico. Forniamo formulazioni di modelli matematici efficienti, approcci di risoluzione esatti ed euristici, e un'analisi comprensiva dei risultati computazionali. La nostra ricerca affronta questioni critiche all'interno dei Nurse Routing Problems (NRPs) nel settore sanitario. Il nostro obiettivo è migliorare i risultati logistici per le organizzazioni sanitarie migliorando contemporaneamente le condizioni di lavoro dei fornitori di assistenza e la qualità dell'assistenza fornita, Per raggiungere questo scopo, introduciamo il concetto di equità negli NRPs, insieme a vincoli che migliorano la qualità come la coerenza infermiere-paziente e le specifiche delle finestre temporali. La nostra analisi inizia esaminando diverse metriche di equità, fornendo un insieme di funzioni obiettivo che possono essere scambiate per valutare l'interazione tra diverse metriche e le loro implicazioni sui costi. Successivamente, estendiamo la nostra indagine sull'equità inserendo nuove misure e fornendo una formulazione Multi-Obiettivo del precedente NRP in cui selezioniamo triplette di funzioni per rappresentare gli interessi di ogni stakeholder. Inoltre, presentiamo un NRP Dinamico Multi-Periodo con Consistenza in cui la distribuzione temporale delle richieste dei pazienti è sconosciuta. L'obiettivo del problema è decidere le assegnazioni infermiere-paziente per diversi giorni basandosi su richieste rivelate giornalmente. Proponiamo due approcci: un metodo dinamico puramente miope, che manca di informazioni sugli eventi futuri, e un metodo di ottimizzazione basato su scenari che sfrutta i dati storici per prevedere sviluppi futuri. Nel dominio logistico proponiamo il Last Mile Delivery Problem with Locker Selection (LMDP-LS) e l’Attended Home Delivery Problem with Recovery Options (AHDP-RO). Nel LMDP-LS, valutiamo l'impatto ambientale dei locker per pacchi tenendo conto dell'impronta ecologica dei consumatori. Nell'AHDP-RO, modelliamo e risolviamo anche il servizio tradizionale di consegna a domicilio assistita, modellando la probabilità di trovare il cliente a casa attraverso profili di disponibilità e tracciando la probabilità di consegne riuscite durante la giornata lavorativa. Introduciamo la possibilità per i corrieri di intraprendere azioni di recupero quando i clienti sono indisponibili, con penalità associate incluse nella funzione obiettivo. L'obiettivo generale è pianificare le rotte giornaliere dei corrieri per minimizzare i costi di routing e di penalità. Nel corso della nostra ricerca, forniamo i risultati di istanze di piccole dimensioni risolte all'ottimalità e impieghiamo la meta-euristica di Adaptive Large Neighborhood Search (ALNS) per ottenere soluzioni di buona qualità per quelle di grandi dimensioni.The Vehicle Routing Problem (VRP) is one of the most central transportation problems in Operations Research. Introduced by Dantzing et al.(1959), the problem aims to find the optimal routes for a fleet of vehicles to serve customers. The traditional version of the VRP and its variants have been extensively studied in the academic literature. However, recent years have witnessed a surge in the application of optimization models by businesses and organizations. This shift in focus aims to address real-world complexities by introducing novel features and constraints. The family of these extended problems is called Rich VRP (RVRPs). RVRPs extend the traditional academic formulations of VRPs by incorporating problem-specific constraints that closely mirror decisions made at both tactical and operational levels in practical settings. This thesis delves into the study of RVRPs in healthcare and logistics. We provide efficient mathematical model formulations, exact and heuristic resolution approaches, and a comprehensive analysis of the computational results. Our research addresses critical issues within the Nurse Routing Problems (NRPs) in healthcare. Our goal is to enhance logistic outcomes for healthcare organizations while simultaneously improving the working conditions of healthcare providers and the quality of care delivered to patients. To achieve this, we introduce the concept of fairness into NRPs, along with quality-enhancing constraints such as nurse-patient consistency and time window specifications. Our analysis begins by examining several fairness metrics, considering patients and nurses within a Single-Objective Single-Period NRP framework. We provide a set of objective functions that can be interchanged to assess the interaction between different metrics and their cost implications. Next, we extend our investigation on fairness by inserting new measures and providing a Multi-Objective formulation of the previous NRP. Employing a lexicographic approach, we simultaneously consider multiple objective functions, selecting triplets of functions to represent the interests of each stakeholder (hospital, nurses, and patients). Furthermore, we present a Dynamic Multi-Period NRP with Consistency Constraints in which the temporal distribution of patient requests is unknown. Objective of the problem is to decide nurse-patient assignments over several days based on newly revealed daily requests. We propose two approaches: a pure myopic dynamic method, which lacks future event information, and a scenario-based optimization method that leverages historical data to forecast future developments. We propose two essential problem formulations in the logistics domain: the Last Mile Logistic Delivery Problem with Parcel Lockers (LMDP-LS) and the Attended Home Delivery Problem with Recovery Options (AHDP-RO). In the LMDP-LS, we evaluate the environmental impact of parcel lockers when the ecological footprint of consumers is taken into account. The problem aims to derive meaningful insights into the environmental impact of both the company and the consumers in the switch from a door-to-door delivery service to a locker-based one. In the AHDP-RO, we also model and solve the traditional attended home delivery service, which mandates the customer's presence at home to avoid delivery failures. Specifically, we model the probability of finding the customer at home through availability profiles, plotting the probability of successful deliveries during the working day. We introduce the possibility for couriers to take recovery actions when customers are unavailable, with associated penalties included in the objective function. The overarching objective is to plan daily courier routes to minimize routing and penalty costs. Throughout our research, we provide the results of small-size instances solved to optimality and we employ the Adaptive Large Neighborhood Search (ALNS) meta-heuristic to obtain good quality solutions for large-size ones

Data Collection in Two-Tier IoT Networks with Radio Frequency (RF) Energy Harvesting Devices and Tags

The Internet of things (IoT) is expected to connect physical objects and end-users using technologies such as wireless sensor networks and radio frequency identification (RFID). In addition, it will employ a wireless multi-hop backhaul to transfer data collected by a myriad of devices to users or applications such as digital twins operating in a Metaverse. A critical issue is that the number of packets collected and transferred to the Internet is bounded by limited network resources such as bandwidth and energy. In this respect, IoT networks have adopted technologies such as time division multiple access (TDMA), signal interference cancellation (SIC) and multiple-input multiple-output (MIMO) in order to increase network capacity. Another fundamental issue is energy. To this end, researchers have exploited radio frequency (RF) energy-harvesting technologies to prolong the lifetime of energy constrained sensors and smart devices. Specifically, devices with RF energy harvesting capabilities can rely on ambient RF sources such as access points, television towers, and base stations. Further, an operator may deploy dedicated power beacons that serve as RF-energy sources. Apart from that, in order to reduce energy consumption, devices can adopt ambient backscattering communication technologies. Advantageously, backscattering allows devices to communicate using negligible amount of energy by modulating ambient RF signals. To address the aforementioned issues, this thesis first considers data collection in a two-tier MIMO ambient RF energy-harvesting network. The first tier consists of routers with MIMO capability and a set of source-destination pairs/flows. The second tier consists of energy harvesting devices that rely on RF transmissions from routers for energy supply. The problem is to determine a minimum-length TDMA link schedule that satisfies the traffic demand of source-destination pairs and energy demand of energy harvesting devices. It formulates the problem as a linear program (LP), and outlines a heuristic to construct transmission sets that are then used by the said LP. In addition, it outlines a new routing metric that considers the energy demand of energy harvesting devices to cope with routing requirements of IoT networks. The simulation results show that the proposed algorithm on average achieves 31.25% shorter schedules as compared to competing schemes. In addition, the said routing metric results in link schedules that are at most 24.75% longer than those computed by the LP

Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

We envision a mobile edge computing (MEC) framework for machine learning (ML) technologies, which leverages distributed client data and computation resources for training high-performance ML models while preserving client privacy. Toward this future goal, this work aims to extend Federated Learning (FL), a decentralized learning framework that enables privacy-preserving training of models, to work with heterogeneous clients in a practical cellular network. The FL protocol iteratively asks random clients to download a trainable model from a server, update it with own data, and upload the updated model to the server, while asking the server to aggregate multiple client updates to further improve the model. While clients in this protocol are free from disclosing own private data, the overall training process can become inefficient when some clients are with limited computational resources (i.e. requiring longer update time) or under poor wireless channel conditions (longer upload time). Our new FL protocol, which we refer to as FedCS, mitigates this problem and performs FL efficiently while actively managing clients based on their resource conditions. Specifically, FedCS solves a client selection problem with resource constraints, which allows the server to aggregate as many client updates as possible and to accelerate performance improvement in ML models. We conducted an experimental evaluation using publicly-available large-scale image datasets to train deep neural networks on MEC environment simulations. The experimental results show that FedCS is able to complete its training process in a significantly shorter time compared to the original FL protocol

An optimisation approach for the e-grocery order picking and delivery problem

[EN] Traditional supermarket chains that are adopting an omni-channel approach must now carry out the order picking and delivery processes to serve online orders, previously done by the customer. The complexity of the logistics processes has increased, therefore modelling and optimising e-grocery operations becomes definitely important. Since there are few studies modelling order picking and delivery processes, we propose an approach that simultaneously optimises the decision variables of different functions which have traditionally been treated separately. In this study, we present a linear programming model for store-based e-fulfilment strategies with multiple picking locations. The proposed model optimises the allocation of online orders to stores, based on the e-fulfilment costs. As well as minimising the picking and delivery costs, the proposed approach consolidates workloads in order to avoid idle times and reduce the amount of resources required. A weighted sum method is applied to compute the solution, integrating parameters that represent different store features such as the product range, sales mode and physical store activities. The proposed model has been tested on one of the largest grocery sellers, showing that substantial savings can be achieved by reallocating orders to different stores, time windows and delivery vehicles. By focusing on optimising e-fulfilment resources, this approach serves as a guide for traditional grocery sellers to redesign their supply chains and to facilitate decision-making at a managerial level.Funding was provided by Universidade de Vigo.Vazquez-Noguerol, M.; Comesaña-Benavides, J.; Poler, R.; Prado-Prado, JC. (2022). An optimisation approach for the e-grocery order picking and delivery problem. Central European Journal of Operations Research. 30(3):961-990. https://doi.org/10.1007/s10100-020-00710-9S96199030