Workload Equity in Vehicle Routing Problems: A Survey and Analysis

Over the past two decades, equity aspects have been considered in a growing number of models and methods for vehicle routing problems (VRPs). Equity concerns most often relate to fairly allocating workloads and to balancing the utilization of resources, and many practical applications have been reported in the literature. However, there has been only limited discussion about how workload equity should be modeled in VRPs, and various measures for optimizing such objectives have been proposed and implemented without a critical evaluation of their respective merits and consequences. This article addresses this gap with an analysis of classical and alternative equity functions for biobjective VRP models. In our survey, we review and categorize the existing literature on equitable VRPs. In the analysis, we identify a set of axiomatic properties that an ideal equity measure should satisfy, collect six common measures, and point out important connections between their properties and those of the resulting Pareto-optimal solutions. To gauge the extent of these implications, we also conduct a numerical study on small biobjective VRP instances solvable to optimality. Our study reveals two undesirable consequences when optimizing equity with nonmonotonic functions: Pareto-optimal solutions can consist of non-TSP-optimal tours, and even if all tours are TSP optimal, Pareto-optimal solutions can be workload inconsistent, i.e. composed of tours whose workloads are all equal to or longer than those of other Pareto-optimal solutions. We show that the extent of these phenomena should not be underestimated. The results of our biobjective analysis are valid also for weighted sum, constraint-based, or single-objective models. Based on this analysis, we conclude that monotonic equity functions are more appropriate for certain types of VRP models, and suggest promising avenues for further research.Comment: Accepted Manuscrip

A large neighbourhood based heuristic for two-echelon routing problems

In this paper, we address two optimisation problems arising in the context of city logistics and two-level transportation systems. The two-echelon vehicle routing problem and the two-echelon location routing problem seek to produce vehicle itineraries to deliver goods to customers, with transits through intermediate facilities. To efficiently solve these problems, we propose a hybrid metaheuristic which combines enumerative local searches with destroy-and-repair principles, as well as some tailored operators to optimise the selections of intermediate facilities. We conduct extensive computational experiments to investigate the contribution of these operators to the search performance, and measure the performance of the method on both problem classes. The proposed algorithm finds the current best known solutions, or better ones, for 95% of the two-echelon vehicle routing problem benchmark instances. Overall, for both problems, it achieves high-quality solutions within short computing times. Finally, for future reference, we resolve inconsistencies between different versions of benchmark instances, document their differences, and provide them all online in a unified format

Prototype of speech translation system for audio effective communication

The present document exposes the development of a prototype of translation system as a Thesis Project. It consists basically on the capture of a flow of voice from the emitter, integrating advanced technologies of voice recognition, instantaneous translation and communication over the internet protocol RTP/RTCP (Real time Transport Protocol) to send information in real-time to the receiver. This prototype doesn't transmit image, it only boards the audio stage. Finally, the project besides embracing a problem of personal communications, tries to contribute to the development of activities related with the speech recognition, motivating new investigations and advances on the area.Applications in Artificial Intelligence - Language ProcessingRed de Universidades con Carreras en Informática (RedUNCI

Utilizing Microwave Ablation in Combination with Vertebroplasty Is an Effective Method in Reducing Cancer Related Back Pain, Disability, and Opioid Use: A Systematic Review and Meta-Analysis

Background: It is estimated that metastases from primary malignant neoplasms affect the spine around 30%-70% of the time. Many times, these osteolytic tumors will cause the degradation of the vertebrae, leaving patients in a tremendous amount of pain, disability, and dependence on opioids as analgesics. Microwave ablation (MWA) followed by vertebroplasties (VP) has been a developing treatment for such a condition; however, there are no systematic reviews or meta-analyses examining the method’s effectiveness thus far. Purpose: This systematic review and meta-analysis analyzes the 4-week and 12-week outcomes of patients with metastatic spinal cancer treated with the combinatorial treatment of microwave ablation followed by a vertebroplasty. Methods: The systematic review and meta-analysis followed the 2020 PRISMA guidelines. Five online databases (Cochrane, Embase, PubMed, Web Of Science, Scopus) were screened. Included were studies that included 4-week and 12-week Visual Analogue Scales (VAS) scores, Oswestry Disability Index (ODI) measures, and Daily Morphine Consumption (DMC). Four studies fit our inclusion criteria, yielding a sample size of 117 patients. Results: Our results portray strong clinically significant results of utilizing MWA with VP at both 4-weeks and 12-weeks. At 4-weeks, the effect size for the reduction of VAS, ODI, and DMC were Cohen’s d = 4.00, Cohen’s d = 3.29, Cohen’s d = 4.50, respectively. At 12-weeks, the reduction in VAS and DMC had an effect size of Cohen’s d = 4.34 and Cohen’s d = 4.56, respectively. Comparing the 4-week and 12-week VAS scores, there was a difference in Cohen’s d = 0.34, in favor of 12-weeks, signifying a possible clinical significance of even further reduction of VAS scores as time progresses. Conclusion: Utilizing microwave ablation in combination with a vertebroplasty greatly reduced pain, disability, and opioid consumption in patients with metastatic spinal cancer over a 4-week and 12-week timeline. However, with only 117 patient data available to be analyzed, future studies are needed in order to increase the available sample size. Furthermore, comparative randomized controlled trials assessing the significance of utilizing MWA with VP, compared to VP alone could further exemplify the importance of MWA to the treatment

Free-rider Attacks on Model Aggregation in Federated Learning

Free-rider attacks against federated learning consist in dissimulating participation to the federated learning process with the goal of obtaining the final aggregated model without actually contributing with any data. This kind of attacks is critical in sensitive applications of federated learning, where data is scarce and the model has high commercial value. We introduce here the first theoretical and experimental analysis of free-rider attacks on federated learning schemes based on iterative parameters aggregation, such as FedAvg or FedProx, and provide formal guarantees for these attacks to converge to the aggregated models of the fair participants. We first show that a straightforward implementation of this attack can be simply achieved by not updating the local parameters during the iterative federated optimization. As this attack can be detected by adopting simple countermeasures at the server level, we subsequently study more complex disguising schemes based on stochastic updates of the free-rider parameters. We demonstrate the proposed strategies on a number of experimental scenarios, in both iid and non-iid settings. We conclude by providing recommendations to avoid free-rider attacks in real world applications of federated learning, especially in sensitive domains where security of data and models is critical

Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization

The aim of Machine Unlearning (MU) is to provide theoretical guarantees on the removal of the contribution of a given data point from a training procedure. Federated Unlearning (FU) consists in extending MU to unlearn a given client's contribution from a federated training routine. Current FU approaches are generally not scalable, and do not come with sound theoretical quantification of the effectiveness of unlearning. In this work we present Informed Federated Unlearning (IFU), a novel efficient and quantifiable FU approach. Upon unlearning request from a given client, IFU identifies the optimal FL iteration from which FL has to be reinitialized, with unlearning guarantees obtained through a randomized perturbation mechanism. The theory of IFU is also extended to account for sequential unlearning requests. Experimental results on different tasks and dataset show that IFU leads to more efficient unlearning procedures as compared to basic re-training and state-of-the-art FU approaches

Personalized Federated Learning through Local Memorization

Federated learning allows clients to collaboratively learn statistical models while keeping their data local. Federated learning was originally used to train a unique global model to be served to all clients, but this approach might be sub-optimal when clients' local data distributions are heterogeneous. In order to tackle this limitation, recent personalized federated learning methods train a separate model for each client while still leveraging the knowledge available at other clients. In this work, we exploit the ability of deep neural networks to extract high quality vectorial representations (embeddings) from non-tabular data, e.g., images and text, to propose a personalization mechanism based on local memorization. Personalization is obtained by interpolating a collectively trained global model with a local $k$-nearest neighbors (kNN) model based on the shared representation provided by the global model. We provide generalization bounds for the proposed approach in the case of binary classification, and we show on a suite of federated datasets that this approach achieves significantly higher accuracy and fairness than state-of-the-art methods.Comment: 23 pages, ICML 202