A new VRPPD model and a hybrid heuristic solution approach for e-tailing

We analyze a business model for e-supermarkets to enable multi-product sourcing capacity through co-opetition (collaborative competition). The logistics aspect of our approach is to design and execute a network system where “premium” goods are acquired from vendors at multiple locations in the supply network and delivered to customers. Our specific goals are to: (i) investigate the role of premium product offerings in creating critical mass and profit; (ii) develop a model for the multiple-pickup single-delivery vehicle routing problem in the presence of multiple vendors; and (iii) propose a hybrid solution approach. To solve the problem introduced in this paper, we develop a hybrid metaheuristic approach that uses a Genetic Algorithm for vendor selection and allocation, and a modified savings algorithm for the capacitated VRP with multiple pickup, single delivery and time windows (CVRPMPDTW). The proposed Genetic Algorithm guides the search for optimal vendor pickup location decisions, and for each generated solution in the genetic population, a corresponding CVRPMPDTW is solved using the savings algorithm. We validate our solution approach against published VRPTW solutions and also test our algorithm with Solomon instances modified for CVRPMPDTW.

Varve data from a sediment profile of Lake Woserin, Germany

Time-series of varve properties and geochemistry were established from varved sediments of Lake Woserin (north-eastern Germany) covering the recent period AD 2010-1923 and the Mid-Holocene time-window 6400-4950 varve years before present (vyr BP) using microfacies analyses, X-ray fluorescence scanning (µ-XRF), microscopic varve chronology and 14C dating. The microscopic varve chronology was compared to a macroscopic varve chronology for the same sediment interval. Calcite layer thickness during the recent period is significantly correlated to increases in local annual precipitation (r=0.46, p=0.03) and reduced air-pressure (r=-0.72, p<0.0001). Meteorologically consistent with enhanced precipitation at Lake Woserin, a composite 500 hPa anomaly map for years with >1 standard deviation calcite layer thickness depicts a negative wave train air-pressure anomaly centred over southern Europe, with north-eastern Germany at its northern frontal zone. Three centennial-scale intervals of thicker calcite layers around the Mid-Holocene periods 6200-5900, 5750-5400 and 5300-4950 vyr BP might reflect humid conditions favouring calcite precipitation through the transport of Ca2+ ions into Lake Woserin, synchronous to wetter conditions in Europe. Calcite layer thickness oscillations of about 88 and 208 years resemble the solar Gleissberg and Suess cycles suggesting that the recorded hydroclimate changes in north-eastern Germany are modified by solar influences on synoptic-scale atmospheric circulation. However, parts of the periods of thicker calcite layers around 5750-5400 and 5200 vyr BP also coincide to enhanced human catchment activity at Lake Woserin. Therefore, calcite precipitation during these time-windows might have further been favored by anthropogenic deforestation mobilizing Ca2+ ions and/or lake eutrophication.

Event-related potential effects of object repetition depend on attention and part-whole configuration

The effects of spatial attention and part-whole configuration on recognition of repeated objects were investigated with behavioral and event-related potential (ERP) measures. Short-term repetition effects were measured for probe objects as a function of whether a preceding prime object was shown as an intact image or coarsely scrambled (split into two halves) and whether or not it had been attended during the prime display. In line with previous behavioral experiments, priming effects were observed from both intact and split primes for attended objects, but only from intact (repeated same-view) objects when they were unattended. These behavioral results were reflected in ERP waveforms at occipital-temporal locations as more negative-going deflections for repeated items in the time window between 220 and 300 ms after probe onset (N250r). Attended intact images showed generally more enhanced repetition effects than split ones. Unattended images showed repetition effects only when presented in an intact configuration, and this finding was limited to the right-hemisphere electrodes. Repetition effects in earlier (before 200 ms) time-windows were limited to attended conditions at occipito-temporal sites (N1), a component linked to the encoding of object structure, while repetition effects at central locations during the same time window (P150) were found only from objects repeated in the same intact configuration—both previously attended and unattended probe objects. The data indicate that view-generalization is mediated by a combination of analytic (part-based) representations and automatic view-dependent representations.

O problema de alocação de berços: um estudo das heurísticas simulated annealing e algoritmo genético

Este trabalho apresenta um estudo de caso das heurísticas Simulated Annealing e Algoritmo Genético para um problema de grande relevância encontrado no sistema portuário, o Problema de Alocação em Berços. Esse problema aborda a programação e a alocação de navios às áreas de atracação ao longo de um cais. A modelagem utilizada nesta pesquisa é apresentada por Mauri (2008) [28] que trata do problema como uma Problema de Roteamento de Veículos com Múltiplas Garagens e sem Janelas de Tempo. Foi desenvolvido um ambiente apropriado para testes de simulação, onde o cenário de análise foi constituido a partir de situações reais encontradas na programação de navios de um terminal de contêineres. Os testes computacionais realizados mostram a performance das heurísticas em relação a função objetivo e o tempo computacional, a m de avaliar qual das técnicas apresenta melhores resultados.

Approximation Algorithms for Network Design and Orienteering

This thesis presents approximation algorithms for some NP-Hard combinatorial optimization problems on graphs and networks; in particular, we study problems related to Network Design. Under the widely-believed complexity-theoretic assumption that P is not equal to NP, there are no efficient (i.e., polynomial-time) algorithms that solve these problems exactly. Hence, if one desires efficient algorithms for such problems, it is necessary to consider approximate solutions: An approximation algorithm for an NP-Hard problem is a polynomial time algorithm which, for any instance of the problem, finds a solution whose value is guaranteed to be within a multiplicative factor of the value of an optimal solution to that instance. We attempt to design algorithms for which this factor, referred to as the approximation ratio of the algorithm, is as small as possible. The field of Network Design comprises a large class of problems that deal with constructing networks of low cost and/or high capacity, routing data through existing networks, and many related issues. In this thesis, we focus chiefly on designing fault-tolerant networks. Two vertices u,v in a network are said to be k-edge-connected if deleting any set of k − 1 edges leaves u and v connected; similarly, they are k-vertex connected if deleting any set of k − 1 other vertices or edges leaves u and v connected. We focus on building networks that are highly connected, meaning that even if a small number of edges and nodes fail, the remaining nodes will still be able to communicate. A brief description of some of our results is given below. We study the problem of building 2-vertex-connected networks that are large and have low cost. Given an n-node graph with costs on its edges and any integer k, we give an O(log n log k) approximation for the problem of finding a minimum-cost 2-vertex-connected subgraph containing at least k nodes. We also give an algorithm of similar approximation ratio for maximizing the number of nodes in a 2-vertex-connected subgraph subject to a budget constraint on the total cost of its edges. Our algorithms are based on a pruning process that, given a 2-vertex-connected graph, finds a 2-vertex-connected subgraph of any desired size and of density comparable to the input graph, where the density of a graph is the ratio of its cost to the number of vertices it contains. This pruning algorithm is simple and efficient, and is likely to find additional applications. Recent breakthroughs on vertex-connectivity have made use of algorithms for element-connectivity problems. We develop an algorithm that, given a graph with some vertices marked as terminals, significantly simplifies the graph while preserving the pairwise element-connectivity of all terminals; in fact, the resulting graph is bipartite. We believe that our simplification/reduction algorithm will be a useful tool in many settings. We illustrate its applicability by giving algorithms to find many trees that each span a given terminal set, while being disjoint on edges and non-terminal vertices; such problems have applications in VLSI design and other areas. We also use this reduction algorithm to analyze simple algorithms for single-sink network design problems with high vertex-connectivity requirements; we give an O(k log n)-approximation for the problem of k-connecting a given set of terminals to a common sink. We study similar problems in which different types of links, of varying capacities and costs, can be used to connect nodes; assuming there are economies of scale, we give algorithms to construct low-cost networks with sufficient capacity or bandwidth to simultaneously support flow from each terminal to the common sink along many vertex-disjoint paths. We further investigate capacitated network design, where edges may have arbitrary costs and capacities. Given a connectivity requirement R_uv for each pair of vertices u,v, the goal is to find a low-cost network which, for each uv, can support a flow of R_uv units of traffic between u and v. We study several special cases of this problem, giving both algorithmic and hardness results. In addition to Network Design, we consider certain Traveling Salesperson-like problems, where the goal is to find short walks that visit many distinct vertices. We give a (2 + epsilon)-approximation for Orienteering in undirected graphs, achieving the best known approximation ratio, and the first approximation algorithm for Orienteering in directed graphs. We also give improved algorithms for Orienteering with time windows, in which vertices must be visited between specified release times and deadlines, and other related problems. These problems are motivated by applications in the fields of vehicle routing, delivery and transportation of goods, and robot path planning.

Dendritic Cells for Anomaly Detection

Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human immune system. DCs perform the vital role of combining signals from the host tissue and correlate these signals with proteins known as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.

Nested Rollout Policy Adaptation for optimizing vehicle selection in complex VRPs

The goal of Vehicle Routing Problems (VRP) and their variations is to transport a set of orders with the minimum number of vehicles at least cost. Most approaches are designed to solve specific problem variations independently, whereas in real world applications, different constraints are handled concurrently. This research extends solutions obtained for the traveling salesman problem with time windows to a much wider class of route planning problems in logistics. The work describes a novel approach that:  supports a heterogeneous fleet of vehicles  dynamically reduces the number of vehicles  respects individual capacity restrictions  satisfies pickup and delivery constraints  takes Hamiltonian paths (rather than cycles) The proposed approach uses Monte-Carlo Tree Search and in particular Nested Rollout Policy Adaptation. For the evaluation of the work, real data from the industry was obtained and tested and the results are reported.

Conservadorismo e herança institucional de financiamento das companhias abertas do Brasil

Tese (doutorado)—Universidade de Brasília, Universidade Federal da Paraíba, Universidade Federal do Rio Grande do Norte, Programa Multi-Institucional e Inter-Regional de Pós-Graduação em Ciências Contábeis, 2015.

The deterministic Dendritic Cell Algorithm

The Dendritic Cell Algorithm is an immune-inspired algorithm originally based on the function of natural dendritic cells. The original instantiation of the algorithm is a highly stochastic algorithm. While the performance of the algorithm is good when applied to large real-time datasets, it is difficult to analyse due to the number of random-based elements. In this paper a deterministic version of the algorithm is proposed, implemented and tested using a port scan dataset to provide a controllable system. This version consists of a controllable amount of parameters, which are experimented with in this paper. In addition the effects are examined of the use of time windows and variation on the number of cells, both which are shown to influence the algorithm. Finally a novel metric for the assessment of the algorithms output is introduced and proves to be a more sensitive metric than the metric used with the original Dendritic Cell Algorithm.

A Ride-Sharing System with High Capacity Vehicles and Flexible Meeting Points

In many major cities, fixed route transit systems such as bus and rail serve millions of trips per day. These systems have people collect at common locations (the station or stop), and board at common times (for example according to a predetermined schedule or headway). By using common service locations and times, these modes can consolidate many trips that have similar origins and destinations or overlapping routes. However, the routes are not sensitive to changing travel patterns, and have no way of identifying which trips are going unserved, or are poorly served, by the existing routes. On the opposite end of the spectrum, personal modes of transportation, such as a private vehicle or taxi, offer service to and from the exact origin and destination of a rider, at close to exactly the time they desire to travel. Despite the apparent increased convenience to users, the presence of a large number of small vehicles results in a disorganized, and potentially congested road network during high demand periods. The focus of the research presented in this paper is to develop a system that possesses both the on-demand nature of a personal mode, with the efficiency of shared modes. In this system, users submit their request for travel, but are asked to make small compromises in their origin and destination location by walking to a nearby meeting point, as well as slightly modifying their time of travel, in order to accommodate other passengers. Because the origin and destination location of the request can be adjusted, this is a more general case of the Dial-a-Ride problem with time windows. The solution methodology uses a graph clustering algorithm coupled with a greedy insertion technique. A case study is presented using actual requests for taxi trips in Washington DC, and shows a significant decrease in the number of vehicles required to serve the demand.

Une heuristique de recherche à voisinage variable pour le problème du voyageur de commerce avec fenêtres de temps

Nous adaptons une heuristique de recherche à voisinage variable pour traiter le problème du voyageur de commerce avec fenêtres de temps (TSPTW) lorsque l'objectif est la minimisation du temps d'arrivée au dépôt de destination. Nous utilisons des méthodes efficientes pour la vérification de la réalisabilité et de la rentabilité d'un mouvement. Nous explorons les voisinages dans des ordres permettant de réduire l'espace de recherche. La méthode résultante est compétitive avec l'état de l'art. Nous améliorons les meilleures solutions connues pour deux classes d'instances et nous fournissons les résultats de plusieurs instances du TSPTW pour la première fois.

Une heuristique de recherche à voisinage variable pour le problème du voyageur de commerce avec fenêtres de temps

Nous adaptons une heuristique de recherche à voisinage variable pour traiter le problème du voyageur de commerce avec fenêtres de temps (TSPTW) lorsque l'objectif est la minimisation du temps d'arrivée au dépôt de destination. Nous utilisons des méthodes efficientes pour la vérification de la réalisabilité et de la rentabilité d'un mouvement. Nous explorons les voisinages dans des ordres permettant de réduire l'espace de recherche. La méthode résultante est compétitive avec l'état de l'art. Nous améliorons les meilleures solutions connues pour deux classes d'instances et nous fournissons les résultats de plusieurs instances du TSPTW pour la première fois.

Altered transcriptional and epigenetic regulation affects brain cells proliferation and differentiation in the ultra-rare genetic demyelinating disease AGC1 deficiency: a study on in vitro models

AGC1 deficiency is a rare demyelinating disease caused by mutations in the SLC25A12 gene, which encodes for the mitochondrial glutamate-aspartate carrier 1 (AGC1/Alarar), highly expressed in the central nervous system. In neurons, impairment in AGC1 activity leads to reduction in N-acetyl-aspartate, the main lipid precursor for myelin synthesis (Profilo et al., 2017); in oligodendrocytes progenitors cells, AGC1 down regulation has been related to early arrest proliferation and premature differentiation (Petralla et al., 2019). Additionally, in vivo AGC1 deficiency models i.e., heterozygous mice for AGC1 knock-out and neurospheres from their subventricular zone, respectively, showed a global decrease in cells proliferation and a switch in neural stem cells (NSCs) commitment, with specific reduction in OPCs number and increase in neural and astrocytic pools (Petralla et al., 2019). Therefore, the present study aims to investigate the transcriptional and epigenetic regulation underlying the alterations observed in OPCs and NSCs biological mechanisms, in either AGC1 deficiency models of Oli-neu cells (murine immortalized oligodendrocytes precursors cells), partially silenced by a shRNA for SLC25A12 gene, and SVZ-derived neurospheres from AGC1+/- mice. Western blot and immunofluorescence analysis revealed significant variations in the expression of transcription factors involved in brain cells’ proliferation and differentiation, in association with altered histone post-translational modifications, as well as histone acetylases (HATs) and deacetylases (HDACs) activity/expression, suggesting an improper transcriptional and epigenetic regulation affecting both AGC1 deficiency in vitro models. Furthermore, given the large role of acetylation in controlling in specific time-windows OPC maturation (Hernandez and Casaccia; 2015), pharmacological HATs/HDACs inhibitions were performed, confirming the involvement of chromatin remodelling enzymes in the altered proliferation and early differentiation observed in the AGC1 deficiency models of siAGC1 Oli-neu cells and AGC1+/- mice-derived neurospheres.

Smart freight transport in urban areas: a crowd-shipping model using autonomous vehicles

L'avanzamento dell'e-commerce e l'aumento della densità abitativa nel centro città sono elementi che incentivano l'incremento della richiesta merci all'interno dei centri urbani. L'attenzione all'impatto ambientale derivante da queste attività operative è un punto focale oggetto di sempre maggiore interesse. Attraverso il seguente studio, l'obiettivo è definire attuali e potenziali soluzioni nell'ambito della logistica urbana, con particolare interesse alle consegne dell'ultimo miglio. Una soluzione proposta riguarda la possibilità di sfruttare la capacità disponibile nei flussi generati dalla folla per movimentare merce, pratica nota sotto il nome di Crowd-shipping. L'idea consiste nella saturazione di mezzi già presenti nella rete urbana al fine di ridurre il numero di veicoli commerciali e minimizzare le esternalità negative annesse. A supporto di questa iniziativa, nell'analisi verranno considerati veicoli autonomi elettrici a guida autonoma. La tesi è incentrata sulla definizione di un modello di ottimizzazione matematica, che mira a designare un network logistico-distributivo efficiente per le consegne dell'ultimo miglio e a minimizzare le distanze degli attori coinvolti. Il problema proposto rappresenta una variante del Vehicle Routing Problem con time windows e multi depots. Il problema è NP-hard, quindi computazionalmente complesso per cui sarà necessario, in fase di analisi, definire un approccio euristico che permetterà di ottenere una soluzione sub-ottima in un tempo di calcolo ragionevole per istanze maggiori. L'analisi è stata sviluppata nell'ambiente di sviluppo Eclipse, attraverso il risolutore Cplex, in linguaggio Java. Per poterne comprendere la validità, è prevista un'ultima fase in cui gli output del modello ottimo e dell'euristica vengono confrontati tra loro su parametri caratteristici. Bisogna tuttavia considerare che l' utilizzo di sistemi cyber-fisici a supporto della logistica non può prescindere da un costante sguardo verso il progresso.

Recognition of human activities through eSteps insoles

Recognition of everyday human activity through mobile personal sensing technology plays a central role in the field of pervasive healthcare. The Bologna-based American company eSteps Inc. addresses the growing motor disability of the lower limbs by offering pre-, during and post-hospitalisation monitoring solutions with biomechanics and telerehabilitation protocol. It has developed a smart, customised and sustainable device to monitor motor activity, fatigue and injury risk for patients and a special app to share data with caregivers and medical specialists. The objective of this study is the development of an Artificial Intelligence model to recognize the activity performed by a person with Multiple Sclerosis or a healthy person through eSteps devices.