Dynamic positioning of idle automated guided vehicles

An automated guided vehicle (AGV) is a mobile robot commonly used to carry loads in material handling systems (MHS). Once a transfer is completed, an AGV stops at a home position, a point where it can park until it is assigned a new task. Determining the home positions is an important control problem with a direct influence on the overall performance of the MHS. The problem can be viewed as a location-allocation problem on a network. In this paper two fast and effective heuristics which dynamically determine the home positions are proposed. The methods were tested using two real-world instances. The obtained results are shown and discussed.     

A mixed-Weibull regression model for the analysis of automotive warranty data

This paper presents a case study regarding the reliability analysis of some automotive components based on field failure warranty data. The components exhibit two different failure modes, namely early and wearout failures, and are mounted on different vehicles, which differ among themselves for car model and engine type, thus involving different operating conditions. Hence, the failure time of each component is a random variable with a bimodal pdf which also depends upon a vector of covariates that indexes the specific operating condition. Then, a mixed-Weibull distribution, where the pdf of each subpopulation (namely the ‘weak’ and ‘strong’ subpopulation) depends on the covariates through the scale parameter, is used to analyze the component lifetime. A Fortran algorithm for the maximum likelihood estimation of model parameters has been implemented and a stepwise procedure, in its backwards version, has been used to test the significance of covariates and to construct the regression model. The presence of a weak subpopulation has been verified and the fraction of weak units in the population has also been estimated. Finally, the adequacy of the proposed model to fit the observed data has been assessed.     

High Dynamic Range Point Clouds for Real‐Time Relighting

Acquired 3D point clouds make possible quick modeling of virtual scenes from the real world. With modern 3D capture pipelines, each point sample often comes with additional attributes such as normal vector and color response. Although rendering and processing such data has been extensively studied, little attention has been devoted using the light transport hidden in the recorded per‐sample color response to relight virtual objects in visual effects (VFX) look‐dev or augmented reality (AR) scenarios. Typically, standard relighting environment exploits global environment maps together with a collection of local light probes to reflect the light mood of the real scene on the virtual object. We propose instead a unified spatial approximation of the radiance and visibility relationships present in the scene, in the form of a colored point cloud. To do so, our method relies on two core components: High Dynamic Range (HDR) expansion and real‐time Point‐Based Global Illumination (PBGI). First, since an acquired color point cloud typically comes in Low Dynamic Range (LDR) format, we boost it using a single HDR photo exemplar of the captured scene that can cover part of it. We perform this expansion efficiently by first expanding the dynamic range of a set of renderings of the point cloud and then projecting these renderings on the original cloud. At this stage, we propagate the expansion to the regions not covered by the renderings or with low‐quality dynamic range by solving a Poisson system. Then, at rendering time, we use the resulting HDR point cloud to relight virtual objects, providing a diffuse model of the indirect illumination propagated by the environment. To do so, we design a PBGI algorithm that exploits the GPU's geometry shader stage as well as a new mipmapping operator, tailored for G‐buffers, to achieve real‐time performances. As a result, our method can effectively relight virtual objects exhibiting diffuse and glossy physically‐based materials in real time. Furthermore, it accounts for the spatial embedding of the object within the 3D environment. We evaluate our approach on manufactured scenes to assess the error introduced at every step from the perfect ground truth. We also report experiments with real captured data, covering a range of capture technologies, from active scanning to multiview stereo reconstruction.     

Ejection chain moves for automatic neighborhood synthesis in constrained cardinality‐minimization problems

In this paper, we deal with the problem of automatically synthesizing “good” neighborhoods for a specific class of problems, namely constrained cardinality‐minimization problems. Exploiting the peculiarity of the objective function of such problems, we develop automatic ejection chain moves that define neighborhood structures to be explored with a black‐box solver. In particular, starting from a formulation of a cardinality‐minimization problem and a feasible solution, our procedure automatically detects the “entities” involved in the problem and learns the strength of the relationships among them. This information is then used to define the characteristics of our moves that consist in ejecting one entity at a time from the solution. If one of such moves results in an infeasible solution, then feasibility is recovered by performing an additional step based on the solution of an auxiliary problem. The computational results show that, when assessed on four well‐known constrained cardinality‐minimization problems, our approach outperforms both a black‐box mixed integer programming solver and a state‐of‐the‐art model‐based neighborhood search procedure with respect to both solution quality and computing times.     

A learn‐and‐construct framework for general mixed‐integer programming problems

In this paper, we propose a new framework for finding an initial feasible solution from a mixed‐integer programming (MIP) model. We call it learn‐and‐construct since it first exploits the structure of the model and its linear relaxation solution and then uses this knowledge to try to produce a feasible solution. In the learning phase, we use an unsupervised learning algorithm to cluster entities originating the MIP model. Such clusters are then used to decompose the original MIP in a number of easier sub‐MIPs that are solved by using a black box solver. Computational results on three well‐known problems show that our procedure is characterized by a success rate larger than both the feasibility pump heuristic and a state‐of‐the‐art MIP solver. Furthermore, our approach is more scalable and uses less computing time on average.     

Enhanced Visualization of Detected 3D Geometric Differences

The wide availability of 3D acquisition devices makes viable their use for shape monitoring. The current techniques for the analysis of time‐varying data can efficiently detect actual significant geometric changes and rule out differences due to irrelevant variations (such as sampling, lighting and coverage). On the other hand, the effective visualization of such detected changes can be challenging when we want to show at the same time the original appearance of the 3D model. In this paper, we propose a dynamic technique for the effective visualization of detected differences between two 3D scenes. The presented approach, while retaining the original appearance, allows the user to switch between the two models in a way that enhances the geometric differences that have been detected as significant. Additionally, the same technique is able to visually hides the other negligible, yet visible, variations. The main idea is to use two distinct screen space time‐based interpolation functions for the significant 3D differences and for the small variations to hide. We have validated the proposed approach in a user study on a different class of datasets, proving the objective and subjective effectiveness of the method.     

Rolling-horizon and fix-and-relax heuristics for the parallel machine lot-sizing and scheduling problem with sequence-dependent set-up costs

In this paper we develop new rolling-horizon and fix-and-relax heuristics for the identical parallel machine lot-sizing and scheduling problem with sequence-dependent set-up costs. Unlike previous papers, our procedures are based on a compact formulation relying on the hypotheses of identical machines. This feature makes our approach suitable for large-scale applications (with hundreds of machines) arising in the textile and fiberglass industries. Moreover, our procedures are shown to provide a feasible solution for any feasible instance. Comparisons with lower bounds provided by a truncated branch-and-bound show that the gap between the best heuristic solution and the lower bound never exceeds 3%.     

Experimental investigation of the heat exchange to a compartmentalized plug flow

A two-phase (air–water), tubular counterflow heat exchanger was developed for industrial unitary process applications to realize an active heat transfer control. A controlled train of air plugs was injected in a stratified smooth water flow to yield a compartmentalized, continuous two-phase flow whose stability was carefully monitored. Related Nusselt numbers were evaluated for single- and two-phase flows, at the same flow regimes. This comparison shows how the traveling, controlled air plug “turbulators” allowed for a large heat transfer rate increase, for all observed heating rates and phase velocity ratios. The analysis includes a comparison with the available literature correlations, and the efficiency of the adopted heat transfer control is finally inferred while a range of flow regimes is identified which optimizes the heat exchange.     

Mast Cell Cytokines in Acute and Chronic Gingival Tissue Inflammation: Role of IL-33 and IL-37

Much evidence suggests autoimmunity in the etiopathogenesis of periodontal disease. In fact, in periodontitis, there is antibody production against collagen, DNA, and IgG, as well as increased IgA expression, T cell dysfunction, high expression of class II MHC molecules on the surface of gingival epithelial cells in inflamed tissues, activation of NK cells, and the generation of antibodies against the azurophil granules of polymorphonuclear leukocytes. In general, direct activation of autoreactive immune cells and production of TNF can activate neutrophils to release pro-inflammatory enzymes with tissue damage in the gingiva. Gingival inflammation and, in the most serious cases, periodontitis, are mainly due to the dysbiosis of the commensal oral microbiota that triggers the immune system. This inflammatory pathological state can affect the periodontal ligament, bone, and the entire gingival tissue. Oral tolerance can be abrogated by some cytokines produced by epithelial cells and activated immune cells, including mast cells (MCs). Periodontal cells and inflammatory–immune cells, including mast cells (MCs), produce cytokines and chemokines, mediating local inflammation of the gingival, along with destruction of the periodontal ligament and alveolar bone. Immune-cell activation and recruitment can be induced by inflammatory cytokines, such as IL-1, TNF, IL-33, and bacterial products, including lipopolysaccharide (LPS). IL-1 and IL-33 are pleiotropic cytokines from members of the IL-1 family, which mediate inflammation of MCs and contribute to many key features of periodontitis and other inflammatory disorders. IL-33 activates several immune cells, including lymphocytes, Th2 cells, and MCs in both innate and acquired immunological diseases. The classic therapies for periodontitis include non-surgical periodontal treatment, surgery, antibiotics, anti-inflammatory drugs, and surgery, which have been only partially effective. Recently, a natural cytokine, IL-37, a member of the IL-1 family and a suppressor of IL-1b, has received considerable attention for the treatment of inflammatory diseases. In this article, we report that IL-37 may be an important and effective therapeutic cytokine that may inhibit periodontal inflammation. The purpose of this paper is to study the relationship between MCs, IL-1, IL-33, and IL-37 inhibition in acute and chronic inflamed gingival tissue.