Generisches Simulationsmodell für Stückgutspeditionsanlagen auf Basis der Anforderungen von KMUs

Simulation can be used to plan and optimize less-than-truckload (LTL) terminals. To develop simulation models, specific expertise in this field is needed, which often requires high financial investments for acquisition of this knowledge. Due to limited financial resources, SMEs are often incapable to get to this expertise. The objective of the paper is to develop a generic model for LTL terminal planning that can be used without simulation expertise and that can be adapted to individual SME layouts. Therefore, based on focus group interviews with SMEs, a catalog of requirements is developed, including input variables and design criteria. Key performance indicators (KPIs) are defined to evaluate the results. A feasibility study for implementing a generic model based on the identified requirements is then performed. The implementation is done by modeling the I-layout of an LTL terminal

A biased-randomized simheuristic for a hybrid flow shop with stochastic processing times in the semiconductor industry

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksCompared to other industries, production systems in semiconductor manufacturing have an above-average level of complexity. Developments in recent decades document increasing product diversity, smaller batch sizes, and a rapidly changing product range. At the same time, the interconnections between equipment groups increase due to rising automation, thus making production planning and control more difficult. This paper discusses a hybrid flow shop problem with realistic constraints, such as stochastic processing times and priority constraints. The primary goal of this paper is to find a solution set (permutation of jobs) that minimizes the production makespan. The proposed algorithm extends our previous work by combining biased-randomization techniques with a discrete-event simulation heuristic. This simulation-optimization approach allows us to efficiently model dependencies caused by batching and by the existence of different flow paths. As shown in a series of numerical experiments, our methodology can achieve promising results even when stochastic processing times are considered.Peer ReviewedPostprint (author's final draft

A biased-randomized discrete-event algorithm for the hybrid flow shop problem with time dependencies and priority constraints

Based on a real-world application in the semiconductor industry, this article models and discusses a hybrid flow shop problem with time dependencies and priority constraints. The analyzed problem considers a production where a large number of heterogeneous jobs are processed by a number of machines. The route that each job has to follow depends upon its type, and, in addition, some machines require that a number of jobs are combined in batches before starting their processing. The hybrid flow model is also subject to a global priority rule and a “same setup” rule. The primary goal of this study was to find a solution set (permutation of jobs) that minimizes the production makespan. While simulation models are frequently employed to model these time-dependent flow shop systems, an optimization component is needed in order to generate high-quality solution sets. In this study, a novel algorithm is proposed to deal with the complexity of the underlying system. Our algorithm combines biased-randomization techniques with a discrete-event heuristic, which allows us to model dependencies caused by batching and different paths of jobs efficiently in a near-natural way. As shown in a series of numerical experiments, the proposed simulation-optimization algorithm can find solutions that significantly outperform those provided by employing state-of-the-art simulation software.Peer ReviewedPostprint (published version

HLA-DP on Epithelial Cells Enables Tissue Damage by NKp44+ Natural Killer Cells in Ulcerative Colitis

Background & aims: Ulcerative colitis (UC) is characterized by severe inflammation and destruction of the intestinal epithelium, and is associated with specific risk single nucleotide polymorphisms in HLA class II. Given the recently discovered interactions between subsets of HLA-DP molecules and the activating natural killer (NK) cell receptor NKp44, genetic associations of UC and HLA-DP haplotypes and their functional implications were investigated.Methods: HLA-DP haplotype and UC risk association analyses were performed (UC: n = 13,927; control: n = 26,764). Expression levels of HLA-DP on intestinal epithelial cells (IECs) in individuals with and without UC were quantified. Human intestinal 3-dimensional (3D) organoid cocultures with human NK cells were used to determine functional consequences of interactions between HLA-DP and NKp44.Results: These studies identified HLA-DPA1∗01:03-DPB1∗04:01 (HLA-DP401) as a risk haplotype and HLA-DPA1∗01:03-DPB1∗03:01 (HLA-DP301) as a protective haplotype for UC in European populations. HLA-DP expression was significantly higher on IECs of individuals with UC compared with controls. IECs in human intestinal 3D organoids derived from HLA-DP401pos individuals showed significantly stronger binding of NKp44 compared with HLA-DP301pos IECs. HLA-DP401pos IECs in organoids triggered increased degranulation and tumor necrosis factor production by NKp44+ NK cells in cocultures, resulting in enhanced epithelial cell death compared with HLA-DP301pos organoids. Blocking of HLA-DP401-NKp44 interactions (anti-NKp44) abrogated NK cell activity in cocultures.Conclusions: We identified an UC risk HLA-DP haplotype that engages NKp44 and activates NKp44+ NK cells, mediating damage to intestinal epithelial cells in an HLA-DP haplotype-dependent manner. The molecular interaction between NKp44 and HLA-DP401 in UC can be targeted by therapeutic interventions to reduce NKp44+ NK cell-mediated destruction of the intestinal epithelium in UC

HLA-DP on Epithelial Cells Enables Tissue Damage by NKp44+ Natural Killer Cells in Ulcerative Colitis

Background & aims: Ulcerative colitis (UC) is characterized by severe inflammation and destruction of the intestinal epithelium, and is associated with specific risk single nucleotide polymorphisms in HLA class II. Given the recently discovered interactions between subsets of HLA-DP molecules and the activating natural killer (NK) cell receptor NKp44, genetic associations of UC and HLA-DP haplotypes and their functional implications were investigated.Methods: HLA-DP haplotype and UC risk association analyses were performed (UC: n = 13,927; control: n = 26,764). Expression levels of HLA-DP on intestinal epithelial cells (IECs) in individuals with and without UC were quantified. Human intestinal 3-dimensional (3D) organoid cocultures with human NK cells were used to determine functional consequences of interactions between HLA-DP and NKp44.Results: These studies identified HLA-DPA1∗01:03-DPB1∗04:01 (HLA-DP401) as a risk haplotype and HLA-DPA1∗01:03-DPB1∗03:01 (HLA-DP301) as a protective haplotype for UC in European populations. HLA-DP expression was significantly higher on IECs of individuals with UC compared with controls. IECs in human intestinal 3D organoids derived from HLA-DP401pos individuals showed significantly stronger binding of NKp44 compared with HLA-DP301pos IECs. HLA-DP401pos IECs in organoids triggered increased degranulation and tumor necrosis factor production by NKp44+ NK cells in cocultures, resulting in enhanced epithelial cell death compared with HLA-DP301pos organoids. Blocking of HLA-DP401-NKp44 interactions (anti-NKp44) abrogated NK cell activity in cocultures.Conclusions: We identified an UC risk HLA-DP haplotype that engages NKp44 and activates NKp44+ NK cells, mediating damage to intestinal epithelial cells in an HLA-DP haplotype-dependent manner. The molecular interaction between NKp44 and HLA-DP401 in UC can be targeted by therapeutic interventions to reduce NKp44+ NK cell-mediated destruction of the intestinal epithelium in UC

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment