Flexible Job-Shop Scheduling with Batching for Semiconductor Manufacturing

International audienceScheduling decisions in the diffusion and cleaning area of a semiconductor manufacturing facilityhave an important impact on the overall performance of a plant. Consequently, we wantto optimize those decisions while taking real-world constraints into account. An importantproperty of machines in this work area is their batching capability: They can perform multipleoperations at the same time. We want to take account of this in our algorithm.We need to schedule a given set of jobs. For each of them, a fixed sequence of operations mustbe performed. This sequence is called the route of the job. Operations can only be performed onqualified machines and their processing durations depend on the selected machine. A capacitylimit constrains the number of jobs that can be processed per batch. Each operation is assignedto a family and only operations of the same family can be combined in the same batch. Foreach job, we are given a ready date and a due date. Those constraints describe a flexiblejob-shop scheduling problem with batching. We aim to minimize total weighted tardiness.For the described problem, we present a simulated annealing algorithm that is based on anextended evaluation of disjunctive graphs. In our proposed approach, batching decisions aretaken dynamically during graph traversal

Efficient Error-Correcting Geocoding

We study the problem of resolving a perhaps misspelled address of a location into geographic coordinates of latitude and longitude. Our data structure solves this problem within a few milliseconds even for misspelled and fragmentary queries. Compared to major geographic search engines such as Google or Bing we achieve results of significantly better quality

A Scheduling Perspective on Modular Educational Systems in Europe

In modular educational systems, students are allowed to choose a part of their own curriculum themselves. This is typically done in the final class levels which lead to maturity for university access. The rationale behind letting students choose their courses themselves is to enhance self-responsibility, improve student motivation, and allow a focus on specific areas of interest. A central instrument for bringing these systems to fruition is the timetable. However, scheduling the timetable in such systems can be an extremely challenging and time-consuming task. In this study, we present a framework for classifying modular educational systems in Europe that reflects different degrees of freedom regarding student choices, and explore the consequences from the perspective of scheduling a timetable that satisfies all requirements from the organizational and the pedagogical perspective. For this purpose, we conducted interviews in Austria, Germany, Finland, Switzerland, the Netherlands, and Luxembourg and apply the framework to these educational systems, finding that among them the Finnish system shows the highest degree of modularity. After analyzing the consequences of modularity from the scheduling perspective, we assess the necessity for automated scheduling methods, which are central for realizing the potential and many benefits of modular education in practice.Comment: Preprint submitted to International Journal of Educational Researc

A cadaver‑based biomechanical model of acetabulum reaming for surgical virtual reality training simulators

Total hip arthroplasty (THA) is a highly successful surgical procedure, but complications remain, including aseptic loosening, early dislocation and misalignment. These may partly be related to lacking training opportunities for novices or those performing THA less frequently. A standardized training setting with realistic haptic feedback for THA does not exist to date. Virtual Reality (VR) may help establish THA training scenarios under standardized settings, morphology and material properties. This work summarizes the development and acquisition of mechanical properties on hip reaming, resulting in a tissue-based material model of the acetabulum for force feedback VR hip reaming simulators. With the given forces and torques occurring during the reaming, Cubic Hermite Spline interpolation seemed the most suitable approach to represent the nonlinear forcedisplacement behavior of the acetabular tissues over Cubic Splines. Further, Cubic Hermite Splines allowed for a rapid force feedback computation below the 1 ms hallmark. The Cubic Hermite Spline material model was implemented using a three-dimensional-sphere packing model. The resulting forces were delivered via a human–machine-interaction certified KUKA iiwa robotic arm used as a force feedback device. Consequently, this novel approach presents a concept to obtain mechanical data from high-force surgical interventions as baseline data for material models and biomechanical considerations; this will allow THA surgeons to train with a variety of machining hardness levels of acetabula for haptic VR acetabulum reaming

A new proof of the Vorono\"i summation formula

We present a short alternative proof of the Vorono\"i summation formula which plays an important role in Dirichlet's divisor problem and has recently found an application in physics as a trace formula for a Schr\"odinger operator on a non-compact quantum graph \mathfrak{G} [S. Egger n\'e Endres and F. Steiner, J. Phys. A: Math. Theor. 44 (2011) 185202 (44pp)]. As a byproduct we give a new proof of a non-trivial identity for a particular Lambert series which involves the divisor function d(n) and is identical with the trace of the Euclidean wave group of the Laplacian on the infinite graph \mathfrak{G}.Comment: Enlarged version of the published article J. Phys. A: Math. Theor. 44 (2011) 225302 (11pp

D5.1. Intermediate report on user needs, SLICES services catalogue, access policies and training strategy

This deliverable is an intermediate report of the workpackage on user needs, services, access and training strategy. It presents the current view that will be further consolidated in Deliverable D5.2 “Final report on user needs, SLICES service catalog, access policies and training strategy” (M40, December 2025) and that will provide the final view on these topics. This deliverable starts by dealing with user needs by describing a methodology for identification of them. By applying it, three first blueprints have been identified and are presented: the “post-5G” blueprint, the “cloud/edge” blueprint, and the "machine learning/federated learning" blueprint. The second section of the deliverable recalls the three access types (trans-national/physical access, trans-national virtual access/remote access, and virtual access) and the three access modes (excellence-driven, market-driven, and wide access) that will be supported by SLICES. The third section of the deliverable deals with the current vision of SLICES services catalogue. Services have been further divided into supporting and basic services. As a consequence of blueprints, a new category of services, SLICES Blueprint services, have been introduced. It aims at gathering the services provided by the blueprints, i.e., services specific to a particular research community. It is important to notice that this part also considered services from a pre-operation point of view and therefore also contains implementation considerations. This work has been carried out in collaboration with relevant workpackages: WP3 “Scientific and technical strategy and specifications”, WP6 “Operational framework”, and WP7 “Data management and ethics requirements”. The fourth section focus on training activities in particular with respect to four objectives: i) to identify the training needs and training methodologies that will be followed; ii) to develop and provide the respective training material to organize SLICES-RI training events (training sessions, webinars, plugfests, hackathons) as well as shared teaching material used in SLICESRI for teaching basic skills at the convergence of computing and networking (SLICES Academy); iii) to provide guidelines, inter-site collaboration incentives and alignment with national programs pertaining to the teaching of key technical skills in the areas of interest of SLICES-RI (SLICES Academy); and iv) to facilitate researcher mobility, among the SLICES-RI member institutions and for the research community at large, for the exchange of know-how among the users of the facilities

Biological Earth observation with animal sensors

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change

Detailed stratified GWAS analysis for severe COVID-19 in four European populations

Given the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), a deeper analysis of the host genetic contribution to severe COVID-19 is important to improve our understanding of underlying disease mechanisms. Here, we describe an extended genome-wide association meta-analysis of a well-characterized cohort of 3255 COVID-19 patients with respiratory failure and 12 488 population controls from Italy, Spain, Norway and Germany/Austria, including stratified analyses based on age, sex and disease severity, as well as targeted analyses of chromosome Y haplotypes, the human leukocyte antigen region and the SARS-CoV-2 peptidome. By inversion imputation, we traced a reported association at 17q21.31 to a ~0.9-Mb inversion polymorphism that creates two highly differentiated haplotypes and characterized the potential effects of the inversion in detail. Our data, together with the 5th release of summary statistics from the COVID-19 Host Genetics Initiative including non-Caucasian individuals, also identified a new locus at 19q13.33, including NAPSA, a gene which is expressed primarily in alveolar cells responsible for gas exchange in the lung.S.E.H. and C.A.S. partially supported genotyping through a philanthropic donation. A.F. and D.E. were supported by a grant from the German Federal Ministry of Education and COVID-19 grant Research (BMBF; ID:01KI20197); A.F., D.E. and F.D. were supported by the Deutsche Forschungsgemeinschaft Cluster of Excellence ‘Precision Medicine in Chronic Inflammation’ (EXC2167). D.E. was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the Computational Life Sciences funding concept (CompLS grant 031L0165). D.E., K.B. and S.B. acknowledge the Novo Nordisk Foundation (NNF14CC0001 and NNF17OC0027594). T.L.L., A.T. and O.Ö. were funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project numbers 279645989; 433116033; 437857095. M.W. and H.E. are supported by the German Research Foundation (DFG) through the Research Training Group 1743, ‘Genes, Environment and Inflammation’. L.V. received funding from: Ricerca Finalizzata Ministero della Salute (RF-2016-02364358), Italian Ministry of Health ‘CV PREVITAL’—strategie di prevenzione primaria cardiovascolare primaria nella popolazione italiana; The European Union (EU) Programme Horizon 2020 (under grant agreement No. 777377) for the project LITMUS- and for the project ‘REVEAL’; Fondazione IRCCS Ca’ Granda ‘Ricerca corrente’, Fondazione Sviluppo Ca’ Granda ‘Liver-BIBLE’ (PR-0391), Fondazione IRCCS Ca’ Granda ‘5permille’ ‘COVID-19 Biobank’ (RC100017A). A.B. was supported by a grant from Fondazione Cariplo to Fondazione Tettamanti: ‘Bio-banking of Covid-19 patient samples to support national and international research (Covid-Bank). This research was partly funded by an MIUR grant to the Department of Medical Sciences, under the program ‘Dipartimenti di Eccellenza 2018–2022’. This study makes use of data generated by the GCAT-Genomes for Life. Cohort study of the Genomes of Catalonia, Fundació IGTP (The Institute for Health Science Research Germans Trias i Pujol) IGTP is part of the CERCA Program/Generalitat de Catalunya. GCAT is supported by Acción de Dinamización del ISCIII-MINECO and the Ministry of Health of the Generalitat of Catalunya (ADE 10/00026); the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) (2017-SGR 529). M.M. received research funding from grant PI19/00335 Acción Estratégica en Salud, integrated in the Spanish National RDI Plan and financed by ISCIII-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (European Regional Development Fund (FEDER)-Una manera de hacer Europa’). B.C. is supported by national grants PI18/01512. X.F. is supported by the VEIS project (001-P-001647) (co-funded by the European Regional Development Fund (ERDF), ‘A way to build Europe’). Additional data included in this study were obtained in part by the COVICAT Study Group (Cohort Covid de Catalunya) supported by IsGlobal and IGTP, European Institute of Innovation & Technology (EIT), a body of the European Union, COVID-19 Rapid Response activity 73A and SR20-01024 La Caixa Foundation. A.J. and S.M. were supported by the Spanish Ministry of Economy and Competitiveness (grant numbers: PSE-010000-2006-6 and IPT-010000-2010-36). A.J. was also supported by national grant PI17/00019 from the Acción Estratégica en Salud (ISCIII) and the European Regional Development Fund (FEDER). The Basque Biobank, a hospital-related platform that also involves all Osakidetza health centres, the Basque government’s Department of Health and Onkologikoa, is operated by the Basque Foundation for Health Innovation and Research-BIOEF. M.C. received Grants BFU2016-77244-R and PID2019-107836RB-I00 funded by the Agencia Estatal de Investigación (AEI, Spain) and the European Regional Development Fund (FEDER, EU). M.R.G., J.A.H., R.G.D. and D.M.M. are supported by the ‘Spanish Ministry of Economy, Innovation and Competition, the Instituto de Salud Carlos III’ (PI19/01404, PI16/01842, PI19/00589, PI17/00535 and GLD19/00100) and by the Andalussian government (Proyectos Estratégicos-Fondos Feder PE-0451-2018, COVID-Premed, COVID GWAs). The position held by Itziar de Rojas Salarich is funded by grant FI20/00215, PFIS Contratos Predoctorales de Formación en Investigación en Salud. Enrique Calderón’s team is supported by CIBER of Epidemiology and Public Health (CIBERESP), ‘Instituto de Salud Carlos III’. J.C.H. reports grants from Research Council of Norway grant no 312780 during the conduct of the study. E.S. reports grants from Research Council of Norway grant no. 312769. The BioMaterialBank Nord is supported by the German Center for Lung Research (DZL), Airway Research Center North (ARCN). The BioMaterialBank Nord is member of popgen 2.0 network (P2N). P.K. Bergisch Gladbach, Germany and the Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany. He is supported by the German Federal Ministry of Education and Research (BMBF). O.A.C. is supported by the German Federal Ministry of Research and Education and is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—CECAD, EXC 2030–390661388. The COMRI cohort is funded by Technical University of Munich, Munich, Germany. This work was supported by grants of the Rolf M. Schwiete Stiftung, the Saarland University, BMBF and The States of Saarland and Lower Saxony. K.U.L. is supported by the German Research Foundation (DFG, LU-1944/3-1). Genotyping for the BoSCO study is funded by the Institute of Human Genetics, University Hospital Bonn. F.H. was supported by the Bavarian State Ministry for Science and Arts. Part of the genotyping was supported by a grant to A.R. from the German Federal Ministry of Education and Research (BMBF, grant: 01ED1619A, European Alzheimer DNA BioBank, EADB) within the context of the EU Joint Programme—Neurodegenerative Disease Research (JPND). Additional funding was derived from the German Research Foundation (DFG) grant: RA 1971/6-1 to A.R. P.R. is supported by the DFG (CCGA Sequencing Centre and DFG ExC2167 PMI and by SH state funds for COVID19 research). F.T. is supported by the Clinician Scientist Program of the Deutsche Forschungsgemeinschaft Cluster of Excellence ‘Precision Medicine in Chronic Inflammation’ (EXC2167). C.L. and J.H. are supported by the German Center for Infection Research (DZIF). T.B., M.M.B., O.W. und A.H. are supported by the Stiftung Universitätsmedizin Essen. M.A.-H. was supported by Juan de la Cierva Incorporacion program, grant IJC2018-035131-I funded by MCIN/AEI/10.13039/501100011033. E.C.S. is supported by the Deutsche Forschungsgemeinschaft (DFG; SCHU 2419/2-1).Peer reviewe