Data quality problems in discrete event simulation of manufacturing operations

High-quality input data are a necessity for successful discrete event simulation (DES) applications, and there are available methodologies for data collection in DES projects. However, in contrast to standalone projects, using DES as a daily manufacturing engineering tool requires high-quality production data to be constantly available. In fact, there has been a major shift in the application of DES in manufacturing from production system design to daily operations, accompanied by a stream of research on automation of input data management and interoperability between data sources and simulation models. Unfortunately, this research stream rests on the assumption that the collected data are already of high quality,and there is a lack of in-depth understanding of simulation data quality problems from a practitioners’ perspective.Therefore, a multiple-case study within the automotive industry was used to provide empirical descriptions of simulation data quality problems, data production processes, and relations between these processes and simulation data quality problems. These empirical descriptions are necessary to extend the present knowledge on data quality in DES in a practical real-world manufacturing context, which is a prerequisite for developing practical solutions for solving data quality problems such as limited accessibility, lack of data on minor stoppages, and data sources not being designed for simulation. Further, the empirical and theoretical knowledge gained throughout the study was used to propose a set of practical guidelines that can support manufacturing companies in improving data quality in DES

Technical note: Optimization functions for re‐irradiation treatment planning

Background Although re-irradiation is increasingly used in clinical practice, almost no dedicated planning software exists. Purpose Standard dose-based optimization functions were adjusted for re-irradiation planning using accumulated equivalent dose in 2-Gy fractions (EQD2) with rigid or deformable dose mapping, tissue-specific α/β, treatment-specific recovery coefficients, and voxelwise adjusted EQD2 penalization levels based on the estimated previously delivered EQD2 (EQD2deliv). Methods To demonstrate proof-of-concept, 35 Gy in 5 fractions was planned to a fictitious spherical relapse planning target volume (PTV) in three separate locations following previous prostate treatment on a virtual human phantom. The PTV locations represented one repeated irradiation scenario and two re-irradiation scenarios. For each scenario, three re-planning strategies with identical PTV dose-functions but various organ at risk (OAR) EQD2-functions was used: 1) reRTregular: Regular functions with fixed EQD2 penalization levels larger than EQD2deliv for all OAR voxels. 2) reRTreduce: As reRTregular, but with lower fixed EQD2 penalization levels aiming to reduce OAR EQD2. 3) reRTvoxelwise: As reRTregular and reRTreduce, but with voxelwise adjusted EQD2 penalization levels based on EQD2deliv. PTV near-minimum and near-maximum dose (D98%/D2%), homogeneity index (HI), conformity index (CI) and accumulated OAR EQD2 (α/β = 3 Gy) were evaluated. Results For the repeated irradiation scenario, all strategies resulted in similar dose distributions. For the re-irradiation scenarios, reRTreduce and reRTvoxelwise reduced accumulated average and near-maximum EQD2 by ˜1–10 Gy for all relevant OARs compared to reRTregular. The reduced OAR doses for reRTreduce came at the cost of distorted dose distributions with D98% = 92.3%, HI = 12.0%, CI = 73.7% and normal tissue hot spots ≥150% for the most complex scenario, while reRTregular (D98% = 98.1%, HI = 3.2%, CI = 94.2%) and reRTvoxelwise (D98% = 96.9%, HI = 6.1%, CI = 93.7%) fulfilled PTV coverage without hot spots. Conclusions The proposed re-irradiation-specific EQD2-based optimization functions introduce novel planning possibilities with flexible options to guide the trade-off between target coverage and OAR sparing with voxelwise adapted penalization levels based on EQD2deliv

The Future of Maintenance Within Industry 4.0: An Empirical Research in Manufacturing

Part 1: Lean and Green ManufacturingInternational audienceThe recent advances in digital technologies are revolutionizing the industrial landscape. Maintenance is one of the functions that may benefit from the opportunities that emerge with the digital transformation of industrial processes. Nevertheless, until now very few research papers investigated on what digitalized manufacturing entails for maintenance organizations along both technical and social dimensions. The aim of this paper is to investigate the vision of the future of Maintenance within the industry 4.0 and to show empirical evidence on how manufacturing companies are approaching the digital transformation process of maintenance. An empirical investigation was developed through multiple case-study involving nine manufacturing companies in Italy. Findings emerge about the main perceived challenges by companies for the success of digital transformation of maintenance as well as the technological and organizational mechanisms that are used in ongoing innovative Maintenance projects

Brain Re-Irradiation Robustly Accounting for Previously Delivered Dose

(1) Background: The STRIDeR (Support Tool for Re-Irradiation Decisions guided by Radiobiology) planning pathway aims to facilitate anatomically appropriate and radiobiologically meaningful re-irradiation (reRT). This work evaluated the STRIDeR pathway for robustness compared to a more conservative manual pathway. (2) Methods: For ten high-grade glioma reRT patient cases, uncertainties were applied and cumulative doses re-summed. Geometric uncertainties of 3, 6 and 9 mm were applied to the background dose, and LQ model robustness was tested using α/β variations (values 1, 2 and 5 Gy) and the linear quadratic linear (LQL) model δ variations (values 0.1 and 0.2). STRIDeR robust optimised plans, incorporating the geometric and α/β uncertainties during optimisation, were also generated. (3) Results: The STRIDeR and manual pathways both achieved clinically acceptable plans in 8/10 cases but with statistically significant improvements in the PTV D98% (p < 0.01) for STRIDeR. Geometric and LQ robustness tests showed comparable robustness within both pathways. STRIDeR plans generated to incorporate uncertainties during optimisation resulted in a superior plan robustness with a minimal impact on PTV dose benefits. (4) Conclusions: Our results indicate that STRIDeR pathway plans achieved a similar robustness to manual pathways with improved PTV doses. Geometric and LQ model uncertainties can be incorporated into the STRIDeR pathway to facilitate robust optimisation

The Journey Towards World Class Maintenance with Profit Loss Indicator

To have a maintenance function in the company that ensures a competitive advantage in the world market requires the world class maintenance (WCM). Though several different periods in history, maintenance has shifted from reactive maintenance fixing it when it breaks towards more systematic analysis techniques in terms of root cause analysis. With the onset of digitalisation and the breakthrough technologies in from Industry 4.0 more advanced analytics are expected in WCM. In particular the indicator profit loss indicator (PLI) has shown promising results in measuring e.g. time losses in production in a monetary term. Further, this indicator has also been proposed to be included in predictive maintenance. However, it is not pointed out clearly which role PLI will have in WCM. The aim of this article is therefore to investigate the trends of WCM as well as how PLI can be included in this journey

Operator 4.0 - Emerging Job Categories in Manufacturing

With the trends of industry 4.0 and increased degree of digitalization in production plants, it is expected that production plants in future is much more adaptive where they can both self-optimize production parameters as well as self-maintain of standard activities. All though this would reduce manual operations, new work activities are expected in a cyber-physical production plant. For instance, the establishment of digital twins in cloud solutions enabled with Internet of Things (IoT) can result in crafts in maintenance analytics as well as more guided maintenance for the maintenance operator with augmented reality. In addition, more service from external personnel such as the machine builder is expected to be offered in Industry 4.0. In overall, it will be of interest to identify and recommend qualification criteria relevant for a cyber physical production plant that would be implemented in the organisation. The aim of this article is to evaluate the role of operator as well as other relevant job categories in a cyber physical production plant. The result in this paper is a recommended framework with qualification criteria of these job categories. Further research will require more case studies of this framework

A framework for inverse planning of beam-on times for 3D small animal radiotherapy using interactive multi-objective optimisation

Advances in precision small animal radiotherapy hardware enable the delivery of increasingly complicated dose distributions on the millimeter scale. Manual creation and evaluation of treatment plans becomes difficult or even infeasible with an increasing number of degrees of freedom for dose delivery and available image data. The goal of this work is to develop an optimisation model that determines beam-on times for a given beam configuration, and to assess the feasibility and benefits of an automated treatment planning system for small animal radiotherapy. The developed model determines a Pareto optimal solution using operator-defined weights for a multiple-objective treatment planning problem. An interactive approach allows the planner to navigate towards, and to select the Pareto optimal treatment plan that yields the most preferred trade-off of the conflicting objectives. This model was evaluated using four small animal cases based on cone-beam computed tomography images. Resulting treatment plan quality was compared to the quality of manually optimised treatment plans using dose-volume histograms and metrics. Results show that the developed framework is well capable of optimising beam-on times for 3D dose distributions and offers several advantages over manual treatment plan optimisation. For all cases but the simple flank tumour case, a similar amount of time was needed for manual and automated beam-on time optimisation. In this time frame, manual optimisation generates a single treatment plan, while the inverse planning system yields a set of Pareto optimal solutions which provides quantitative insight on the sensitivity of conflicting objectives. Treatment planning automation decreases the dependence on operator experience and allows for the use of class solutions for similar treatment scenarios. This can shorten the time required for treatment planning and therefore increase animal throughput. In addition, this can improve treatment standardisation and comparability of research data within studies and among different institutes

A Simple Indicator Based Evolutionary Algorithm for Set-Based Minmax Robustness

For multiobjective optimization problems with uncertain parameters in the objective functions, different variants of minmax robustness concepts have been defined in the literature. The idea of minmax robustness is to optimize in the worst case such that the solutions have the best objective function values even when the worst case happens. However, the computation of the minmax robust Pareto optimal solutions remains challenging. This paper proposes a simple indicator based evolutionary algorithm for robustness (SIBEA-R) to address this challenge by computing a set of non-dominated set-based minmax robust solutions. In SIBEA-R, we consider the set of objective function values in the worst case of each solution. We propose a set-based non-dominated sorting to compare the objective function values using the definition of lower set less order for set-based dominance. We illustrate the usage of SIBEA-R with two example problems. In addition, utilization of the computed set of solutions with SIBEA-R for decision making is also demonstrated. The SIBEA-R method shows significant promise for finding non-dominated set-based minmax robust solutions.peerReviewe