Angel A. Juan, Pedro Copado, Javier Panadero, Christoph Laroque, Rocio de la Torre • Winter Simulation Conference, Orlando (USA)

Modern Manufacturing, known as Industrial Internet or Industry 4.0, is more than ever determined by customer-specific products, that are to be manufactured and delivered in given lead times and due-dates. Many of these manufacturing systems can be modeled as flow-shops where some of the processes can handle jobs on parallel machines. In addition, complex manufacturing environments contain specific machine loops or re-entry cycles where jobs might re-enter specific processes at some point of the flow-shop chain. A specific server is assigned to a job the first time it visits a machine, and it is quite usual that this job has to be processed by exactly the same server if it re-visits the machine due to quality issues. With the goal of minimizing the makespan, this paper analyzes this complex flow-shop setting and proposes an original discrete-event heuristic for solving it in short computing times. Our algorithm combines biased (non-uniform) randomization strategies with the use of a discrete-event list, which iteratively processes as the simulation clock advances. A series of computational experiments contribute to illustrate the performance of our methodology.

ResearchGate

Anna-Maria Nitsche, Olga Matthias, Christoph Laroque, Christian-Andreas Schumann • British Academy of Management Annual Conference (BAM)

This paper is concerned with the changes brought about by digital transformation, which impact society and businesses as well as individuals. These changes also influence manufacturing organisations as decision-making processes are automated and increasingly driven by data analysis. The aim of this research paper is to discuss and analyse technological and organisational readiness in manufacturing. The main areas of focus are Big Data Analytics, Artificial Intelligence in collaboration processes, and the role of the human in future manufacturing organisations.

ResearchGate

Tim Jarschel, Christoph Laroque, Ronny Maschke, Peter Hartmann • Machines 8(2)

An increasing shortening of product life cycles, as well as the trend towards highly individualized food products, force manufacturers to digitize their own production chains. Especially the collection, monitoring, and evaluation of food data will have a major impact in the future on how the manufacturers will satisfy constantly growing customer demands. For this purpose, an automated system for collecting and analyzing food data was set up to promote advanced production technologies in the food industry. Based on the technique of laser triangulation, various types of food were measured three-dimensionally and examined for their chromatic composition. The raw data can be divided into individual data groups using clustering technologies. Subsequent indexing of the data in a big data architecture set the ground for setting up real-time data visualizations. The cluster-based back-end system for data processing can also be used as an organization-wide communication network for more efficient monitoring of companies’ production data flows. The results not only describe the procedure for digitization of food data, they also provide deep insights into the practical application of big data analytics while helping especially small- and medium-sized enterprises to find a good introduction to this field of research.

ResearchGate

Christoph Laroque, Christoph Löffler, Wolfgang Scholl, Germar Schneider • 25. ASIM Symposium Simulationstechnik

Manufacturing is in general characterized by a growing number of customer-specific products that have to be manufactured and delivered in given lead times, according to concrete delivery dates. Thus, highly relevant questions like “When to start a production order at latest, in order to stay within my lead time?” are answered by more or less primitive, backward-oriented planning approaches and without taking into consideration uncertainty or alternatives. It gets more complex, if different products are to be produced and the more complex the underlying manufacturing system is (e.g. semiconductor with reentry cycles). These questions could be answered more specifically, more detailed and more robust, if discrete, event-based simulation (DES) would be applied in a backward-oriented manner. Reseach results show, that the backward-oriented simulation approach can be in principle applied successfully for the scheduling of customer-specific orders.

ResearchGate