Abstracts Track 2022

Area 1 - Intelligent Manufacturing and Management

Nr: 3

Business Benefits from Multiple Digital Twins in Machine Manufacturing Industries


Jukka Hemilä

Abstract: Machine manufacturers have collect data related to their products within different formats and tools already for years. Today, these data constitute Digital Twins (DT), which can be seen as enablers for new service opportunities and ecosystem collaboration. The number of DTs increases continuously, as for example modern factory consists of different production machines, which all might have their individual DTs and in the operative environment might be multiple DTs. Thus, need for seamless integration of individual machine DTs arises, semantic transformations needed, and gaining useful information from DTs arises. Ongoing study investigate what are the business benefits from multiple DTs and how to develop next generation industrial services based on the available DT data of the smart connected machines. This study is a part of an international project focused on researching the new service opportunities provided by the DT concept in several industrial use cases. The empirical data is gathered by semi-structured interviews with use case company practitioners, which research organization facilitated workshops developed further. Several workshops were conducted to map the service processes of the use cases in three ecosystems in Finland, Turkey and the Netherlands. Literature findings and benchmarking studies enriches the empirical findings. Within the study, research will focus on the two main research questions: 1) How Digital Twin can boost value creation in industrial product-service lifecycle, and 2) Which kind of business models are needed in future digitalized industrial context. There is a continuously growing volume, complexity, and strategic importance of data in manufacturing industry. Therefore, manufacturers need to create DT based services together with selected strategic partners. The multiple DT requires new kind of collaboration between manufacturers and SW providers to consolidate data collection, aggregation and analytics for making data and insights available across functions and business units. Realization of multiple DT in practice, new performance and value-based revenue models can be formed. Therefore, new earning logics and pricing models within the DT based services should be innovated. The services based on the DT should be developed according the logical steps. DT have enabler role, but still customers do not fully understand the DT based services in practice. Study have identified new forms of collaboration and combined earnings for ecosystem members. In the future, towards the Industrial Metaverse, new ecosystems will arise and new kind of operative digital tools will come to realize multiple DTs in modern manufacturing environments. This study increases the understanding of multiple DT business benefits and service business innovations for manufacturing industries by further developing previous conceptual framework for service development and management. As a result, study propose new service business development model and methods to support collaborative businesses based on the utilization of DT for the entire lifecycle of the systems.

Area 2 - Logistics and Operations Research

Nr: 2

Ant Colony Optimization based Multi-robot Planner for Combined Task Allocation and Path Finding (ACTF) in Production Logistics


Kai Pfeiffer, Werner Kraus and Agha Ali Haider Qizilbash

Abstract: Nature has inspired many solutions to the problems in computer science and recently in the field of robotics as well. Ant based algorithms have been successful in solving the NP hard problems such as traveling salesman problem. In the field of multi-robots it has been used to solve path finding and task allocation problems. In industrial warehouse applications, these problems are often combined, when for example multiple robots need to pick-up objects from one location and drop-off at the other. Multiple mobile robots need to perform these task optimally and simultaneously being on the floor without collisions. In this paper, we address this problem keeping the objective of being able to obtain collision free paths for all robots in a map, assigned for all given pick-up and drop-off tasks among themselves with an optimized minimal total distance traveled by the robots. We propose a multi-robot planner inspired from ant colony optimization to solve this combined problem. This planner finds collision free paths to all tasks to be done using a spread of ants from each robot. Ignoring the ones with collisions from other ants in their determined paths, the planner rates the tasks according to the total distance traveled. Using this rating system through multiple iterations, the planner eventually selects the best task allocations with paths for all robots among given iterations. This planner or as we call it, Ant Colony Optimization based Multi-Robot Planner for Combined Task Allocation and Path Finding (ACTF) for pick-up and drop-off tasks has been tested against other similar planners producing promising results. This planner is already published as a conference paper at 2020 17th International Conference on Ubiquitous Robots (UR) and in continuation of the work, we have applied this approach on the scenarios involving production applications. A matrix like production environment has been created and used to test the performance of the planner. Pick-up and drop-off tasks have been simulated in the above mentioned scenario and grouped to form a batch of orders. Specific scenarios have been tested in simulation and a considerable difference is observed in total distance traveled by all robots where our planner outperforms the usual methods involving greedy and cost-based approaches.