4,635 research outputs found
An efficient genetic algorithm for large-scale planning of robust industrial wireless networks
An industrial indoor environment is harsh for wireless communications
compared to an office environment, because the prevalent metal easily causes
shadowing effects and affects the availability of an industrial wireless local
area network (IWLAN). On the one hand, it is costly, time-consuming, and
ineffective to perform trial-and-error manual deployment of wireless nodes. On
the other hand, the existing wireless planning tools only focus on office
environments such that it is hard to plan IWLANs due to the larger problem size
and the deployed IWLANs are vulnerable to prevalent shadowing effects in harsh
industrial indoor environments. To fill this gap, this paper proposes an
overdimensioning model and a genetic algorithm based over-dimensioning (GAOD)
algorithm for deploying large-scale robust IWLANs. As a progress beyond the
state-of-the-art wireless planning, two full coverage layers are created. The
second coverage layer serves as redundancy in case of shadowing. Meanwhile, the
deployment cost is reduced by minimizing the number of access points (APs); the
hard constraint of minimal inter-AP spatial paration avoids multiple APs
covering the same area to be simultaneously shadowed by the same obstacle. The
computation time and occupied memory are dedicatedly considered in the design
of GAOD for large-scale optimization. A greedy heuristic based
over-dimensioning (GHOD) algorithm and a random OD algorithm are taken as
benchmarks. In two vehicle manufacturers with a small and large indoor
environment, GAOD outperformed GHOD with up to 20% less APs, while GHOD
outputted up to 25% less APs than a random OD algorithm. Furthermore, the
effectiveness of this model and GAOD was experimentally validated with a real
deployment system
An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments
The industrial wireless local area network (IWLAN) is increasingly dense, due to not only the penetration of wireless applications to shop floors and warehouses, but also the rising need of redundancy for robust wireless coverage. Instead of simply powering on all access points (APs), there is an unavoidable need to dynamically control the transmit power of APs on a large scale, in order to minimize interference and adapt the coverage to the latest shadowing effects of dominant obstacles in an industrial indoor environment. To fulfill this need, this paper formulates a transmit power control (TPC) model that enables both powering on/off APs and transmit power calibration of each AP that is powered on. This TPC model uses an empirical one-slope path loss model considering three-dimensional obstacle shadowing effects, to enable accurate yet simple coverage prediction. An efficient genetic algorithm (GA), named GATPC, is designed to solve this TPC model even on a large scale. To this end, it leverages repair mechanism-based population initialization, crossover and mutation, parallelism as well as dedicated speedup measures. The GATPC was experimentally validated in a small-scale IWLAN that is deployed a real industrial indoor environment. It was further numerically demonstrated and benchmarked on both small- and large-scales, regarding the effectiveness and the scalability of TPC. Moreover, sensitivity analysis was performed to reveal the produced interference and the qualification rate of GATPC in function of varying target coverage percentage as well as number and placement direction of dominant obstacles. (C) 2018 Elsevier B.V. All rights reserved
Platooning-based control techniques in transportation and logistic
This thesis explores the integration of autonomous vehicle technology with smart manufacturing systems. At first, essential control methods for autonomous vehicles, including Linear Matrix Inequalities (LMIs), Linear Quadratic Regulation (LQR)/Linear Quadratic Tracking (LQT), PID controllers, and dynamic control logic via flowcharts, are examined. These techniques are adapted for platooning to enhance coordination, safety, and efficiency within vehicle fleets, and various scenarios are analyzed to confirm their effectiveness in achieving predetermined performance goals such as inter-vehicle distance and fuel consumption. A first approach on simplified hardware, yet realistic to model the vehicle's behavior, is treated to further prove the theoretical results.
Subsequently, performance improvement in smart manufacturing systems (SMS) is treated. The focus is placed on offline and online scheduling techniques exploiting Mixed Integer Linear Programming (MILP) to model the shop floor and Model Predictive Control (MPC) to adapt scheduling to unforeseen events, in order to understand how optimization algorithms and decision-making frameworks can transform resource allocation and production processes, ultimately improving manufacturing efficiency.
In the final part of the work, platooning techniques are employed within SMS. Autonomous Guided Vehicles (AGVs) are reimagined as autonomous vehicles, grouping them within platoon formations according to different criteria, and controlled to avoid collisions while carrying out production orders. This strategic integration applies platooning principles to transform AGV logistics within the SMS. The impact of AGV platooning on key performance metrics, such as makespan, is devised, providing insights into optimizing manufacturing processes.
Throughout this work, various research fields are examined, with intersecting future technologies from precise control in autonomous vehicles to the coordination of manufacturing resources. This thesis provides a comprehensive view of how optimization and automation can reshape efficiency and productivity not only in the domain of autonomous vehicles but also in manufacturing
Factors influencing employee perceptions in lean transformations
The purpose of the study was to investigate employee perceptions during a lean transformation1. The
combination of case study and survey methodologies was used to define elements influencing the perceived lean
success of shop floor employees. According to our findings, belief, commitment, work method and
communication all have a considerable direct impact on workersâ perceptions of lean success. However, their
effects are very different based on the scope and focus of changes that is influenced by process characteristics.
Perceptions regarding successful lean transformation during a moderate reorganisation of the companyâs welding
plant, where mainly males work, are affected only by commitment and work method, whereas the deep
reorganisation of the sewing plant (populated by female employees) is only influenced by belief and
communication
Using discrete event simulation for scheduling and long range capacity planning of a high volume press shop
The thesis expresses the essential requirement for and the use of Discrete Event Simulation (DES) in a high volume press shop. The press shop produces blanks and panels for the body shop, which manufactures three car models. DES is used to combat the battle between shop efficiency and low inventory. The process used to choose the most appropriate software package is described and then current situation in the press shop is discussed. The procedures involved in model creation follow set model construction guidelines. There are several assumptions made, which together with the constraints of the system, provide the limitations of the inputs facing the system. There is a trade off between model complexity and accuracy, so the setting of the constraints and assumptions often provided difficult decisions. Validation of the model is very important, so this was a lengthy process, involving using a series of dummy buffers to check inputs such as cycle times and batch quantities. The validated model is used to monitor the methods used to reduce inventory on the shop floor over a period of eight weeks and then used for 'What If? Scenarios, to ascertain the systems capacity and inventory levels underdifferent conditions. The scenarios include using volumes that are 100% higher on some models than the current situation and 20% less than currently. The findings are examined and proposals made for the introduction of the proposed volumes where possible. Findings of the scenarios highlight bottlenecks in the shop and areas for improvement. Using the model, the schedules can be changed quickly and easily to try and eliminate the bottlenecks and improve capacity. Conclusions discuss the problems encountered during the modelling process as well as the benefits. The integration of DES into the current scheduling processes in the shop poses no problems and the model will be used as an aid for capacity planning in the future
Dynamics in Logistics
This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions
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