scholarly journals A Biobjective Model for Manual Materials Handling with Energy Consumption Being Accounted For

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Wenqian Zhang ◽  
Shuhai Fan ◽  
Jiawei Xiong ◽  
Siyu Chen
Keyword(s):  
Energy Consumption ◽  
Optimal Solution ◽  
Handling Time ◽  
Simulation Software ◽  
Simulation Platform ◽  
Interactive Simulation ◽  
Production Operation ◽  
Production Environment ◽  
Materials Handling ◽  
Manual Materials Handling

Aiming at production environment and operation design in manual materials handling which often overlook workers’ physiological factors and cause fatigue, even work-related musculoskeletal disorders, we construct a biobjective model based on economics and ergonomics. In the model, two objectives include functions about handling time and energy consumption. Based on the openness of IGRIP/ERGO simulation software combined with MATLAB, we design and develop the interactive simulation platform, where program language can be automatically generated. Then, we analyze the case about handling operations in an automobile brake pad manufacturing company, and the number of input materials and process scheduling are taken as research objects. Finally, the results show that the win-win optimal solution can be usually obtained between productivity and ergonomics for decision makers according to the proposed biobjective model. Moreover, the case study demonstrates that the interactive simulation platform can be devoted to providing the solution for modern production operation directly and conveniently, which can make the production environment and operation design in accordance with ergonomics.

Survey of One-handed Lifting in Manufacturing Industry: A Cross-sectional Study of the BackWorks Study Cohort

2020 ◽  
Vol 64 (1) ◽  
pp. 942-946
Author(s):  
Ruoliang Tang ◽  
Jay M. Kapellusch ◽  
Andrew S. Merryweather ◽  
Matthew S. Thiese ◽  
Kurt T. Hegmann ◽  
...  
Keyword(s):  
Manufacturing Industry ◽  
Cross Sectional Study ◽  
Study Cohort ◽  
The European Union ◽  
Sectional Study ◽  
Cross Sectional ◽  
Materials Handling ◽  
Manual Materials Handling ◽  
Lost Productivity ◽  
The One

Low back pain (LBP) is a common health problem and a major cause of lost productivity in workplaces. Manual materials handling (MMH) jobs have traditionally been regarded as risk factor for LBP. Compared to two-handed lifting, one-handed lifting has received little attention in both epidemiological and biomechanical research. In addition, one frequent complaint of the revised NIOSH lifting equation (RNLE) has been the lack of capability to directly evaluate one-handed lifting. Modifications have been proposed by the European Union, however their efficacy and influence have not yet been evaluated. This cross-sectional study provided objective survey of the MMH jobs, especially the one-handed lifting performed in manufacturing industry and investigated the outcomes of three proposed methods to address one-handed lifting using RNLE approach. Preliminary results suggest that workers with some one-handed lifting are associated with higher physical exposure. However, the increase was more significant among those who perform primarily one-handed lifting.

scholarly journals Fully Automated Cultivation of Adipose-Derived Stem Cells in the StemCellDiscovery—A Robotic Laboratory for Small-Scale, High-Throughput Cell Production Including Deep Learning-Based Confluence Estimation

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 575
Author(s):  
Jelena Ochs ◽  
Ferdinand Biermann ◽  
Tobias Piotrowski ◽  
Frederik Erkens ◽  
Bastian Nießing ◽  
...  
Keyword(s):  
Stem Cells ◽  
Deep Learning ◽  
High Throughput ◽  
High Speed ◽  
New Technologies ◽  
Human Mesenchymal Stem Cells ◽  
Simulation Software ◽  
System Capacity ◽  
Small Scale ◽  
Production Environment

Laboratory automation is a key driver in biotechnology and an enabler for powerful new technologies and applications. In particular, in the field of personalized therapies, automation in research and production is a prerequisite for achieving cost efficiency and broad availability of tailored treatments. For this reason, we present the StemCellDiscovery, a fully automated robotic laboratory for the cultivation of human mesenchymal stem cells (hMSCs) in small scale and in parallel. While the system can handle different kinds of adherent cells, here, we focus on the cultivation of adipose-derived hMSCs. The StemCellDiscovery provides an in-line visual quality control for automated confluence estimation, which is realized by combining high-speed microscopy with deep learning-based image processing. We demonstrate the feasibility of the algorithm to detect hMSCs in culture at different densities and calculate confluences based on the resulting image. Furthermore, we show that the StemCellDiscovery is capable of expanding adipose-derived hMSCs in a fully automated manner using the confluence estimation algorithm. In order to estimate the system capacity under high-throughput conditions, we modeled the production environment in a simulation software. The simulations of the production process indicate that the robotic laboratory is capable of handling more than 95 cell culture plates per day.

scholarly journals Trunk Posture during Manual Materials Handling of Beer Kegs

2021 ◽  
Vol 18 (14) ◽  
pp. 7380
Author(s):  
Colleen Brents ◽  
Molly Hischke ◽  
Raoul Reiser ◽  
John Rosecrance
Keyword(s):  
Small Businesses ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Low Back ◽  
Inertial Measurement ◽  
Materials Handling ◽  
Kinematic Measurement ◽  
Manual Materials Handling ◽  
Trunk Posture ◽  
Data Design

Craft brewing is a rapidly growing industry in the U.S. Most craft breweries are small businesses with few resources for robotic or other mechanical-assisted equipment, requiring work to be performed manually by employees. Craft brewery workers frequently handle stainless steel half-barrel kegs, which weigh between 13.5 kg (29.7 lbs.) empty and 72.8 kg (161.5 lbs.) full. Moving kegs may be associated with low back pain and even injury. In the present study, researchers performed a quantitative assessment of trunk postures using an inertial measurement unit (IMU)-based kinematic measurement system while workers lifted kegs at a craft brewery. Results of this field-based study indicated that during keg handling, craft brewery workers exhibited awkward and non-neutral trunk postures. Based on the results of the posture data, design recommendations were identified to reduce the hazardous exposure for musculoskeletal disorders among craft brewery workers.

scholarly journals Linear Weighted Regression and Energy-Aware Greedy Scheduling for Heterogeneous Big Data

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 554
Author(s):  
Suresh Kallam ◽  
Rizwan Patan ◽  
Tathapudi V. Ramana ◽  
Amir H. Gandomi
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Total Energy ◽  
Optimal Solution ◽  
Data Locality ◽  
Weighted Regression ◽  
Heterogeneous Environments ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Greedy Scheduling

Data are presently being produced at an increased speed in different formats, which complicates the design, processing, and evaluation of the data. The MapReduce algorithm is a distributed file system that is used for big data parallel processing. Current implementations of MapReduce assist in data locality along with robustness. In this study, a linear weighted regression and energy-aware greedy scheduling (LWR-EGS) method were combined to handle big data. The LWR-EGS method initially selects tasks for an assignment and then selects the best available machine to identify an optimal solution. With this objective, first, the problem was modeled as an integer linear weighted regression program to choose tasks for the assignment. Then, the best available machines were selected to find the optimal solution. In this manner, the optimization of resources is said to have taken place. Then, an energy efficiency-aware greedy scheduling algorithm was presented to select a position for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without a significant performance loss. To evaluate the performance, the LWR-EGS method was compared with two related approaches via MapReduce. The experimental results showed that the LWR-EGS method effectively reduced the total energy consumption without producing large scheduling overheads. Moreover, the method also reduced the execution time when compared to state-of-the-art methods. The LWR-EGS method reduced the energy consumption, average processing time, and scheduling overhead by 16%, 20%, and 22%, respectively, compared to existing methods.

Well Placement Technique and Production Optimization for Mature Field - A Case Study of L-X Field

2020 ◽  
Vol 46 ◽  
pp. 168-179
Author(s):  
Patrick Nwafor ◽  
Kelani Bello
Keyword(s):  
Oil And Gas ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Oil And Gas Industry ◽  
Optimization Method ◽  
Simulation Software ◽  
Production Optimization ◽  
Direct Optimization ◽  
Well Placement ◽  
Local Optimal Solution

A Well placement is a well-known technique in the oil and gas industry for production optimization and are generally classified into local and global methods. The use of simulation software often deployed under the direct optimization technique called global method. The production optimization of L-X field which is at primary recovery stage having five producing wells was the focus of this work. The attempt was to optimize L-X field using a well placement technique.The local methods are generally very efficient and require only a few forward simulations but can get stuck in a local optimal solution. The global methods avoid this problem but require many forward simulations. With the availability of simulator software, such problem can be reduced thus using the direct optimization method. After optimization an increase in recovery factor of over 20% was achieved. The results provided an improvement when compared with other existing methods from the literatures.

scholarly journals Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Lhassane Idoumghar ◽  
Mahmoud Melkemi ◽  
René Schott ◽  
Maha Idrissi Aouad
Keyword(s):  
Energy Consumption ◽  
Embedded Systems ◽  
Evolutionary Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Time Saving ◽  
Local Optima ◽  
Hybrid Evolutionary Algorithm ◽  
Multimodal Function Optimization ◽  
Multimodal Function

The paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called HPSO-SA, based on the idea that PSO ensures fast convergence, while SA brings the search out of local optima because of its strong local-search ability. The proposed HPSO-SA algorithm is validated on ten standard benchmark multimodal functions for which we obtained significant improvements. The results are compared with these obtained by existing hybrid PSO-SA algorithms. In this paper, we provide also two versions of HPSO-SA (sequential and distributed) for minimizing the energy consumption in embedded systems memories. The two versions, of HPSO-SA, reduce the energy consumption in memories from 76% up to 98% as compared to Tabu Search (TS). Moreover, the distributed version of HPSO-SA provides execution time saving of about 73% up to 84% on a cluster of 4 PCs.

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