scholarly journals Automated Optimization for the Production Scheduling of Prefabricated Elements Based on the Genetic Algorithm and IFC Object Segmentation

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1593
Author(s):  
Zhao Xu ◽  
Xiang Wang ◽  
Zezhi Rao
Keyword(s):  
Genetic Algorithm ◽  
Production Process ◽  
Production Scheduling ◽  
Process Model ◽  
Query Language ◽  
Optimal Schedule ◽  
Optimization Framework ◽  
Computational Environment ◽  
Automated Optimization ◽  
Component Production

Background: With the ever-increasing availability of data and a higher level of automation and simulation, production scheduling in the factory for prefabrication can no longer be seen as an autonomous solution. Concepts such as building information modelling (BIM), graphic techniques, databases, and interface development as well as heightened emphasis on overall-process optimization topics increase the pressure to connect to and interact with interrelated tasks and procedures. Methods: The automated optimization framework detailed in this study intended to generate optimal schedule of prefabricated component production based on the manufacturing process model and genetic algorithm method. An extraction and segmentation approach based on industry foundation classes (IFC) for prefabricated component production is discussed. During this process, the position and geometric information of the prefabricated components are adjusted and output in the extracted IFC file. Then, the production process and the completion time of each process have been examined and simulated with the genetic algorithm. Lastly, the automated optimization solution can be formed by the linking production scheduling database and the computational environment. Results: This shows that the implementation of the automated optimization framework for the production scheduling of the prefabricated elements improves the operability and accuracy of the production process. Conclusions: Based on the integration technique discussed above, the data transmission and integration in the mating application program is achieved by linking the Python-based application, the Structured Query Language (SQL) database and the computational environment. The implementation of the automated optimization framework model enables BIM models to play a better foundational role in patching up the technical gaps between prefabricated building designers and element producers.

scholarly journals Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 297 ◽  
Author(s):  
Ke Shen ◽  
Toon Pessemier ◽  
Xu Gong ◽  
Luc Martens ◽  
Wout Joseph
Keyword(s):  
Genetic Algorithm ◽  
Production Scheduling ◽  
Large Scale ◽  
Optimization Problems ◽  
Continuous Production ◽  
Optimal Schedule ◽  
Memory Search ◽  
Search Problem ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm

Energy and failure are separately managed in scheduling problems despite the commonalities between these optimization problems. In this paper, an energy- and failure-aware continuous production scheduling problem (EFACPS) at the unit process level is investigated, starting from the construction of a centralized combinatorial optimization model combining energy saving and failure reduction. Traditional deterministic scheduling methods are difficult to rapidly acquire an optimal or near-optimal schedule in the face of frequent machine failures. An improved genetic algorithm (IGA) using a customized microbial genetic evolution strategy is proposed to solve the EFACPS problem. The IGA is integrated with three features: Memory search, problem-based randomization, and result evaluation. Based on real production cases from Soubry N.V., a large pasta manufacturer in Belgium, Monte Carlo simulations (MCS) are carried out to compare the performance of IGA with a conventional genetic algorithm (CGA) and a baseline random choice algorithm (RCA). Simulation results demonstrate a good performance of IGA and the feasibility to apply it to EFACPS problems. Large-scale experiments are further conducted to validate the effectiveness of IGA.

Formulation of Colors Using a Genetic Algorithm

2012 ◽  
Vol 17 (4) ◽  
pp. 241-244
Author(s):  
Cezary Draus ◽  
Grzegorz Nowak ◽  
Maciej Nowak ◽  
Marcin Tokarski
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Production Process ◽  
Computer Technology ◽  
Manual Method ◽  
Apply Genetic ◽  
Computer Aided ◽  
Multiple Samples ◽  
Plastics Industry

Abstract The possibility to obtain a desired color of the product and to ensure its repeatability in the production process is highly desired in many industries such as printing, automobile, dyeing, textile, cosmetics or plastics industry. So far, most companies have traditionally used the "manual" method, relying on intuition and experience of a colorist. However, the manual preparation of multiple samples and their correction can be very time consuming and expensive. The computer technology has allowed the development of software to support the process of matching colors. Nowadays, formulation of colors is done with appropriate equipment (colorimeters, spectrophotometers, computers) and dedicated software. Computer-aided formulation is much faster and cheaper than manual formulation, because fewer corrective iterations have to be carried out, to achieve the desired result. Moreover, the colors are analyzed with regard to the metamerism, and the best recipe can be chosen, according to the specific criteria (price, quantity, availability). Optimaization problem of color formulation can be solved in many diferent ways. Authors decided to apply genetic algorithms in this domain.

scholarly journals Automated Optimization of Intersections Using a Genetic Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 15452-15468 ◽  
Author(s):  
Luis Cruz-Piris ◽  
Miguel A. Lopez-Carmona ◽  
Ivan Marsa-Maestre
Keyword(s):  
Genetic Algorithm ◽  
Automated Optimization

scholarly journals Multimodal Optimization of Permutation Flow-Shop Scheduling Problems Using A Clustering-Genetic-Algorithm-Based Approach

2021 ◽  
Vol 11 (8) ◽  
pp. 3388
Author(s):  
Pan Zou ◽  
Manik Rajora ◽  
Steven Y. Liang
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Flow Shop ◽  
Optimal Schedule ◽  
Flow Shop Scheduling ◽  
Multimodal Optimization ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop ◽  
Global Optima

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current techniques only provide a single optimal schedule. Therefore, a new algorithm is proposed, by combining the k-means clustering algorithm and Genetic Algorithm (GA), for the multimodal optimization of PFSSP. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters, based on some machine-sequence-related features. Next, the operators of GA are applied to the individuals belonging to the same cluster to find multiple global optima. Unlike standard GA, where all individuals belong to the same cluster, in the proposed approach, these are split into multiple clusters and the crossover operator is restricted to the individuals belonging to the same cluster. Doing so, enabled the proposed algorithm to potentially find multiple global optima in each cluster. The performance of the proposed algorithm was evaluated by its application to the multimodal optimization of benchmark PFSSP. The results obtained were also compared to the results obtained when other niching techniques such as clearing method, sharing fitness, and a hybrid of the proposed approach and sharing fitness were used. The results of the case studies showed that the proposed algorithm was able to consistently converge to better optimal solutions than the other three algorithms.

scholarly journals Penjadwalan Tenaga Kebidanan Menggunakan Algoritma Memetika

2018 ◽  
Vol 8 (1) ◽  
pp. 99
Author(s):  
A. Y. Erwin Dodu ◽  
Deny Wiria Nugraha ◽  
Subkhan Dinda Putra
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Public Hospital ◽  
Memetic Algorithm ◽  
Optimal Schedule ◽  
Scheduling System ◽  
Local Search Methods ◽  
Evolution Algorithm ◽  
Central Sulawesi

The problem of midwife scheduling is one of the most frequent problems in hospitals. Midwife should be available 24 hours a day for a full week to meet the needs of the patient. Therefore, good or bad midwife scheduling result will have an impact on the quality of care on the patient and the health of the midwife on duty. The midwife scheduling process requires a lot of time, effort and good cooperation between some parties to solve this problem that is often faced by the Regional Public Hospital Undata Palu Central Sulawesi Province. This research aimed to apply Memetics algorithm to make scheduling system of midwifery staff at Regional Public Hospital Undata Palu Central Sulawesi Province that can facilitate the process of midwifery scheduling as well as to produce optimal schedule. The scheduling system created will follow the rules and policies applicable in the hospital and will also pay attention to the midwife's preferences on how to schedule them according to their habits and needs. Memetics algorithm is an optimization algorithm that combines Evolution Algorithm  and Local Search method. Evolution Algorithm in Memetics Algorithm generally refers to Genetic Algorithm so that the characteristics of Memetics Algotihm are identical with  Genetic Algorithm characteristics with the addition of Local Search methods. Local Search in Memetic Algorithm aims to improve the quality of an individual so it is expected to accelerate the time to get a solution.

scholarly journals ANALISA PERBANDINGAN PENJADWALAN PRODUKSI DENGAN METODE CDS (CAMPBELL DUDEK SMITH), EDD (EARLIEST DUE DATE) DAN FCFS (FIRST COME FIRST SERVE) PADA PT. SARI WARNA ASLI UNIT V

2021 ◽  
Vol 1 (2) ◽  
pp. 46-51
Author(s):  
Dwi Ayu Lestari, Vikha Indira Asri
Keyword(s):  
Production Process ◽  
Production System ◽  
Production Scheduling ◽  
Job Shop ◽  
Allocation Of Resources ◽  
Due Date ◽  
Scheduling System ◽  
Make To Order ◽  
First Come First Serve ◽  
Scheduling Method

Scheduling is defined as the process of sequencing the manufacture of a product as a whole on several machines. All industries need proper scheduling to manage the allocation of resources so that the production system can run quickly and precisely as of it can produce optimal product. PT. Sari Warna Asli Unit V is one of the companies that implements a make to order production system with the FCFS system. Thus, scheduling the production process at this company is also known as job shop production scheduling. The methods used in this research are the CDS method, the EDD method and the FCFS method. The purpose of this research is to minimize the production time and determine the best method that can be applied to the company. The results of this research showed that the makespan obtained in the company's scheduling system with FCFS rules was 458 minutes, and the results of scheduling using the CDS method obtained a makespan value of 329 minutes, then the best production scheduling method that had the smallest makespan value was the CDS method.

scholarly journals PENJADWALAN MESIN DENGAN MENGGUNAKAN ALGORITMA JADWAL NON-DELAY DI PT. SNN SEKSI WORKSHOP

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Inna Kholidasari ◽  
Keyword(s):  
Production Process ◽  
Production Scheduling ◽  
Completion Time ◽  
Job Shop ◽  
Machine Scheduling ◽  
Spare Parts ◽  
First In First Out ◽  
A Company

Production scheduling is the most important part in carrying out the production process that will be carried out on a production floor. Scheduling activities are carried out before the production process begins to ensure the smooth running of the production process. If the production scheduling is not done properly, there will be obstacles in the production process and will cause losses to the company. This study aims to determine the production machine scheduling in a company engaged in the manufacture of spare parts for automotive products. This company implements a job shop production process and uses the First In First Out method in completing its work. Due to the large number of products that have to be produced, there are often two or more products that must be worked on at the same time and machine. This condition causes some products to have to wait for the associated machine to finish operating and causes long product turnaround times. This problem is solved by making a production machine scheduling using the Non-Delay method. By applying this method, the makespan of completion time can be minimized.

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