Penjadwalan Mesin Pada PT.XYZ Dengan Menggunakan Algoritma Genetik

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Rosnani Ginting ◽  
Benedictus Vito Bayu
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Spare Part ◽  
Industrial Sector ◽  
Spare Parts ◽  
Make To Order ◽  
Fitness Value ◽  
Scheduling Method ◽  
Palm Oil Mill ◽  
A Company

Persaingan antar perusahaan semakin meningkat seiring dengan meningkatnya kemajuan ilmu pengetahuan dan teknologi. Permintaan konsumen juga semakin meningkat sesuai dengan perkembangan dalam bidang perindustrian. PT. XYZ merupakan perusahaan yang bergerak di bidang produksi mesin pada Pabrik Kelapa Sawit (PKS) dan juga memproduksi spare part mesin untuk perusahaan lainnya. Perusahaan melakukan kegiatan produksi berdasarkan pesanan atau order yang masuk (make to order), dengan proses pengerjaan job shop. Permasalahan yang dihadapi oleh PT. XYZ adalah permasalahan keterlambatan. Terdapat 5 produk yang mengalami keterlambatan. Untuk itu, pada penelitian ini dilakukan penjadwalan mesin untuk mengatasi permasalahan keterlambatan tersebut.Metode penjadwalan yang digunakan yaitu menggunakan algoritma genetik. Pada lagoritma genetik ini, dilakukan inisialisasi awal dengan menggunakan metode SPT untuk memperoleh urutan job. Kemudian dilakukan tahap seleksi, crossover dan mutase untuk memperoleh uruta job yang paling optimal. Berdasarkan pengolahan data yang dilakukan, diperoleh bahwa dalam tiga generasi terdapat empat kromosom terbaik, yaitu BECAD, BACED, BEACD, BCAED dengan nilai fitness yang sama yaitu 0,02144. Urutan kerja yang dipilih dalam hal ini adalah BCAED, yakni urutan pengerjaan semua produk. Urutan job ini memiliki makespan sebesar 46,637 jam merupakan yang terbaik dari tiap generasi dengan nilai fitness terbaik yaitu 0,02144.   Competition between companies is increasing along with the advancement of science and technology. Consumer demand is also increasing according to developments in the industrial sector. PT. XYZ is a company engaged in the production of machinery at the Palm Oil Mill (PKS) and also produces engine spare parts for other companies. The company conducts production activities based on orders or orders that enter (make to order), with the job shop process. Problems faced by PT. XYZ is a matter of delay. There are 5 products that experience delays. For that reason, in this study a machine scheduling was carried out to overcome the problem of delay. The scheduling method used was using a genetic algorithm. In this genetic lag, initial initialization is done by using the SPT method to obtain the job sequence. Then do the selection, crossover and mutase stages to get the most optimal job sequence. Based on the data processing performed, it was found that in three generations there were four of the best chromosomes, namely BECAD, BACED, BEACD, BCAED with the same fitness value of 0.02144. The work order chosen in this case is BCAED, which is the order of work for all products. This job sequence has a makespan of 46,637 hours which is the best of each generation with the best fitness value of 0,02144.

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.

Improved Genetic Algorithm Based on Operation Order Matrix Encoding for Job Shop Scheduling Problem

2011 ◽  
Vol 189-193 ◽  
pp. 4212-4215
Author(s):  
Hong Zhan ◽  
Jian Jun Yang ◽  
Lu Yan Ju
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Order Matrix ◽  
Fitness Value ◽  
Matrix Encoding

This paper presents an improved genetic algorithm for the job shop scheduling problem. We designed a new encoding method based on operation order matrix, a matrix correspond to a chromosome, the value of elements is not repetitive, that means a processing order number in all operations of all jobs. Aiming at the features of the matrix encoding, we designed the crossover and mutation methods based on jobs, and the infeasible solutions are avoided. Through adjusting the computing method of fitness value, the improved genetic algorithm takes on some self adapting capability. The proposed approach is tested on some standard instances and compared with two other approaches. The computation results validate the algorithm is efficient.

Nesting Scheduling in Sheet Metal Processing Based on Coevolutionary Genetic Algorithm in Different Environments

2018 ◽  
Vol 12 (5) ◽  
pp. 730-738 ◽  
Author(s):  
Tatsuhiko Sakaguchi ◽  
◽  
Kohki Matsumoto ◽  
Naoki Uchiyama
Keyword(s):  
Genetic Algorithm ◽  
Sheet Metal ◽  
Processing Time ◽  
Factors Affecting ◽  
Fitness Value ◽  
Evolutionary Genetic ◽  
Scheduling Method ◽  
Grouping Method ◽  
Metal Processing ◽  
Coevolutionary Genetic Algorithm

In sheet metal processing, nesting and scheduling are important factors affecting the efficiency and agility of manufacturing. The objective of nesting is to minimize the waste of material, while that of scheduling is to optimize the processing sequence. As the relation between them often becomes a trade-off, they should be considered simultaneously for the efficiency of the total manufacturing process. In this study, we propose a co-evolutionary genetic algorithm-based nesting scheduling method. We first define a cost function as a fitness value, and then we propose a grouping method that forms gene groups based on the processing layout and processing time. Finally, we validate the effectiveness of the proposed method through computational experiments.

S1440102 A Flexible Job Shop Scheduling Method Using Genetic Algorithm

2015 ◽  
Vol 2015 (0) ◽  
pp. _S1440102--_S1440102-
Author(s):  
Yuki SAKAGUCHI ◽  
Eiji MORINAGA ◽  
Hidefumi WAKAMATSU ◽  
Eiji ARAI
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Scheduling Method

An Optimization Algorithm Based on Job Shop Scheduling with Fuzzy Goals

2014 ◽  
Vol 912-914 ◽  
pp. 1156-1159
Author(s):  
Qing Ling Dai ◽  
Sheng Bo Zhang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Operating Mode ◽  
Problem Solution ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Delivery Date ◽  
Scheduling Method

The mathematical model was built up for the job shop scheduling problem at first. With following the fuzzy submit time of customer requirement an improved genetic algorithm of fuzzy objective scheduling method was put forward which took the minimum production cost as the objective function. It solved the faults of the chromosomes in genetic algorithm is difficult to accurately express the complex optimization problem solution and determined the more suitable multilayer encoding and operating mode. The simulation results show that this algorithm can be applied to fuzzy object shop scheduling optimization problem, which can ensure the machine's load balance and meet the requirements of the customer delivery date.

scholarly journals Using Guided Initial Chromosome of Genetic Algorithm for Scheduling Production-Distribution System

2019 ◽  
Vol 4 (1) ◽  
pp. 26
Author(s):  
Rafiuddin Rody ◽  
Wayan Firdaus Mahmudy ◽  
Ishardita Pambudi Tama
Keyword(s):  
Genetic Algorithm ◽  
Distribution System ◽  
Complex Problem ◽  
Mixed Integer ◽  
Order System ◽  
Solution Quality ◽  
Size Problem ◽  
Make To Order ◽  
Production Distribution ◽  
A Company

Production and distribution system in a company should be managed carefully. Delay in product delivery not only results in a late penalty due to customer dissatisfaction or breach of contract, but also causes a supply chain failure. Of course, all these impacts will also reduce the reputation of a company. Scheduling integrated production-distribution is classified as NP-Hard problem. Genetic algorithm can be used to solve complex problem. In this paper, genetic algorithm is used for scheduling production-distribution in make to order system where each job has a different deadline and volume (size). This problem is represented on mixed integer programing model. We verify the genetic algorithm’s performance by comparing the results with the total cost calculated by lower bounds of the problems. Experiments show that the traditional initial random cannot produce good result with more than 15 job size problem. We proposed guided initial chromosome to tackle this problem. From further experiments shows that the proposed method approach can increase the performances of genetic algorithm in more than 15 job size problem. In general, proposed genetic algorithm with guided initial chromosome shows better solution quality and better time efficiency compared to previous related research.

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