scholarly journals An improved Genetic Algorithm For Fuzzy Production PlanningProblems with Application

2015 ◽  
Vol 1 (3) ◽  
pp. 390
Author(s):  
Jalal Abdulkareem Sultan ◽  
Omar Ramzi Jasim ◽  
Sarmad Abdulkhaleq Salih
Keyword(s):  
Genetic Algorithm ◽  
Customer Service ◽  
Fundamental Problem ◽  
Demand Forecasting ◽  
Fuzzy Model ◽  
Main Idea ◽  
Production Schedule ◽  
Improved Genetic Algorithm ◽  
Customer Service Level ◽  
Master Production Schedule

Production Planning or Master Production Schedule (MPS) is a key interface between marketing and manufacturing, since it links customer service directly to efficient use of production resources. Mismanagement of the MPS is considered as one of fundamental problem in operation and it can potentially lead to poor customer satisfaction.  In this paper, an improved Genetic Algorithm (IGA) is used to solving fuzzy multi-objective master production schedule (FMOMPS). The main idea is to integrate GA with local search operator. The FMOMPS was applied in the Cotton and medical gauzes plant in Mosul city. The application involves determine the gross requirements by demand forecasting using artificial neural networks. The IGA proved its efficiency in solving MPS problems compared with the genetic algorithm for fuzzy and non-fuzzy model, as the results clearly showed the ability of IGA to determine intelligently how much, when, and where the additional capacities (overtimes) are required such that the inventory can be reduced without affecting customer service level.

Adaptive Weighting Decoupling Design for Constrained Generalized Predictive Control Using Improved Genetic Algorithm

2013 ◽  
Vol 441 ◽  
pp. 829-832
Author(s):  
Yong Bin Dai
Keyword(s):  
Genetic Algorithm ◽  
Predictive Control ◽  
Shape Control ◽  
Control Method ◽  
Dynamic Performance ◽  
Main Idea ◽  
Generalized Predictive Control ◽  
Multivariable System ◽  
Improved Genetic Algorithm ◽  
Error Weight

The paper proposes a new method for decoupling multivariable system based on generalized predictive control (GPC) with constrains. It is the main idea of proposed control method that the error weight can change with output deviation caused by reference changes in order to reduce interactions in the system and improve dynamic performance of coupling loops. With improved genetic algorithm to optimize the performance index of GPC, the algorithm is applied to auto shape control and auto gauge control (ASC-AGC). The simulation results demonstrate the efficiency and correctness of approach proposed.

scholarly journals Penerapan MRP Pada Persediaan Material Dan Pengaruhnya Terhadap Ketepatan Waktu

Jurnal METTEK ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 119
Author(s):  
Gede Widya Jaya Laksana ◽  
I Gusti Agung Kade Suriadi ◽  
I Putu Lokantara
Keyword(s):  
Time Series ◽  
Production Process ◽  
Demand Forecasting ◽  
Work Productivity ◽  
Total Production ◽  
Production Schedule ◽  
Chief Of Staff ◽  
Material Requirement ◽  
Bottle Neck ◽  
Master Production Schedule

PT. X sudah dapat memproduksi kendaraan roda empat berkisar 300 buah/hari, pada saat penulis melaksanakan penelitian beberapa kali sempat terjadi bottle neck pada pos kerja perakitan engine sehingga hal tersebut menjadi dorongan bagi penulis untuk mencari tahu permasalahan yang sebenarnya sedang terjadi. Setelah melakukan observasi dan mewawancarai operator serta kepala staff yang bertugas pada assy-line mendapatkan hasil bahwa terhambatnya proses produksi tersebut dikarenakan terjadinya keterlambatan pada part yang dibutuhkan pada pos tersebut, maka dari itu dilaksanakan penelitian mengenai penerapan metode (MRP) material requirements planning, penelitian ini bertujuan untuk mengoptimalkan kinerja daripada suatu proses produksi material mentah menjadi bahan jadi yang bernilai jual tinggi, agar dapat terlaksana dengan tepat waktu dan dengan jumlah material yang tepat. Penelitian ini dilaksanakan dengan membuat master production schedule (MPS) atau jadwal induk produksi kemudian membuat catatan ketersediaan material dilanjutkan dengan membuat bill of material (BOM) atau struktur produk dari engine dan melakukan perhitungan peramalan permintaan menggunakan metode time series. Seluruh data yang telah diolah akan menjadi dasar dari pembuatan MRP, dengan jumlah produksi sebanyak 877 buah pada minggu ke-V Bulan April dan 864 buah pada minggu ke-I Bulan Mei menerapkan MRP pada suatu proses produksi dapat meningkatkan produktivitas kerja. Hasil perhitungan waktu yang didapat menunjukkan bahwa tidak terjadi bottle neck pada perhitungan MRP minggu ke-III, ke-IV dan ke-V X Inc. has been able to produce approximately 300 pieces of four-wheel vehicles per day, when the author carried out this research, there were some bottle necks happened several times on the work post of the engine assembly where it became a motivation for the author to find out what problems are going on. After observing and interviewing operator and chief of staff on the assy-line, it was concluded that the obstructed production process was caused by a delay in the needed parts on the said post, therefore a research about the application method of Material Requirement Planning (MRP) is carried out, where this research is aimed to optimize the production process of raw into finished, high valued materials, and can be done in a timely manner with exact amount of materials. This study was done by making a Master Production Schedule (MPS), then, making a material availability notation, continued by making a Bill of Material (BOM) and calculating the demand forecasting using the time series method. The entire data that had been processed became the base of the MRP-making, with the total production as many as 877 on the 5th week of April and 864 on the 1st week of May. Applying MRP on the production process can increase work productivity. The result of the time calculation obtained indicates that there was no bottle neck in the 3rd, 4th, and 5th week MRP calculation.

Simulation-Based Genetic Algorithm for Cross-Docking Center Operation Optimization under Supply Disruptions

2011 ◽  
Vol 201-203 ◽  
pp. 1103-1106
Author(s):  
Yi Fan Wu
Keyword(s):  
Genetic Algorithm ◽  
Customer Service ◽  
Performance Optimization ◽  
Transportation Cost ◽  
Search Space ◽  
Inventory Level ◽  
Cost Rate ◽  
Supply Disruptions ◽  
Cross Docking ◽  
Customer Service Level

Practical performance optimization of a cross-docking center has been rare in the literature so far. The measures representing operation efficiency are average inventory level and transportation cost rate, while average backorder level represents the customer service level. In this paper, a simulation optimization problem is considered and a solution framework has been developed by integrating simulation, genetic algorithm (GA) and smart computing budget allocation (SCBA) to find an optimized solution. Moreover, supply disruptions are considered in the simulation model. This problem has huge search space even for medium-sized problem scenarios. To address this difficulty, the framework employs simulation to estimate the performance measures, GA to search for better design and SCBA to efficiently allocate the simulation budget.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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