A nested ant colony algorithm for hybrid production scheduling

2002 ◽  
Author(s):  
Yanjun Li ◽  
Tie-Jun Wu
Keyword(s):  
Production Scheduling ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Hybrid Production

Weaving scheduling based on an improved ant colony algorithm

2020 ◽  
pp. 004051752094889
Author(s):  
Wentao He ◽  
Shuo Meng ◽  
Jing’an Wang ◽  
Lei Wang ◽  
Ruru Pan ◽  
...  
Keyword(s):  
Production Scheduling ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Superior Performance ◽  
Ant Colony System ◽  
Local Optimum ◽  
Scheduling Method ◽  
Automatic Methods ◽  
Automatic Scheduling ◽  
Improved Ant Colony Algorithm

Weaving enterprises are faced with problems of small batches and many varieties, which leads to difficulties in manual scheduling during the production process, resulting in more delays in delivery. Therefore, an automatic scheduling method for the weaving process is proposed in this paper. Firstly, a weaving production scheduling model is established based on the conditions and requirements during actual production. By introducing flexible model constraints, the applicability of the model has been greatly expanded. Then, an improved ant colony algorithm is proposed to solve the model. To address the problem of the traditional ant colony algorithm that the optimizing process usually traps into local optimum, the proposed algorithm adopts an iterative threshold and the maximum and minimum ant colony system. In addition, the initial path pheromone distribution is formed according to the urgency of the order to balance each objective. Finally, the simulation experiments confirm that the proposed method achieves superior performance compared with manual scheduling and other automatic methods. The proposed method shows a certain guiding significance for weaving scheduling in practice.

Usulan Penjadwalan Produksi dengan Algoritma Ant Colony (Studi Kasus PT. KLM Medan)

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Dila Syafrina Bangko ◽  
Rosnani Ginting
Keyword(s):  
Production Process ◽  
Production Scheduling ◽  
Completion Time ◽  
Processing Time ◽  
Ant Colony Algorithm ◽  
Optimal Combination ◽  
Ant Colony ◽  
A Value ◽  
Scheduling Method

Algoritma Ant Colony adalah suatu metauristik yang menggunakan teknik semut dengan kombinasi permasalahan secara optimal atau secara komunikasi semunt yang menggunakan alat penciuman untuk memecahkan masalah. Metode penjadwalan produksi yang selama ini digunakan berdasarkan kesamaan proses produksi. Hal inilah yang terkadang menyebabkan waktu penyelesaian produksi menjadi lebih panjang. Maka dilakukan metode lain untuk menggurangi makespan yaitu dengan menggunakan metode penjadwalan algoritma ant colony. Pada metode Shortest Processing Time (SPT) menghasilkan makespan sebesar 236079.89, sedangkan pada algoritma ant colony menghasilkan nilai α=10, β = 1, ρ = 0.5, Ncmax = 50, jumlah semut 7 dan makespan=215243.22. berdasarkan hasil yang diperoleh maka algoritma ant colony memiliki makespan terkecil yaitu 215243.22 detik.   The Ant Colony algorithm is a metauristic that uses ant techniques with optimal combination of problems or semuntic communication using olfactory tools to solve problems. The production scheduling method that has been used is based on the similarity of the production process. This is what sometimes causes the production completion time to be longer. Then another method is used to reduce makespan by using the ant colony algorithm scheduling method. In the Shortest Processing Time (SPT) method produces makespan of 236079.89, while the ant colony algorithm produces a value of α = 10, β = 1, ρ = 0.5, Ncmax = 50, the number of ants 7 and makespan = 215243.22. Based on the results obtained, the ant colony algorithm has the smallest makespan, 215243.22 seconds.

Expert System Aided Multi-Agent Intelligent Ant Colony Optimization System

2011 ◽  
Vol 121-126 ◽  
pp. 2021-2025 ◽  
Author(s):  
Jing Hua Zhao ◽  
Jie Lin
Keyword(s):  
Supply Chain ◽  
Expert System ◽  
Mass Customization ◽  
Production Scheduling ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Production Environment ◽  
Scheduling Model ◽  
Decomposition Structure ◽  
Multi Agent

In the dynamic production environment of supply chain, based on information sharing among enterprises of supply chain, this paper designs an expert system aided multi-agent intelligent ant colony algorithm system to solve the production scheduling optimization model. Where ant colony is constructed with multi-agent and the order decomposition structure and constraint are expressed by expert system. And then it builds a system using JESS and JADE to confirm this algorithm applied in a mass customization supply chain scheduling model

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