Analyzing the behaviors of virtual cells (VCs) and traditional manufacturing systems: Ant colony optimization (ACO)-based metamodels

2009 ◽  
Vol 36 (7) ◽  
pp. 2275-2285 ◽  
Author(s):  
Saadettin Erhan Kesen ◽  
M. Duran Toksari ◽  
Zülal Güngör ◽  
Ertan Güner
Keyword(s):  
Ant Colony Optimization ◽  
Manufacturing Systems ◽  
Ant Colony ◽  
Virtual Cells ◽  
Traditional Manufacturing

Solving Machine Loading Problem of FMS

2011 ◽  
pp. 19-43 ◽  
Author(s):  
Anoop Prakash ◽  
Nagesh Shukla ◽  
Ravi Shankar ◽  
Manoj Kumar Tiwari
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Random Search ◽  
Search Algorithms ◽  
Ant Colony ◽  
Complex Nature ◽  
Machine Loading ◽  
Loading Problem

Artificial intelligence (AI) refers to intelligence artificially realized through computation. AI has emerged as one of the promising computer science discipline originated in mid-1950. Over the past few decades, AI based random search algorithms, namely, genetic algorithm, ant colony optimization, and so forth have found their applicability in solving various real-world problems of complex nature. This chapter is mainly concerned with the application of some AI based random search algorithms, namely, genetic algorithm (GA), ant colony optimization (ACO), simulated annealing (SA), artificial immune system (AIS), and tabu search (TS), to solve the machine loading problem in flexible manufacturing system. Performance evaluation of the aforementioned search algorithms have been tested over standard benchmark dataset. In addition, the results obtained from them are compared with the results of some of the best heuristic procedures in the literature. The objectives of the present chapter is to make the readers fully aware about the intricate solutions existing in the machine loading problem of flexible manufacturing systems (FMS) to exemplify the generic procedure of various AI based random search algorithms. Also, the present chapter describes the step-wise implementation of search algorithms over machine loading problem.

A Hybrid Ant Colony Optimization Algorithm for Permutation Flow-Shop Scheduling in Manufacturing Systems and Industrial Process

2013 ◽  
Vol 345 ◽  
pp. 438-441
Author(s):  
Jing Chen ◽  
Xiao Xia Zhang ◽  
Yun Yong Ma
Keyword(s):  
Ant Colony Optimization ◽  
Hybrid Algorithm ◽  
Manufacturing Systems ◽  
Flow Shop ◽  
Ant Colony ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

This paper presents a novel hybrid ant colony optimization approach (ACO&VNS) to solve the permutation flow-shop scheduling problem (PFS) in manufacturing systems and industrial process. The main feature of this hybrid algorithm is to hybridize the solution construction mechanism of the ant colony optimization (ACO) with variable neighborhood search (VNS) which can also be embedded into the ACO algorithm as neighborhood search to improve solutions. Moreover, the hybrid algorithm considers both solution diversification and solution quality. Finally, the experimental results for benchmark PFS instances have shown that the hybrid algorithm is very efficient to solve the permutation flow-shop scheduling in manufacturing engineering compared with the best existing methods in terms of solution quality.

A Hybrid Ant Colony Optimization and Simulated Annealing Algorithm for Multi-Objective Scheduling of Cellular Manufacturing Systems

2020 ◽  
Vol 11 (3) ◽  
pp. 1-40 ◽  
Author(s):  
Aidin Delgoshaei ◽  
Ahad Ali
Keyword(s):  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Manufacturing Systems ◽  
Simulated Annealing Algorithm ◽  
Cellular Manufacturing ◽  
Ant Colony ◽  
Mixed Integer ◽  
Production Plant ◽  
Manufacturing Companies ◽  
Multi Objective

During the last 2 decades, there have been many manufacturing companies in various industries that used the advantages of cellular manufacturing layouts. However, determining the best schedule for cellular layouts considering uncertain product demands is a big concern for scientists. In this research, a multi-objective decision-making model is proposed in the process of dynamic cellular production planning where the market demands are uncertain. In this regard, a non-linear mixed integer programming model is developed. The complexity of the model is high to consider the model as NP-hard. Therefore, a hybrid Ant colony Optimization and Simulated Annealing Algorithms are proposed to solve the problem. Then, the Taguchi method is used to estimate appropriate sets of parameters of the proposed algorithm. The results demonstrated that the proposed algorithm can generate the best part-routes of products in terms of time, cost and load variance in a reasonable time. The algorithm is then used for a cellular production plant which is the producer of heavy vehicles parts.

scholarly journals Simultaneous Scheduling of Machines and AGVs in FMS Through Ant Colony Optimization Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 1392-1397
Keyword(s):  
Ant Colony Optimization ◽  
Manufacturing Systems ◽  
Material Handling ◽  
Manufacturing System ◽  
Response Times ◽  
Life Cycles ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Ant Colony Optimization Algorithm ◽  
Simultaneous Scheduling

High amount of flexibility and quick response times have become essential features of modern manufacturing systems where customers are demanding a variety of products with reduced product life cycles. Flexible manufacturing system (FMS) is the right choice to achieve these challenging tasks. The performance of FMS is dependent on the selection of scheduling policy of the manufacturing system. In Traditional scheduling problems machines are as considered alone. But material handling equipment’s are also valuable resources in FMS. The scheduling of AGVs is needed to be optimized and harmonized with machine operations. Scheduling in FMS is a well-known NP-hard problem due to considerations of material handling and machine scheduling. Many researchers addressed machine and AGVs individually. In this work an attempt is made to schedule both the machines and AGVs simultaneously. For solving these problems-a new metaheuristic Ant Colony Optimization (ACO) algorithm is proposed.

Parallel Ant Colony Optimization on the University Course-Faculty Timetabling Problem in MSU-IIT

2012 ◽  
Author(s):  
Earth B. Ugat ◽  
Jennifer Joyce M. Montemayor ◽  
Mark Anthony N. Manlimos ◽  
Dante D. Dinawanao
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Timetabling Problem ◽  
University Course ◽  
The University
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