An Enhanced Intelligent Water Drops Algorithm for Scheduling of an Agile Manufacturing System

Ehsan Teymourian, Vahid Kayvanfar (), Gh. M. Komaki and Majtaba Khodarahmi
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Ehsan Teymourian: School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR 97331-6001, USA
Vahid Kayvanfar: #x2020;Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Ave., 15875-4413 Tehran, Iran
Gh. M. Komaki: #x2021;Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Ave., Cleveland, OH 44106, USA
Majtaba Khodarahmi: #xA7;Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran

International Journal of Information Technology & Decision Making (IJITDM), 2016, vol. 15, issue 02, 239-266

Abstract: This paper deals with an agile manufacturing system with two stages where the first stage is the machining stage including a single flexible machine while the second stage is assembly stage including parallel identical assembly machines. Products have an assembly structure where their parts should be processed at the first stage and then proceed to the assembly stage. The goal is to find the parts sequence which minimizes makespan. Since the studied problem is NP-hard, enhanced intelligent water drops (EIWD) algorithm as a new swarm-based nature inspired optimization algorithm is proposed. Also, artificial immune system (AIS) algorithm has been proposed to tackle the addressed problem. Computational results based on randomly generated instances show the effectiveness of the proposed technique.

Keywords: Agile manufacturing system; intelligent water drops algorithm; iterated local search algorithm (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219622016500024

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