A two-stage Ant Colony optimization algorithm to minimize the makespan on unrelated parallel machines—part II: enhancements and experimentations

In a previous paper (Arnaout et al in J Intell Manuf 21:693–701, 2010 ), an Ant Colony optimization (ACO I) algorithm was introduced for minimizing the schedule’s makespan on unrelated parallel machines with sequence-dependent setup times. Optimal solutions for small instances of this problem were o...

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Bibliographic Details
Published in:Journal of intelligent manufacturing Vol. 25; no. 1; pp. 43 - 53
Main Authors: Arnaout, Jean-Paul, Musa, Rami, Rabadi, Ghaith
Format: Journal Article
Language:English
Published: Boston Springer US 01.02.2014
Springer Nature B.V
Subjects:
Algorithms
Analysis
Business and Management
Control
Heuristic
Integer programming
Job shops
Literature reviews
Machines
Manufacturing
Manufacturing execution systems
Mechatronics
Optimization
Optimization algorithms
Parallel processing
Processes
Production
Ratios
Robotics
Scheduling
Studies
Unrelated parallel machine
Sequence-dependent setup times
Scheduling
Ant Colony optimization
ISSN:0956-5515, 1572-8145
Online Access:Get full text
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Summary:In a previous paper (Arnaout et al in J Intell Manuf 21:693–701, 2010 ), an Ant Colony optimization (ACO I) algorithm was introduced for minimizing the schedule’s makespan on unrelated parallel machines with sequence-dependent setup times. Optimal solutions for small instances of this problem were obtained by solving a mixed integer program. However, for larger instances (up to 10 machines and 120 jobs), heuristic and approximate algorithms were necessary to reach solutions in reasonable computational times. ACO I’s performance was evaluated by comparing its solutions to solutions obtained using Tabu Search and MetaRaPS (metaheuristic for Randomized Priority Search). While the results indicated that ACO I outperformed the other heuristics, it also showed that MetaRaPS had a better performance when all ratios of N/M (jobs to machines ratio) were considered. In this paper, we introduce an enhanced ACO which will be referred to as ACO II and compare its performance to other existing and new algorithms including ACO I, MetaRaPS, and SA. The extensive and expanded experiments conducted prove the superiority of the enhanced ACO II.
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ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-012-0672-3