scholarly journals Deadline Constrained Workflow Scheduling Optimization by Initial Seeding with ANT Colony Optimization

2016 ◽  
Vol 155 (14) ◽  
pp. 24-29 ◽  
Author(s):  
Neel Sinha ◽  
Vishesh Srivastav ◽  
Waquar Ahmad
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Workflow Scheduling ◽  
Scheduling Optimization ◽  
Initial Seeding

scholarly journals Jswa An Improved Algorithm For Grid Workflow Scheduling Using Ant Colony Optimization

2013 ◽  
Vol 06 (04) ◽  
pp. 315-331 ◽  
Author(s):  
Emetis Niazmand ◽  
Javad Bayrampoor ◽  
Arash Ghorbannia Delavar ◽  
Ali Reza Khalili Boroujeni
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Workflow Scheduling ◽  
Grid Workflow ◽  
Improved Algorithm

Modified Ant Colony Optimization for Workflow Scheduling in Cloud Enviornment

2015 ◽  
Vol 14 (10) ◽  
pp. 6176-6183
Author(s):  
S.J. Mohana ◽  
Dr.M. Saroja ◽  
Dr.M. Venkatachalam
Keyword(s):  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Heuristic Algorithms ◽  
Ant Colony ◽  
Workflow Scheduling ◽  
Ant Colony Optimization Algorithm ◽  
Computing Environment ◽  
Shared Resources ◽  
Cloud Computing Environment ◽  
Computing Model

Cloud computing is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. This technological trend has enabled the realization of a new computing model called cloud computing, in which shared resources, information,software & other devices are provided according to client requirement at specific time, are provided as general utilities that can be leased and released by users through the Internet in an on-demand fashion.Cloud workflow scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it.Allocation of resources to a large number of workflows in a cloud computing environment presents more difficulty than in network computational environments.A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this work, modified ant colony optimization for cloud task scheduling is proposed. The goal of modification is to enhance the performance of the basic ant colony optimization algorithm and optimize the task execution time in view of minimizing the makespan of a given tasks set.

scholarly journals Ant Colony Optimization Algorithm for Maintenance, Repair and Overhaul Scheduling Optimization in the Context of Industrie 4.0

2019 ◽  
Vol 9 (22) ◽  
pp. 4815 ◽  
Author(s):  
Le Vu Tran ◽  
Bao Huy Huynh ◽  
Humza Akhtar
Keyword(s):  
Ant Colony Optimization ◽  
Dynamic Scheduling ◽  
Ant Colony ◽  
Convergence Time ◽  
Shop Floor ◽  
Manufacturing Processes ◽  
Ant Colony Optimization Algorithm ◽  
Intelligent Algorithm ◽  
Scheduling Optimization ◽  
Tuning Parameters

Maintenance, Repair, and Overhaul (MRO) is a crucial sector in the remanufacturing industry and scheduling of MRO processes is significantly different from conventional manufacturing processes. In this study, we adopted a swarm intelligent algorithm, Ant Colony Optimization (ACO), to solve the scheduling optimization of MRO processes with two business objectives: minimizing the total scheduling time (make-span) and total tardiness of all jobs. The algorithm also has the dynamic scheduling capability which can help the scheduler to cope with the changes in the shop floor which frequently occur in the MRO processes. Results from the developed algorithm have shown its better solution in comparison to commercial scheduling software. The dependency of the algorithm’s performance on tuning parameters has been investigated and an approach to shorten the convergence time of the algorithm is emerging.

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