Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system

Yang-Kuei Lin () and Chin Soon Chong ()
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Yang-Kuei Lin: Feng Chia University
Chin Soon Chong: A-star

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 5, No 10, 1189-1201

Abstract: Abstract Cloud manufacturing is becoming an increasingly popular enterprise model in which computing resources are made available on-demand to the user as needed. Cloud manufacturing aims at providing low-cost, resource-sharing and effective coordination. In this study, we present a genetic algorithm (GA) based resource constraint project scheduling, incorporating a number of new ideas (enhancements and local search) for solving computing resources allocation problems in a cloud manufacturing system. A newly generated offspring may not be feasible due to task precedence and resource availability constraints. Conflict resolutions and enhancements are performed on newly generated offsprings after crossover or mutation. The local search can exploit the neighborhood of solutions to find better schedules. Due to its complex characteristics, computing resources allocation in a cloud manufacturing system is NP-hard. Computational results show that the proposed GA can rapidly provide a good quality schedule that can optimally allocate computing resources and satisfy users’ demands.

Keywords: Resource allocation; Cloud manufacturing; Project scheduling; Genetic algorithm (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10845-015-1074-0

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