@article {10.3844/ajassp.2015.142.154,
article_type = {journal},
title = {Ant Colony Optimization for Load Management Based on Load Shifting in the Textile Industry},
author = {Chokpanyasuwan, Chaimongkon and Bunnag, Tika and Prommas, Ratthasak},
volume = {12},
year = {2015},
month = {Apr},
pages = {142-154},
doi = {10.3844/ajassp.2015.142.154},
url = {https://thescipub.com/abstract/ajassp.2015.142.154},
abstract = {The textile industry is a complicated manufacturing industry because it is a fragmented and heterogeneous sector dominated by Small and Medium Enterprises (SMEs). There are various energy-efficiency opportunities that exist in every textile plant. However, even cost-effective options often are not implemented in textile plants mostly because of limited information on how to implement energy-efficiency measures. This paper presents the expansion of problem formulation of consummation management based on load shifting in textile industry. The mathematical model is a Non Polynomial (NP) hard optimization problem to determine the start time of the process in order to minimize the total electricity cost under varying tariffs such as flat rate and Time of Use (TOU). For solve this problem, Ant Colony Optimization (ACO) is applied and compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). To show its efficiency, the case studies in case of Single Process Multiple Jobs (SPMJ) in term of small, medium and large scales are demonstrated.  The results show that the proposed method is able to achieve the best solution efficiently and easy to implement.},
journal = {American Journal of Applied Sciences},
publisher = {Science Publications}
}