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http://hdl.handle.net/11452/29132
Title: | A genetic algorithm for minimizing energy consumption in warehouses |
Authors: | Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü. 0000-0002-2971-2701 0000-0002-5075-0876 Ene, Seval Küçükoğlu, İlker Aksoy, Aslı Öztürk, Nursel D-8543-2015 AAG-9235-2021 AAG-8949-2021 AAG-9336-2021 48461146800 55763879600 35221094400 7005688805 |
Keywords: | Thermodynamics Energy & fuels Genetic algorithm Green supply chain Minimization of energy consumption Warehouse management Order-picking Storage assignment Travel distance Policies Design Green Algorithms Energy policy Energy utilization Genetic algorithms Industrial management Product design Supply chain management Environmental issues Green supply chain Green supply chain management Impact on the environment Manufacturing process Storage assignment policies Supply chain operation Warehouse management Energy conservation Environmental issue Genetic algorithm Manufacturing Supply chain management Warehouses |
Issue Date: | 1-Nov-2016 |
Publisher: | Pergamon-Elsevier Science |
Citation: | Ene, S. vd. (2016). "A genetic algorithm for minimizing energy consumption in warehouses". Energy, 114, 973-980. |
Abstract: | Green supply chain Management is generally defined as integration of green thinking and environmental issues into the whole supply chain operations like product design, manufacturing process, warehousing, distribution etc. Within this context green principles should be adopted in warehouse management to minimize negative impact on the environment. In warehouse operations, picking must be analyzed attentively which is widely studied in literature for minimizing service time levels because of its close relation to the higher costs. The efficiency of picking in warehouses mainly depends on storage assignment policy that directly affects picking performance in warehouses. In this paper, picking operation in warehouses is studied to minimize energy consumption with proper storage policy other than service time. Genetic algorithm (GA) is proposed to solve the problem and numerical examples are presented to demonstrate the performance of the GA. Results show that, the GA gives efficient solutions to the problem. |
URI: | https://doi.org/10.1016/j.energy.2016.08.045 https://www.sciencedirect.com/science/article/pii/S0360544216311586 http://hdl.handle.net/11452/29132 |
ISSN: | 0360-5442 1873-6785 |
Appears in Collections: | Scopus Web of Science |
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