Please use this identifier to cite or link to this item: 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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