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:: Volume 1, Issue 2 (Winter 2018) ::
3 2018, 1(2): 92-103 Back to browse issues page
Optimization of a Supply Chain Including Warehouses and Retail Stores Using a Data Mining Technique of Improved Greedy Algorithm
Marzieh Faridi Masouleh
Abstract:   (1315 Views)
Objective: Today, the main challenge in the field of supply chain is to focus on goods’ mobility and logistic issues. The right product should be available to the right customer in the fastest way and at the lowest cost. A supply chain consists of several components that play an important role in reducing service costs. The purpose of this paper is to minimize the cost of the supply chain, including start-up costs, holding cost, and transportation costs at different time periods.
Methodology: Due to the complexity of the problem, a data mining technique of improved greedy algorithm has been used to solve it. Since dealing with large databases in the supply chain management is of utmost importance, this paper has proposed the use of data mining methods in the supply chain issue.
Results: Based on the results of the present study, data mining techniques can be used effectively in different parts of the supply chain such as selecting suppliers and distributors, demand predicting, etc.
Conclusion: The results revealed that the greedy algorithm was not able to find the best solution in the search space; therefore, a local search phase was added to improve the solution. The obtained results are indicative of the effectiveness of the proposed algorithm.
Keywords: Optimization, Supply chain, Warehouses, Retail stores, Data mining, Improved greedy algorithm
Full-Text [PDF 386 kb]   (231 Downloads)    
Type of Study: Research |
Published: 2019/08/29
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Faridi Masouleh M. Optimization of a Supply Chain Including Warehouses and Retail Stores Using a Data Mining Technique of Improved Greedy Algorithm. 3. 2018; 1 (2) :92-103
URL: http://somjournal.ir/article-1-32-en.html

Volume 1, Issue 2 (Winter 2018) Back to browse issues page
فصلنامه مدیریت عملیات خدمات Journal of Services Operation Management
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