Two-phase optimization for the design of multiple coils

2005 ◽  
Vol 41 (10) ◽  
pp. 4093-4095 ◽  
Author(s):  
Woochul Kim ◽  
Jae Eun Kim ◽  
Yoon Young Kim
Keyword(s):  
Two Phase ◽  
Phase Optimization

scholarly journals Two-Phase Optimization Method for Determining Distribution Center Locations and Distribution Routes (Case Study: X Ltd.)

2020 ◽  
Vol 0 (5) ◽  
pp. 45
Author(s):  
Muhammad Rayhan Azzindani ◽  
Nabila Fajri Kusuma Ningrum ◽  
Mega Rizkah Sudiar ◽  
Anak Agung Ngurah Perwira Redi
Keyword(s):  
Optimization Method ◽  
Distribution Center ◽  
Two Phase ◽  
Phase Optimization

scholarly journals Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS

2014 ◽  
Vol 24 (3) ◽  
pp. 669-682 ◽  
Author(s):  
D. Thresh Kumar ◽  
Hamed Soleimani ◽  
Govindan Kannan
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Supply Chain Design ◽  
Second Phase ◽  
Planning Problem ◽  
Two Phase ◽  
Closed Loop Supply Chain ◽  
Inference System ◽  
Forecasting Method ◽  
Phase Optimization

Abstract Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network

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