A two-phase optimization algorithm in controller synthesis

2000 ◽  
Author(s):  
Long-Life Show ◽  
Jyh-Ching Juang ◽  
Ciann-Dong Yang
Keyword(s):  
Optimization Algorithm ◽  
Controller Synthesis ◽  
Two Phase ◽  
Phase Optimization

A Two-Phase Optimization Algorithm For Mastermind

2007 ◽  
Vol 50 (4) ◽  
pp. 435-443 ◽  
Author(s):  
Shan-Tai Chen ◽  
Shun-Shii Lin ◽  
Li-Te Huang
Keyword(s):  
Optimization Algorithm ◽  
Two Phase ◽  
Phase Optimization

scholarly journals An Adaptive Phase Optimization Algorithm for Distributed Scatterer Phase History Retrieval

2021 ◽  
Vol 14 ◽  
pp. 3914-3926
Author(s):  
Shijin Li ◽  
Shubi Zhang ◽  
Tao Li ◽  
Yandong Gao ◽  
Qianfu Chen ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
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

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

A Two-Phase Meta Heuristic Approach to the Location-Routing Problem for Transportation Network Planning

2013 ◽  
Vol 361-363 ◽  
pp. 1900-1905 ◽  
Author(s):  
Ji Ung Sun
Keyword(s):  
Ant Colony Optimization ◽  
Facility Location ◽  
Vehicle Routing ◽  
Optimization Algorithm ◽  
Transportation Network ◽  
Ant Colony Optimization Algorithm ◽  
Two Phase ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing

In this paper we consider the location-routing problem which combines the facility location and the vehicle routing decisions. In this type of problem, we have to determine the location of facilities within a set of possible locations and routes of the vehicles to meet the demands of number of customers. Since the location-routing problem is NP-hard, it is difficult to obtain optimal solution within a reasonable computational time. Therefore, a two-phase ant colony optimization algorithm is developed which solves facility location problem and vehicle routing problem hierarchically. Its performance is examined through a comparative study. The experimental results show that the proposed ant colony optimization algorithm can be a viable solution method for the general transportation network planning.

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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