Fuzzy Cell Genetic Algorithm Approach for Flexible Flow-Line Scheduling Model

被引：0
|
作者：
Shirazi, Arash Nasrolahi [1 ]
Steinhaus, Meghan [2 ]
Agostinelli, Matthew [1 ]
Sodhi, Manbir [1 ]
机构：
[1] Univ Rhode Isl, Dept Mech Ind & Syst Engn, Kingston, RI 02881 USA
[2] US Coast Guard Acad, Dept Math, New London, CT 06320 USA
来源：
PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) | 2016年
关键词：
FAMILY SETUP TIMES; LIMITED INTERMEDIATE BUFFERS; UNRELATED PARALLEL MACHINES; BOUND ALGORITHM; MULTIPLE PROCESSORS; PRODUCTION SYSTEM; HYBRID FLOWSHOP; BRANCH; SHOP; JOBS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper focuses on makespan minimization for the flow line scheduling problem using a Fuzzy Cell Genetic Algorithm (FCGA). Real world applications of this problem are commonly found in printing and electronic circuit board manufacturing industries. A generalized integer programming (IP) model for this problem is proposed. The Fuzzy Cell Genetic Algorithm (FCGA) is proposed to solve the IP model, which has been proven to be NP-hard. Sample problems are generated with known good solutions to evaluate the effectiveness of the FCGA approach. The FCGA matches the performance of the IP model for small sized problem instances and it is proven to be effective for larger problem instances.
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页数：7
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