Multiobjective Order Assignment Optimization in a Global Multiple‐Factory Environment
Author(s) -
Rong-Chang Chen,
Pei-Hsuan Hung
Publication year - 2014
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2014/673209
Subject(s) - factory (object oriented programming) , order (exchange) , aggregate (composite) , production (economics) , genetic algorithm , competition (biology) , computer science , multi objective optimization , mathematical optimization , range (aeronautics) , operations research , industrial engineering , engineering , business , mathematics , economics , microeconomics , biology , composite material , programming language , ecology , materials science , aerospace engineering , finance
In response to radically increasing competition, many manufacturers who produce time-sensitive products have expanded their production plants to worldwide sites. Given this environment, how to aggregate customer orders from around the globe and assign them quickly to the most appropriate plants is currently a crucial issue. This study proposes an effective method to solve the order assignment problem of companies with multiple plants distributed worldwide. A multiobjective genetic algorithm (MOGA) is used to find solutions. To validate the effectiveness of the proposed approach, this study employs some real data, provided by a famous garment company in Taiwan, as a base to perform some experiments. In addition, the influences of orders with a wide range of quantities demanded are discussed. The results show that feasible solutions can be obtained effectively and efficiently. Moreover, if managers aim at lower total costs, they can divide a big customer order into more small manufacturing ones
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