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A non‐linear multi‐criteria model for strategic fms selection decisions
Author(s) -
Kuula Markku,
Stam Antonie,
Ranta Jukka
Publication year - 1992
Publication title -
journal of multi‐criteria decision analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.462
H-Index - 47
eISSN - 1099-1360
pISSN - 1057-9214
DOI - 10.1002/mcda.4020010306
Subject(s) - flexibility (engineering) , computer science , production (economics) , process (computing) , throughput , selection (genetic algorithm) , operations research , decision support system , industrial engineering , product (mathematics) , software , linear programming , mathematical optimization , data mining , engineering , artificial intelligence , mathematics , algorithm , economics , telecommunications , statistics , geometry , wireless , macroeconomics , programming language , operating system
The strategic decision of selecting an optimal flexible manufacturing system (FMS) configuration is a complicated question which involves evaluating trade‐offs between a number of different, potentially conflicting criteria such as annual production volume, flexibility, production and investment costs and average throughput of the system. Recently, several structured multicriteria approaches have been proposed to aid management in the FMS selection process. While acknowledging the non‐linear nature of a number of the relationships in the model, notably between batch size and the number of batches produced of each part, these studies used linear simplifications to illustrate the decision dynamics of the problem. These linear models were shown to offer useful analytical tools in the FMS pre‐design process. Owing to the non‐linearities of the true relationships, however, the trade‐offs between the criteria could not fully be explored within the linear framework. This paper builds on the two‐phase decision support framework proposed by Stam and Kuula (1991) and uses a modified non‐linear multi‐criteria formulation to solve the problem. The software used in the illustration can easily be implemented, is user‐interactive and menu‐driven. The methodology is applied to real data from a Finnish metal product company and the results are compared with those obtained in previous studies.