Flexible Job Shop Scheduling With Operators in Aeronautical Manufacturing: A Case Study

This paper analyzes a flexible job shop scheduling problem with operators and is motivated by a real-life case study of the aeronautical industry, where each process can be performed on alternative machines (shared by multiple products) and requires not only machines but also some operators to execu...

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Bibliographic Details
Published inIEEE access Vol. 6; pp. 224 - 233
Main Authors Borreguero-Sanchidrian, Tamara, Pulido, Raul, Garcia-Sanchez, Alvaro, Ortega-Mier, Miguel
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Summary:This paper analyzes a flexible job shop scheduling problem with operators and is motivated by a real-life case study of the aeronautical industry, where each process can be performed on alternative machines (shared by multiple products) and requires not only machines but also some operators to execute the process. One goal of this scheduling is to minimize the number of operators and airframes that are needed in the assembly line. Minimizing the number of airframes in the assembly line, which is particularly important due to the cost of each airframe, is generally ignored in production planning models that are used in prior studies. Moreover, the output of the program refers to a set of schedules that includes different combinations of number of operators and intermediate stock, to enable a better decision making. A mixed integer linear programming model is presented to solve this problem. Experimentation was conducted using real-life examples from the aeronautical industry. The solutions that are presented in this paper outperformed current industrial methods in both quality and calculation time. To the best of our knowledge, this variant has not been addressed in prior studies either in scheduling or in aeronautics contexts. However, minimizing the number of operators and intermediate stock could have significant implications for numerous labor intensive industries, contributing to enhanced and more agile decision making processes.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2761994