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Optimal Operator Allocation in Semic...

Patil, Aniket Kamalakar,

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Optimal Operator Allocation in Semiconductor Manufacturing Industry Using MILP Model /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimal Operator Allocation in Semiconductor Manufacturing Industry Using MILP Model // Aniket Kamalakar Patil.
作者:	Patil, Aniket Kamalakar,
面頁冊數:	1 electronic resource (59 pages)
附註:	Source: Masters Abstracts International, Volume: 85-04.
Contained By:	Masters Abstracts International85-04.
標題:	Industrial engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30636853
ISBN:	9798380605281

Optimal Operator Allocation in Semiconductor Manufacturing Industry Using MILP Model /
Patil, Aniket Kamalakar,

Optimal Operator Allocation in Semiconductor Manufacturing Industry Using MILP Model /Aniket Kamalakar Patil. - 1 electronic resource (59 pages)

Source: Masters Abstracts International, Volume: 85-04.

Operator allocation is one of the most important factors when it comes to the semiconductor contract manufacturing industry. Most of the semiconductor manufacturing industry uses intensive manual labor to produce electronic products. Since productivity is one of the most important parameters for contract manufacturers, it is crucial to have the assembly line optimized to increase productivity. In some industries, operators are allocated randomly to operate manual assembly stations. It is difficult to identify if the highly efficient operator has been allocated to all the stations since the operators are not compared for their performance. This thesis focuses on the topic of maximizing the productivity of the assembly line by the allocation of operators using a mixed integer linear programming mathematical model.A case study was conducted to identify the performance and variance of four operators working on four stations. All the operators were trained to work on all manual stations. As each operator has a different capacity to work from the start to the end of the shift, a time study methodology was conducted in three different time phases 8 am to 11 am, 11 pm to 2 pm, and 2 pm to 5 pm. During these time phases, the performance of all operators was recorded. Based on the data, there was a variation in the operator's performance for each time phase, and then the mathematical model was created using the PULP library on Python software. The model successfully allocated the highly efficient operator to the correct station at that specific time phase, which resulted in an increase in the overall productivity of the assembly line by 10% and the quarterly profit of the company by $92120 USD.

English

ISBN: 9798380605281Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Optimal operator allocation

Optimal Operator Allocation in Semiconductor Manufacturing Industry Using MILP Model /
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500 $a Advisors: Wang, Zimo Committee members: Deng, Jia; Chen, Changqing.
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520 $a Operator allocation is one of the most important factors when it comes to the semiconductor contract manufacturing industry. Most of the semiconductor manufacturing industry uses intensive manual labor to produce electronic products. Since productivity is one of the most important parameters for contract manufacturers, it is crucial to have the assembly line optimized to increase productivity. In some industries, operators are allocated randomly to operate manual assembly stations. It is difficult to identify if the highly efficient operator has been allocated to all the stations since the operators are not compared for their performance. This thesis focuses on the topic of maximizing the productivity of the assembly line by the allocation of operators using a mixed integer linear programming mathematical model.A case study was conducted to identify the performance and variance of four operators working on four stations. All the operators were trained to work on all manual stations. As each operator has a different capacity to work from the start to the end of the shift, a time study methodology was conducted in three different time phases 8 am to 11 am, 11 pm to 2 pm, and 2 pm to 5 pm. During these time phases, the performance of all operators was recorded. Based on the data, there was a variation in the operator's performance for each time phase, and then the mathematical model was created using the PULP library on Python software. The model successfully allocated the highly efficient operator to the correct station at that specific time phase, which resulted in an increase in the overall productivity of the assembly line by 10% and the quarterly profit of the company by $92120 USD.
546 $a English
590 $a School code: 0792
650 4 $a Industrial engineering. $3 526216
650 4 $a Systems science. $3 3168411
653 $a Optimal operator allocation
653 $a Semiconductor contract
653 $a Manufacturing industry
653 $a MILP model
653 $a Electronic products
690 $a 0546
690 $a 0790
710 2 $a State University of New York at Binghamton. $b Systems Science Industrial Engineering. $e degree granting institution. $3 3765714
720 1 $a Wang, Zimo $e degree supervisor.
773 0 $t Masters Abstracts International $g 85-04.
790 $a 0792
791 $a M.S.
792 $a 2023
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