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Research Papers

Assembly System Configuration Design for Reconfigurability Under Uncertain Production Evolution

[+] Author and Article Information
Zhengqian Jiang

Mem. ASME
Department of Industrial and Manufacturing Engineering,
Florida A&M University-Florida State University College of Engineering,
2525 Pottsdamer St.,
Tallahassee, FL 32310
e-mail: zj14@my.fsu.edu

Hui Wang

Mem. ASME
Department of Industrial and Manufacturing Engineering,
Florida A&M University-Florida State University College of Engineering,
2525 Pottsdamer St.,
Tallahassee, FL 32310
e-mail: hwang10@fsu.edu

Maxim A. Dulebenets

Department of Civil and Environmental Engineering,
Florida A&M University-Florida State University College of Engineering,
2525 Pottsdamer St.,
Tallahassee, FL 32310
e-mail: mdulebenets@eng.famu.fsu.edu

Junayed Pasha

Department of Civil and Environmental Engineering,
Florida A&M University-Florida State University College of Engineering,
2525 Pottsdamer St.,
Tallahassee, FL 32310
e-mail: jp17j@my.fsu.edu

1Corresponding author.

Manuscript received August 3, 2018; final manuscript received April 12, 2019; published online May 3, 2019. Assoc. Editor: Dragan Djurdjanovic.

J. Manuf. Sci. Eng 141(7), 071001 (May 03, 2019) (12 pages) Paper No: MANU-18-1581; doi: 10.1115/1.4043581 History: Received August 03, 2018; Accepted April 15, 2019

Assembly system configuration determines the topological arrangement of stations with defined logical material flow among them. The design of assembly system configuration involves (1) subassembly planning that defines subassembly tasks and between-task material flows and (2) workload balancing that determines the task-station assignments. The assembly system configuration should be flexibly changed and updated to cope with product design evolution and updating. However, the uncertainty in future product evolution poses significant challenges to the assembly system configuration design since the higher cost can be incurred if the assembly line suitable for future products is very different from that for the current products. The major challenges include (1) the estimation of reconfiguration cost, (2) unavailability of probability values for possible scenarios of product evolution, and (3) consideration of the impact of the subassembly planning on the task-station assignments. To address these challenges, this paper formulates a concurrent optimization problem to design the assembly system configuration by jointly determining the subassembly planning and task-station assignments considering uncertain product evolution. A new assembly hierarchy similarity model is proposed to estimate the reconfiguration effort by comparing the commonalities among different subassembly plans of current and potential future product designs. The assembly system configuration is chosen by maximizing both assembly hierarchy similarity and assembly system throughput under the worst-case scenario. A case study motivated by real-world scenarios demonstrates the applicability of the proposed method including scenario analysis.

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Figures

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Fig. 1

A serially linked product design

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Fig. 2

Two possible assembly hierarchies for the product design in Fig. 1

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Fig. 3

Example of bypassing tasks and end-idle tasks

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Fig. 4

Illustration of potential reconfiguration effort in Fig. 3

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Fig. 5

A simple product evolution from design (1) to (2)

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Fig. 6

Assembly hierarchies for product design (1) and (2) in Fig. 5

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Fig. 7

An example chromosome representation for 6-task and 3-station problem

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Fig. 8

Joint-precedence diagram for one product design and two future evolutions

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Fig. 9

An illustration of the proposed crossover operator (change the task sequence)

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Fig. 10

An illustration of the mutation operator (changing the task-station indicator)

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Fig. 11

Existing product design with three future product evolutions

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Fig. 12

Joint-precedence diagram

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Fig. 13

Assembly system reconfiguration design under scenario 1

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Fig. 14

Assembly system reconfiguration planning for scenario 2

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Fig. 15

Assembly system reconfiguration planning for scenario 3

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Fig. 16

Convergence pattern for three scenarios.

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