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Research Papers

Cost Analysis and Fuzzy Control for Collapsible Container Usage Based on Closed-Loop Supply Chain Model

[+] Author and Article Information
Xinyan Ou

Department of Mechanical Engineering,
Stony Brook University,
101 Heavy Engineering,
Stony Brook, NY 11794
e-mail: xinyan.ou@stonybrook.edu

Jorge Arinez

Manufacturing Systems Research Laboratory,
General Motors Research and
Development Center,
30500 Mound Road,
Warren, MI 48090
e-mail: jorge.arinez@gm.com

Qing Chang

Department of Mechanical Engineering,
Stony Brook University,
163 Light Engineering,
Stony Brook, NY 11794
e-mail: qing.chang@stonybrook.edu

Guoxian Xiao

Manufacturing Systems Research Laboratory,
General Motors Research and
Development Center,
30500 Mound Road,
Warren, MI 48090
e-mail: guoxian.xiao@gm.com

1Corresponding author.

Manuscript received July 12, 2016; final manuscript received April 6, 2017; published online May 8, 2017. Assoc. Editor: Dragan Djurdjanovic.

J. Manuf. Sci. Eng 139(8), 081005 (May 08, 2017) (11 pages) Paper No: MANU-16-1378; doi: 10.1115/1.4036522 History: Received July 12, 2016; Revised April 06, 2017

In the last decade, global competition has forced manufacturers to optimize logistics. The implementation of collapsible containers provides a new perspective for logistics cost savings, since using collapsible containers reduces the frequency of shipping freight. However, optimization of logistic cost is complicated due to the interactions in a system, such as market demand, inventory, production throughput, and uncertainty. Therefore, a systematic model and accurate estimation of the total cost and system performance are of great importance for decision making. In this paper, a mathematical model is developed to describe deterministic and stochastic scenarios for a closed-loop container dynamic flow system. The uncertainties in a factory and a supplier are considered in the model. The performance evaluation of the collapsible container system and total cost estimation are provided through model analysis. Furthermore, fuzzy control method is proposed to monitor the processing rate of the supplier and the factory and to adjust the rate of the supplier operation then further reduce the logistic cost. A case study with a matlab simulation is presented to illustrate the accuracy of the mathematical model and the effectiveness of the fuzzy controller.

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Figures

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Fig. 1

Closed-lined supply chain (CLSC)

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Fig. 2

Layout of closed-lined supply chain

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Fig. 3

Container counts when collapsible containers are sufficient

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Fig. 4

Container counts when collapsible containers are insufficient

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Fig. 5

Container count with a supplier downtime event

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Fig. 6

Container count with a factory downtime event

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Fig. 7

Conversion from closed line to open line

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Fig. 8

Container production rate comparison between closed and open lines

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Fig. 9

Extra container number versus bsi(0) with different collapsible rates

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Fig. 10

Cost saving versus bsi(0) with different collapsible rates

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Fig. 11

Fuzzy controller schematic

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Fig. 12

Comparison between the simulation result and themathematical result: (a) Collapsible rate CR=M/m=3, (b)collapsible rate CR=M/m=4, and (c) collapsible rate CR=M/m=5

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Fig. 13

Degree of membership of inputs and output

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Fig. 14

Container count of supplier and factory with fuzzy controller

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Fig. 15

Buffer level bfi with fuzzy controller

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Fig. 16

Cost saving comparison with CR = 3

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Fig. 17

Cost saving comparison with CR = 4

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Fig. 18

Cost saving comparison with CR = 5

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