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Research Papers

Agent Based Simulation Optimization of Waste Electrical and Electronics Equipment Recovery

[+] Author and Article Information
Ardeshir Raihanian Mashhadi

Graduate Research Assistant
Department of Mechanical and
Aerospace Engineering,
University at Buffalo,
State University of New York,
Buffalo, NY 14260
e-mail: ardeshir@buffalo.edu

Sara Behdad

Assistant Professor
Department of Mechanical and
Aerospace Engineering,
Industrial and Systems Engineering Department,
University at Buffalo,
State University of New York,
Buffalo, NY 14260
e-mail: sarabehd@buffalo.edu

Jun Zhuang

Associate Professor
Industrial and Systems Engineering Department,
University at Buffalo,
State University of New York,
Buffalo, NY 14260
e-mail: jzhuang@buffalo.edu

Manuscript received February 28, 2016; final manuscript received July 5, 2016; published online August 10, 2016. Assoc. Editor: Karl R. Haapala.

J. Manuf. Sci. Eng 138(10), 101007 (Aug 10, 2016) (11 pages) Paper No: MANU-16-1129; doi: 10.1115/1.4034159 History: Received February 28, 2016; Revised July 05, 2016

The profitability of electronic waste (e-waste) recovery operations is quite challenging due to various sources of uncertainties in the quantity, quality, and timing of returns originating from consumers' behavior. The cloud-based remanufacturing concept, data collection, and information tracking technologies seem promising solutions toward the proper collection and recovery of product life cycle data under uncertainty. A comprehensive model that takes every aspect of recovery systems into account will help policy makers perform better decisions over a planning horizon. The objective of this study is to develop an agent based simulation (ABS) framework to model the overall product take-back and recovery system based on the product identity data available through cloud-based remanufacturing infrastructure. Sociodemographic properties of the consumers, attributes of the take-back programs, specific characteristics of the recovery process, and product life cycle information have all been considered to capture the optimum buy-back price (bbp) proposed for a product with the aim of controlling the timing and quality of incoming used products to collection sites for recovery. A numerical example of an electronic product take-back system and a simulation-based optimization are provided to illustrate the application of the model.

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Figures

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Fig. 1

The buy-back price offered by the trade-in program for four different models of cellphones with different quality condition

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Fig. 2

Histogram of the number of returns after 1800 simulation days. This figure is based on 100 simulation runs with different random seeds.

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Fig. 3

Histogram of simulated revenue for each EoL process. a) Refurbishing, b) remanufacturing, and c) recycling.

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Fig. 4

Total number of returned products to the manufacturer per different initial buy-back prices

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Fig. 5

Distribution of quality grade of products received by the manufacturer for bbp = $103. The red line indicates the mean.

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Fig. 6

Distribution of quality grade of products received by the manufacturer for bbp = $120. The red line indicates the mean.

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Fig. 7

Distribution of quality grade of products received by the manufacturer for bbp = $140. The red line indicates the mean.

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Fig. 8

Distribution of quality grade of products received by the manufacturer for bbp = $160. The red line indicates the mean.

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Fig. 9

Distribution of the recovery profit of the manufacturer for bbp = $103. The red line indicates the mean.

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Fig. 10

Distribution of the recovery profit of the manufacturer for bbp = $120. The red line indicates the mean.

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Fig. 11

Distribution of the recovery profit of the manufacturer for bbp = $140. The red line indicates the mean.

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Fig. 12

Distribution of the recovery profit of the manufacturer for bbp = $160. The red line indicates the mean.

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Fig. 13

Result of the optimization

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