Modeling electronic waste recovery systems under uncertainty

Author(s):  
Boma M. Brown-West ◽  
Jeremy R. Gregory ◽  
Randolph E. Kirchain
Author(s):  
Zoey Laskaris ◽  
Stuart A. Batterman ◽  
John Arko‐Mensah ◽  
Bhramar Mukherjee ◽  
Julius N. Fobil ◽  
...  

2015 ◽  
Vol 45 ◽  
pp. 374-384 ◽  
Author(s):  
Jenni Ylä-Mella ◽  
Riitta L. Keiski ◽  
Eva Pongrácz

2020 ◽  
Vol 161 ◽  
pp. 104841 ◽  
Author(s):  
Megan Kramer Jaunich ◽  
Joseph DeCarolis ◽  
Robert Handfield ◽  
Eda Kemahlioglu-Ziya ◽  
S. Ranji Ranjithan ◽  
...  

2020 ◽  
Vol 22 (3) ◽  
pp. 495-512 ◽  
Author(s):  
Gökçe Esenduran ◽  
Yen-Ting Lin ◽  
Wenli Xiao ◽  
Minyue Jin

Problem definition: We consider two competing electronic waste (e-waste) recovery channels, each of which consists of a collector and a recycler. Collectors obtain donated e-waste and sell the collected items to recyclers or in the secondary market, whereas recyclers process e-waste and sell the recycled material in the commodity market. Each recycler chooses for certification of one of two standards: e-Stewards or Responsible Recycling (R2). E-Stewards requires comparably more responsible handling, thus a higher processing cost, but attracts more e-waste from environmentally conscious donors. Academic/practical relevance: Despite the rapid growth of e-waste, the operations management community still understands little about e-waste processing supply chains. We add to this body of knowledge by capturing three salient features in the e-waste recovery industry: the existence of two recycling standards, the secondary market, and competition both within and between recovery channels. Methodology: We model the problem as a Stackelberg game and characterize the firms’ equilibrium decisions, deriving managerial insights through sensitivity analysis and numerical studies. Results: Competition between recovery channels is a key factor motivating e-Stewards adoption, whereas a recycler always chooses R2 in its absence. Interestingly, when competition exists both within and between recovery channels, recyclers with strong e-waste processing scale economies choose e-Stewards when incurring significantly higher processing costs than with R2. Furthermore, both the total environmental benefit and welfare might be higher when recyclers choose R2. Managerial implications: Policy makers who aim to encourage e-Stewards adoption should (1) lower entry barriers for new recyclers to induce competition, and (2) offer incentive programs to alleviate e-Stewards’ cost disadvantage, though only when recyclers have weak scale economies. Policy makers and nongovernmental organizations, however, should exercise caution in endorsing e-Stewards because R2 actually may generate a higher environmental benefit because of higher recycling volumes.


2017 ◽  
Vol 60 ◽  
pp. 521-533 ◽  
Author(s):  
Garrath T. Wilson ◽  
Grace Smalley ◽  
James R. Suckling ◽  
Debra Lilley ◽  
Jacquetta Lee ◽  
...  

2014 ◽  
Vol 986-987 ◽  
pp. 2167-2170
Author(s):  
Li Gang Sun ◽  
Zheng Zhang

Considering the uncertainty of electronic waste recovery quantity in consumption areas, a fuzzy optimization model for electronic waste reverse logistics network with capacity constraints was constructed to determine the number and location of the facilities, the flows between each facility. A numerical example was provided to demonstrate the feasibility of the model.


2019 ◽  
Vol 63 (8) ◽  
pp. 829-841 ◽  
Author(s):  
Zoey Laskaris ◽  
Chad Milando ◽  
Stuart Batterman ◽  
Bhramar Mukherjee ◽  
Niladri Basu ◽  
...  

Abstract Objectives Approximately 2 billion workers globally are employed in informal settings, which are characterized by substantial risk from hazardous exposures and varying job tasks and schedules. Existing methods for identifying occupational hazards must be adapted for unregulated and challenging work environments. We designed and applied a method for objectively deriving time-activity patterns from wearable camera data and matched images with continuous measurements of personal inhalation exposure to size-specific particulate matter (PM) among workers at an informal electronic-waste (e-waste) recovery site. Methods One hundred and forty-two workers at the Agbogbloshie e-waste site in Accra, Ghana, wore sampling backpacks equipped with wearable cameras and real-time particle monitors during a total of 171 shifts. Self-reported recall of time-activity (30-min resolution) was collected during the end of shift interviews. Images (N = 35,588) and simultaneously measured PM2.5 were collected each minute and processed to identify activities established through worker interviews, observation, and existing literature. Descriptive statistics were generated for activity types, frequencies, and associated PM2.5 exposures. A kappa statistic measured agreement between self-reported and image-based time-activity data. Results Based on image-based time-activity patterns, workers primarily dismantled, sorted/loaded, burned, and transported e-waste materials for metal recovery with high variability in activity duration. Image-based and self-reported time-activity data had poor agreement (kappa = 0.17). Most measured exposures (90%) exceeded the World Health Organization (WHO) 24-h ambient PM2.5 target of 25 µg m−3. The average on-site PM2.5 was 81 µg m−3 (SD: 94). PM2.5 levels were highest during burning, sorting/loading and dismantling (203, 89, 83 µg m−3, respectively). PM2.5 exposure during long periods of non-work-related activities also exceeded the WHO standard in 88% of measured data. Conclusions In complex, informal work environments, wearable cameras can improve occupational exposure assessments and, in conjunction with monitoring equipment, identify activities associated with high exposures to workplace hazards by providing high-resolution time-activity data.


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