Reverse logistics network design for waste solar photovoltaic panels: a case study of New South Wales councils in Australia

Md. Tasbirul Islam, Mohammad Sohrab Hasan Nizami, Sajjad Mahmoudi, Nazmul Huda*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Waste solar photovoltaic (PV) panels are considered as one of the fastest-growing future waste streams under the category of large electronic waste (e-waste). The lifespan of solar panels varies from 20 to 30 years, and an appropriate reverse logistics network design is essential to manage the waste stream efficiently once their lifetime expires. Mixed-integer programming-based RL model is proposed in this paper for New South Wales, Australia that minimizes the overall cost by identifying optimal locations and sizing of the collection points while determining optimal capacities for recycling facilities. Using the historical data (2001–2017) on the installed capacity of solar panels in the state, the potential waste generation (at council-level) is estimated and optimized solutions are proposed for the year 2047. The results of the study show that the highest waste solar PV will be generated at Murrumbidgee, Berrigan, Balranald, and Bogan councils. Out of 129 councils in the state, the model identifies 78 optimized-locations of the collection points that would be required in the councils. In the councils of Newcastle, Narrandera and Wagga Wagga, three major recycling facilities would need to be established. This is the first systematic attempt in designing an optimized RL network in Australia focusing on waste solar PV. Policy-makers will find this research highly valuable in decision-making on local recycling infrastructure development.

Original languageEnglish
Pages (from-to)386-395
Number of pages10
JournalWaste Management and Research
Issue number2
Early online date6 Oct 2020
Publication statusPublished - Feb 2021


  • Waste solar photovoltaic panels
  • e-waste
  • reverse logistics
  • optimization
  • mixed-integer programming
  • Australia


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