A multi-objective model for solar industry closed-loop supply chain by using particle swarm optimization
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Solar energy industry is an exceptional industry which desperately relies on government support and subsidy. The demand is decreasing since the government support reduction, moreover, the dramatically increase China solar manufacturers have great impact on solar product price in recent years. Because the insufficient supply of silicon materials carries the issue of solar cell recycle, the solar manufacturer must design a sustainable closed-loop supply chain (CLSC) to recycle and reuse the retired solar cells to achieve 3E (Effective, Efficient, Environmental3E) objectives. This paper studies an integrated CLSC network design problem with sustainable concerns in the solar energy industry. We are interested in the logistics flows, capacity expansion and technology investments of existing and potential facilities in the multi-stage CLSC. Therefore, a deterministic multi-objective mixed integer programming model capturing the tradeoffs between the total cost and the carbon dioxide (CO2) emission is developed to tackle the multi-stage CLSC design problem from both economic and environmental perspectives. Due to the multi-objective nature and computational complexity, a multi-objective particle swarm optimization (MOPSO) with novel flow assignment algorithms is designed to search non-dominated /Pareto CLSC design solutions. Finally, a case study of crystalline solar energy industry is illustrated to verify the proposed multi-objective CLSC design model and demonstrate the efficiency of the developed MOPSO algorithm in terms of computational time and solution quality.
Paper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San AntonioIncludes bibliographical references