| Title | Waste material recycling and exchanging decisions for industrial symbiosis network optimization |
|---|---|
| ID_Doc | 24172 |
| Authors | Huang, LF; Zhen, L; Yin, LS |
| Title | Waste material recycling and exchanging decisions for industrial symbiosis network optimization |
| Year | 2020 |
| Published | |
| Abstract | The industrial symbiosis initiative, which aims to reuse waste resources, is considered as a potential solution for sustainable manufacturing. Compared with much literature for the industrial symbiosis optimization about waste energy and wastewater, this paper proposes an industrial symbiosis optimization model based on the waste material recycling and exchanging network. By-products from the production process are considered the main components of waste material flows. A Mixed-integer programming method is employed to formulate the optimization model. In response to the three pillars of sustainability, three sub-objectives, namely the economy, environment, and society, are built for the proposed optimization model. The Epsilon-constraint technology is established to tackle the multiobjective model efficiently. Decisions on waste material recycling and exchanging flow are formulated in the proposed model, as well as options on reprocessing sites and reprocessing technology levels. The results show that the proposed Mixed-Inter Programming optimization method and Epsilon-constraint technology are sufficient to obtain the solutions of industrial symbiosis optimization. Technology level decisions integrated into the optimization model can balance the objectives of economy, environment, and society and provide a more extensive decision option space for managers. Raw material price and by-product recycling cost are two main elements of the waste material exchanging of industrial symbiosis optimization based on the parameter robustness analysis. Multi-objective optimization of waste material reprocessing and exchange network allows managers to make decisions based on different objective priorities. (C) 2020 Elsevier Ltd. All rights reserved. |
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