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Motivated by mounting pressures to achieve environmental sustainability, and the emergence of online waste exchange platforms, in this work we propose an optimization‐based framework for studying waste‐to‐resource formation in the industry. We first develop a linear programming market clearing model comprising self‐interested agents participating in waste‐to‐resource trading. We then embed this in a bi‐level capacity optimization problem under uncertainties in the agents' reserve prices, the goal of which is to maximize the economic savings achieved by waste‐to‐resource. We show that the resulting robust optimization models admit linear mixed integer programming formulations that can be readily computed using available solvers. Furthermore, we provide the formulas for applying an equitable levy and incentive scheme, to enable capacity cost‐sharing among the agents. Numerical studies based on a case of organic waste streams demonstrate the usefulness of the proposed models in generating valuable insights for decision support in the design of waste‐to‐resource markets. [ABSTRACT FROM AUTHOR]

In a linear economy, consumers typically dispose end-of-life products which are eventually incinerated or landfilled. Digital platforms could unlock rich waste reutilization opportunities and economic benefits by connecting the waste producers with potential waste buyers in online marketplaces. While previous studies mostly focus on input-output matching, this study proposes a decision support system that can be incorporated to an online marketplace to optimize the waste trading based on economic considerations. Moreover, the decision support system could facilitate higher level decisions such as capacity planning and incentive design. Based on a case study on Singapore's organic waste streams, the waste trading platform presents economic benefits for agents that sell their waste. Additionally, enforcing waste reutilization targets would require relevant policy support in terms of incentives or levies, where the levies can be implemented as co-payment rate for the agents. For instance, results showed that when the food waste reutilization target is set as 6 t/d, increasing the co-payment rate from 0 to 20% allows the waste trading network to be self-sustaining without external funding. Finally, sensitivity analyses show that collecting more waste records in the database helps in optimizing the investment decisions, preferably when the number of records exceeds 64. [ABSTRACT FROM AUTHOR]