Robustness in optimal design of Eco-Industrial Parks under the lens of two-stage stochastic optimization

Eco‑industrial parks (EIPs) let nearby companies share materials and waste, which cuts both environmental impact and operating costs. In real life, daily operations vary a lot, creating uncertainty that can break fixed exchange networks. This study tackles how to design water exchange networks (WENs) that stay reliable despite that uncertainty. We use a two‑stage stochastic optimization model that represents daily variations with probability functions, allowing us to assess and improve network robustness. The mathematical problem is solved with the Sample Average Approximation (SAA) method, which handles the scenario‑based uncertainty that comes from many firms interacting. By putting probabilistic robustness directly into the objective, the approach balances cost efficiency with operational

reliability. Simulations based on standard benchmark data from the literature show that the stochastic programming model works well. The framework gives clear guidance for building flexible industrial‑symbiosis networks that keep water‑sharing efficient even when supply and demand are volatile.

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