https://journals.tultech.eu/index.php/eil/issue/feedEnvironmental Industry Letters2026-08-30T20:56:56+02:00Associate Editor: Mr. Amir Hedayatiamir.hedayati@tultech.euOpen Journal Systems<p style="font-weight: 400;"><strong>Environmental Industry Letters (EIL)</strong> is an innovative open-access journal that sits at the intersection of environmental science and industrial development. Our mission is to offer a unique perspective on the mutually beneficial relationship between industries and the environment and, hence, promote practical and sustainable approaches to their coexistence. EIL maintains a global perspective, addressing the diverse needs and challenges faced by industries across developed and developing nations. The journal distinguishes itself by introducing fresh viewpoints into the scientific discourse, enriching our understanding of the intricate connection between industry and the environment.</p> <p style="font-weight: 400;"> </p>https://journals.tultech.eu/index.php/eil/article/view/680A Genetic-Algorithm-Based Multi-Objective Decision Support System for Cascade Reservoir Water Resource Management2026-08-30T20:56:56+02:00Aleksander VokhmintcevAbotalebmostafa@bk.ruTatyana VokhmintcevaAbotalebmostafa@bk.ruNatalia D. IvanovaAbotalebmostafa@bk.ruHussein AlkattanAbotalebmostafa@bk.ruMostafa AbotalebAbotalebmostafa@bk.ruMustafa AdelAbotalebmostafa@bk.ruIoannis AdamopoulosAbotalebmostafa@bk.ru<p>Growing volumes of publicly available hydrological, meteorological, and reservoir-operation data create new opportunities for evidence-based water resource management. However, converting these data into actionable operating strategies requires optimisation frameworks capable of addressing the non-linear, non-convex, and conflicting objectives of reservoir operation. This study develops and evaluates a genetic-algorithm-based decision support system (DSS) for a three-reservoir cascade serving irrigation, municipal water supply, hydropower generation, and flood control. A real-coded, constraint-handling Non-dominated Sorting Genetic Algorithm II (NSGA-II) is implemented in Python and coupled with a mass-balance simulation model driven by a ten-year stochastically generated monthly inflow record calibrated to a monsoon-influenced river basin. The optimisation considers a 45-parameter cyclic hedging-rule policy, producing implementable and repeatable management strategies rather than fixed release schedules. Eight independent runs (150 individuals, 300 generations) produce a consolidated Pareto front of 417 non-dominated policies balancing water-supply deficit, hydropower generation, and flood-control performance. A TOPSIS-based compromise-selection layer converts these solutions into operating recommendations under three managerial preference profiles. Compared with a conventional Standard Operating Policy baseline, the balanced GA-optimised policy increases hydropower generation from 6,453.9 to 6,564.2 GWh over ten years and reduces the flood-control exceedance index by 90%, while the water-supply-priority policy increases generation to 6,789.3 GWh. The results demonstrate a deliberate reliability-resilience-vulnerability trade-off, with GA policies rationing water more frequently but moderately, thereby redistributing risk across the cascade. Sensitivity and convergence analyses further confirm the robustness of the optimisation framework.</p>2026-03-16T00:00:00+01:00Copyright (c) 2026 Authors