Extended Abstract

Introduction and Purpose :Perishable horticultural value chains are characterized by product heterogeneity, quality deterioration, uncertain supply and demand, and competing economic and environmental objectives. In pomegranate systems, cracking, sunburn, mechanic al damage, and food-safety failures can reduce fresh-market eligibility and redirect fruit toward processing or non-food uses. Although previous studies have addressed perishability, uncertainty, sustainability, and closed-loop recovery, these elements are often modeled separately. This study develops an integrated simulation–optimization decision support framework that transforms fruit-level quality variability into operational quality grades and links those grades to multi-period allocation, inventory, processing, and resource-valorization decisions under alternative scenarios.

Methodology: Monte Carlo simulation is used as a preprocessing stage to generate scenario-specific initial quality shares from fruit-level physical attributes, food-safety status, and visible defects. Five scenarios represent severe drought, mild drought, normal conditions, adequate and stable water availability, and constrained supply with high demand. Weight, diameter, total soluble solids, and titratable acidity are simulated, and fruit are classified into three operational grades: fresh-market eligible, food-safe processing grade, and non-food grade. The resulting mass-based quality shares are transferred to a multi-period optimization model incorporating fresh-market priority, inventory deterioration, cascading processing, composting, profit, water footprint, and carbon emissions. The multi-objective model is solved using AUGMECON2.

Findings: The optimization procedure generated a diverse Pareto set of efficient economic–environmental operating plans. The selected compromise solution reduced profit by only 1.486% relative to the profit-maximizing solution, while decreasing carbon emissions by 8.557% and the water footprint by 0.462%, indicating that the main environmental improvement was achieved through carbon reduction. Fresh-market performance varied considerably across scenarios. The expected service level was 82.49%, reaching 100% under favorable conditions and declining to 25.36% under constrained supply and high demand, while the normal scenario achieved 98.86%. These results demonstrate the strong influence of supply, demand, and quality conditions on fresh-market performance. Monte Carlo simulation also revealed substantial differences in initial quality composition. The fresh-market grade accounted for 44.414%–80.280% of total fruit mass across scenarios, while the processing-grade share ranged from 17.724% to 49.120% and the non-food grade from 1.996% to 6.466%. Thus, even under unfavorable conditions, a considerable proportion of fruit remained suitable for processing rather than being lost from the value chain. Within the cascading processing structure, approximately 40% of pomegranate input was converted into juice and about 9% was recovered as seeds, of which approximately 14% was converted into seed oil. Compost output represented about 40% of organic waste input, and approximately 93.45% of the produced compost was returned to orchards, supporting circular resource use within the value chain. These percentages are derived from the reported processing flows Sensitivity analysis confirmed that the fresh-market weight threshold had the strongest influence on quality composition. Increasing the threshold reduced the fresh-market grade share by 4.2701 percentage points, decreased profit by 1.772%, and increased carbon emissions by 2.486%. Changes in the scenario-specific weight adjustment and cracking probability also produced meaningful effects, whereas changes in the water footprint remained below 0.07%, indicating relatively low sensitivity of water use to quality variations.

Conclusions :The proposed framework provides a proactive decision-support approach for quality-driven pomegranate value-chain planning under uncertainty. By linking Monte Carlo quality simulation with multi-objective optimization, it enables decision-makers to compare operating alternatives before implementation, preserve fresh-market priority, coordinate cascading processing and resource valorization, and evaluate economic–environmental trade-offs across scenarios. Explicitly modeling quality heterogeneity therefore strengthens allocation, recovery, and sustainability decisions in perishable horticultural value chains.