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Fuzzy Multi-Objective Optimization of Resource Allocation in the Agricultural Metaverse
Abstract
This chapter presents a fuzzy multi‐objective optimization framework for resource allocation in an XR‐enabled agricultural metaverse. It introduces fuzzy‐set modeling of yield, cost, and sustainability metrics, along with system constraints, via membership functions and α‐cut arithmetic. Two solution strategies—fuzzy weighted aggregation and fuzzy goal programming are extended into an evolutionary NSGA-II algorithm incorporating fuzzy dominance and crowding measures. A numerical vertical‐farm case study illustrates the methodology, producing a robust fuzzy Pareto front and revealing trade‐offs among productivity, expense, and environmental impact. Sensitivity analyses on weight selection, α‐cut resolution, and scenario sampling validate solution stability. Implementation guidelines for real‐time XR integration are discussed. The chapter concludes with insights on algorithmic performance, limitations, and future directions toward dynamic, interactive, and scalable fuzzy MOO in metaverse agriculture.
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