Long-term planning for nuclear energy systems has been an area of interest for policy planners and systems designers to assess and manage the complexity of the system and the long-term, wide-ranging societal impacts of decisions. However, traditional planning tools are often poorly equipped to cope with the deep parametric, structural, and value uncertainties in long-term planning. A more robust, multiobjective decision-making method is applied to a model of the nuclear fuel cycle to address the many sources of complexity, uncertainty, and ambiguity inherent to long-term planning. Unlike prior studies that rely on assessing the outcomes of a limited set of deployment strategies, solutions in this study arise from optimizing behavior against multiple incommensurable objectives, utilizing goal-seeking multiobjective evolutionary algorithms to identify minimax regret solutions across various demand scenarios. By excluding inferior and infeasible solutions, the choice between the Pareto optimal solutions depends on a decision-maker's preferences for the defined outcomes—limiting analyst bias and increasing transparency.

Though simplified by the necessity of reducing computational burdens, the nuclear fuel cycle model captures important phenomena governing the behavior of the nuclear energy system relevant to the decision to close the fuel cycle—incorporating reactor population dynamics, material stocks and flows, constraints on material flows, and outcomes of interest to decision-makers. Technology neutral performance criteria are defined consistent with the Generation IV International Forum goals of improved security and proliferation resistance based on structural features of the nuclear fuel cycle, natural resource sustainability, and waste production. A review of safety risks and the economic history of the development of nuclear technology suggests that safety and economic criteria may not be decisive criteria as the safety risks posed by alternative fuel cycles may be comparable in aggregate and economic performance is uncertain and path dependent.

Technology strategies impacting reactor lifetimes and advanced reactor introduction dates are evaluated against a high, medium, and phaseout scenarios of nuclear energy demand. Non-dominated, minimax regret solutions are found with the NSGA-II multiobjective evolutionary algorithm. Results suggest that more aggressive technology strategies featuring the early introduction of breeder and burner reactors, possibly combined with lifetime extension of once-through systems, tend to dominate less aggressive strategies under more demanding growth scenarios over the next century. Less aggressive technology strategies that delay burning and breeding tend to be clustered in the minimax regret space, suggesting greater sensitivity to shifts in preferences. Lifetime extension strategies can unexpectedly result in fewer deployments of once-through systems, permitting the growth of advanced systems to meet demand. Both breeders and burners are important for controlling plutonium inventories with breeders achieving lower inventories in storage by locking material in reactor cores while burners can reduce the total inventory in the system. Other observations include the indirect impacts of some performance measures, the relatively small impact of technology strategies on the waste properties of all material in the system, and the difficulty of phasing out nuclear energy while meeting all objectives with the specified technology options.