Сравнение методов

	Robust Tabu Search	Робастный имитированный отжиг
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1989 (TS); robust variant ~2000s	1983 (SA); robust variant emerged 1990s–2000s
Автор метода	Glover, F. (Tabu Search); robustness extensions by various authors	Kirkpatrick, Gelatt & Vecchi (SA basis); robust formulation developed across the operations research community
Тип	Metaheuristic with robustness mechanism	Metaheuristic with robustness evaluation
Основополагающий источник	Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206.	Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
Другие названия	RTS, Robust TS, Uncertainty-aware Tabu Search, Tabu Search under Uncertainty	RSA, Robust SA, Uncertainty-robust simulated annealing, Worst-case simulated annealing
Связанные	6	5
Сводка	Robust Tabu Search (RTS) extends the classical Tabu Search metaheuristic by evaluating candidate solutions not only on their nominal objective value but also on their performance under uncertainty. Instead of seeking the best solution for a single scenario, RTS seeks solutions that perform well across a range of scenarios or realizations, trading peak optimality for reliability.	Robust Simulated Annealing (RSA) adapts the classical simulated annealing metaheuristic to seek solutions that perform well not just under nominal conditions but across the full range of uncertain or adversarial parameter values. By embedding a robustness evaluation — worst-case, expected-case, or regret-based — into the SA acceptance step, RSA trades some nominal optimality for resilience, making it valuable when problem parameters are imprecisely known or subject to environmental variation.