مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| برنامهریزی عدد صحیح (IP) و برنامهریزی عدد صحیح مختلط (MIP)× | شبه بهینهسازی: ادغام شبیهسازی با فرا بهینهسازی برای بهینهسازی تصادفی× | |
|---|---|---|
| حوزه | بهینهسازی | بهینهسازی |
| خانواده | Process / pipeline | Process / pipeline |
| سال پیدایش≠ | 1958 | 2015 |
| پدیدآور≠ | Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960) | Juan et al. |
| نوع≠ | Mathematical optimisation — exact combinatorial method | Hybrid simulation-optimization framework |
| منبع بنیادین≠ | Wolsey, L.A. (1998). Integer Programming. Wiley. ISBN: 9780471283669 | Juan, A. A., et al. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62–72. DOI ↗ |
| نامهای دیگر≠ | IP, MIP, mixed-integer programming, mixed-integer linear programming | Simulation-based Metaheuristics, Stochastic Metaheuristics with Simulation, Hybrid Simulation-Optimization, Simülistik Sezgiseller |
| مرتبط≠ | 4 | 3 |
| خلاصه≠ | Integer programming (IP), also called mixed-integer programming (MIP) when only some variables are restricted to whole numbers, is a branch of mathematical optimisation in which some or all decision variables must take integer or binary values. Building on linear programming, it was formalised through Ralph Gomory's cutting-plane method (1958) and the Land-and-Doig branch-and-bound algorithm (1960), and it has since become the standard exact framework for scheduling, assignment, routing, and resource-allocation problems. | Simheuristics is a hybrid algorithmic framework that integrates Monte Carlo or discrete-event simulation into metaheuristic search procedures to solve stochastic combinatorial optimization problems. Introduced by Juan et al. in 2015, it addresses settings where objective function evaluations involve random variables, providing near-optimal solutions with probabilistic quality guarantees. The approach is especially suited for real-world logistics, transportation, and scheduling problems where uncertainty is inherent and classical deterministic solvers fail to capture variability. |
| ScholarGateمجموعهداده ↗ |
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