Operations Research

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12 domain-specific abstractions whose origin domain is Operations Research.

• Algebraic modeling language — A high-level language for expressing optimization models in index-based algebraic notation while separating mathematical structure from data and solver implementation.
• Gittins index — Assign each state of an eligible discounted Markov reward arm the greatest reward rate attainable before an adapted stopping time, enabling an optimal classical bandit policy by selecting the arm with largest current index.
• Goal programming — A multiobjective optimization method that assigns target levels to several objectives and minimizes weighted or prioritized deviations from those targets.
• Liner shipping network design and scheduling problem — A maritime-operations optimization problem jointly choosing liner services, schedules, vessel deployment, and container flows.
• Minimum-cost flow problem — The optimization problem of routing a required amount of flow through a capacitated network while satisfying conservation and minimizing total edge cost.
• Multi-commodity flow problem — Route multiple source–sink commodities through one capacitated network while each commodity obeys flow conservation and all commodities jointly share edge capacities, optimizing congestion, cost, or satisfied demand.
• Network Simplex Algorithm — Solve a minimum-cost flow by maintaining a spanning-tree basis, pricing bound-fixed arcs with node-potential reduced costs, augmenting around an entering arc's fundamental cycle, and exchanging the limiting arc until dual signs certify optimality.
• Nonlinear programming — Optimization of an objective subject to constraints when the objective or at least one constraint is nonlinear in the decision variables.
• Nurse scheduling problem — The constrained optimization problem of assigning qualified staff to shifts while satisfying coverage rules and balancing preferences, fairness and cost.
• Simulation-based optimization — Optimization in which candidate decisions are evaluated by a computational simulation—often noisy, expensive and derivative-free—rather than a closed-form objective or constraint model.
• Single-machine scheduling — The optimization of job order and timing on one capacity-one resource under declared release, precedence and objective rules.
• Stochastic programming — An optimization framework in which uncertain parameters are represented by probability distributions or scenarios and decisions are chosen across one or more information stages.