ScholarGate
Asisten

Bandingkan metode

Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.

Pemrograman Dinamis Deterministik×Pemrograman Integer Campuran×
BidangSimulasiSimulasi
KeluargaProcess / pipelineProcess / pipeline
Tahun asal19571958–1960
PencetusRichard E. BellmanRalph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
TipeExact sequential optimization algorithmMathematical optimization
Sumber perintisBellman, R. E. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
AliasDDP, Deterministic DP, Classical Dynamic Programming, Bellman Dynamic ProgrammingMIP, Mixed-Integer Linear Programming, MILP, Integer Programming
Terkait66
RingkasanDeterministic Dynamic Programming (DDP) is a mathematical optimization technique that decomposes a multi-stage decision problem into a sequence of simpler subproblems, solving them exactly when all system parameters — transition functions, costs, and rewards — are known with certainty. It guarantees a globally optimal policy via Bellman's principle of optimality.Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally.
ScholarGateSet data
  1. v1
  2. 2 Sumber
  3. PUBLISHED
  1. v1
  2. 2 Sumber
  3. PUBLISHED

Ke halaman pencarian Download slides

ScholarGateBandingkan metode: Deterministic Dynamic Programming · Mixed-Integer Programming. Diakses 2026-06-15 dari https://scholargate.app/id/compare