Mathematical Methods for Operations Research Problems

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integer programming; digital geometry; non-traditional grids; shortest chamfer paths; 4D grid; linear programming; optimization; digital distances; chamfer distances; weighted distances; ant colony optimization; mathematical programming; negative learning; minimum dominating set; multi-dimensional knapsack problem; discrete optimization; operational research; computational geometry; complexity; algorithms; dynamic programming; clustering; k-center; p-center; sum-radii clustering; sum-diameter clustering; bi-objective optimization; Pareto Front; parallel programming; global total domination; total k-domination number; Best–Worst Method; Eigenvalue Method; Geometric Mean Method; Monte Carlo simulations; pairwise comparisons; sensitivity; scheduling; uniform machines; release time; delivery time; time complexity; algorithm; pressing process; printed circuit board; scheduling; mixed-integer linear programming; heuristic; swarm intelligence method; parameter control; adaptive technique; hidden Markov model; (s, Q)-policy; Markovian Arrival Process; N-policy; impatient customers; cryptocurrency; portfolio selection; return and risk measures; market capitalization; volume; attractiveness; PROMETHEE II; multicriteria model; carbon emission; carbon trading; e-commerce supply chain; sustainable development; clustering techniques; metaheuristics; machine learning; self-adaptive; parameter setting; exploration; exploitation; metaheuristics; machine learning; hybrid approach; optimisation; multiphase systems; heavy traffic; Little’s formula; timetabling problem; course university timetabling problem; AACSB standards; integer linear programming; n/a