operations-research-guide

npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill operations-research-guide --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# Operations Research Guide A skill for applying operations research (OR) methods to business, logistics, and resource allocation problems. Covers linear programming, integer programming, scheduling, network optimization, simulation, and decision analysis using Python optimization libraries. ## Linear Programming ### Problem Formulation and Solving ```python from scipy.optimize import linprog import numpy as np def solve_production_planning(): """ Example: A factory produces two products (A and B). Product A: profit $40, uses 2h labor + 1kg material Product B: profit $30, uses 1h labor + 2kg material Constraints: 100h labor available, 80kg material available Maximize total profit. """ # linprog minimizes, so negate for maximization c = [-40, -30] # objective coefficients (negated) # Inequality constraints: A_ub @ x <= b_ub A_ub = [ [2, 1], # labor constraint [1, 2], # material constraint ] b_ub = [100, 80] # Non-negativity bounds bounds = [(0, None), (0, None)] result = linprog(c, A_ub=A_ub, b_ub=b_ub, bounds=bounds, method="highs") return { "product_A": result.x[0], "product_B": result.x[1], "max_profit": -result.fun, "status": "optimal" if result.success else "infeasible", } ```