Learn entire Operation Research with Practical Examples

Explore assignment problems and solve them with the Hungarian method, including row and column reductions, balancing a square matrix, and finding minimum-cost, optimal job assignments.

Learn the assignment problem using the Hungarian method, balancing unbalanced matrices with dummy rows or columns, and converting maximize to minimize through row and column reduction and optimality tests.

Explore assignment problems in operation research, including unbalanced and prohibited assignments, using the Hungarian method to convert maximization to minimization, with infinity handling and practical examples.

Explore operation research with practical examples through live and recorded sessions, covering linear programming, transportation problems, game theory, CPM, network diagrams, and queueing theory.

Learn to model transportation problems in operation research by using cost matrices, balancing supply and demand, and applying northwest corner, least cost, and Vogel approximation methods.

Learn to solve unbalanced transportation problems using northwest corner, least cost, and Vogel approximation method, balancing demand and supply with dummy rows or columns to minimize cost.

Learn to solve transportation problems with basic feasible solutions using northwest corner, least-cost, and Vogel approximation method, including balancing, dummy rows or columns, and profit optimization.

Explore solving transportation problems using northwest corner, least cost, and Modi methods to obtain initial feasible solutions, check feasibility, and minimize total transportation cost.

Learn linear programming with a graphical method to maximize or minimize an objective function, under constraints and non-negativity, identifying the feasible region in the first quadrant for x and y.

Learn to solve linear programming problems graphically, identify the feasible region in the first quadrant, find corner points via simultaneous equations, and maximize or minimize the objective function.

Learn to set up linear programming problems, maximize or minimize the objective under non-negativity and constraints, graph the feasible region in the first quadrant, and solve for corner points.

Learn to model a linear programming problem with constraints and non-negativity, graph the feasible region, identify corner points, and determine the minimum or maximum of the objective function.

Explore the graphical method for linear programming, determine feasible regions from multiple constraints, locate corner points at line intersections, and identify the minimum of the objective function.

Learn to formulate linear programming problems, identify the feasible region in the positive first quadrant, determine corner points, and maximize the objective function using practical examples.

Formulate linear programming problems by defining variables, an objective function, and constraints with non-negativity and signs such as less than or equal to or greater than or equal to.

Learn to formulate linear programming problems for two plants Solan and Mohan Nagar with multiple drinks, applying demand, capacity, and non-negativity constraints to minimize cost or maximize revenue.

Learn to formulate and solve two-variable linear programming problems using graphing, with objectives like minimizing costs or maximizing profits under constraints such as capacity, demand, and non-negativity.

Learn how to use the simplex method to solve linear programming problems, set up objective functions and constraints with slack variables, and perform tableau iterations to reach the optimal solution.

Apply the simplex method to maximize z = 12 x1 + 16 x2 under non-negativity and linear constraints. Follow iterative tableaux and basic variable updates to reach the optimal solution.

Learn to make informed decisions using decision theory under risk and uncertainty, applying EMV and criteria such as maximin, minimax, Laplace, Hurwicz, and regret to payoff matrices and perfect information.

Explore decision theory under risk and uncertainty by comparing EMV, Laplace, Hurwicz alpha, and minimax regret to select optimal strategies in business and agriculture scenarios.

Explore game theory fundamentals, including pure and mixed games, two-player matrices, and strategy concepts; learn saddle point, minimax, row minima, column maxima, and basic mixed strategies with probabilities.

Explore game theory fundamentals, including the principle of dominance, saddle points, and pure versus mixed strategies, and learn to solve two by two games using row and column analysis.

Explore Monte Carlo simulation to forecast daily bakery demand from past data, using random numbers and probability tables to estimate future outcomes and averages.

Explore sequencing and assignment problems using the Hungarian method on two machines, A and B, to minimize total elapsed time and idle time while identifying the optimal job order.

Learn sequencing of multiple jobs on three machines using left-to-right and right-to-left rules to minimize total elapsed time and idle time, with multiple optimum solutions and Hungarian method concepts.

Explore sequencing with multiple machines, using left-to-right and right-to-left methods to calculate in-out timing, idle times, and total elapsed time, including matrix sequencing and virtual machines.

Learn to use Markov chain simulation with a brand-switching matrix to forecast future market shares, compute state probabilities after k periods, and analyze steady-state equilibrium.