My matric no is 302170 from Bachelor of Operations Management

I’m from Pulau Pinang BukitMertajam, a place full with warm heart people and delicious food😋

My inasis is TNB ⚡️

Lastly my phone number is 01126822929📱

*if got any problem we can discuss under comment

*if got any problem we can discuss under comment

*if got any problem we can discuss under comment

*internal DUE 9 NOV 2024

Because I got class after this🥲I’m afraid I cannot…

If can just comment here 🫶🏻🥲

• The best solution often arrived at the extreme point of the feasible region polygonal boundary or the vertices.

• To solve the maximization problem, we need to slide the objective function line in the direction that hikes the value of an objective to the last point of the feasible area

• To solve the minimization problem we need to slide it in the direction that lowers down the value of an objective to the last point of the feasible region

• In a maximization problem,the objective function line is shifted outwards to the extreme of the feasible region

• In a minimization problem, the objective function line is shifted towards the most minimal value of the feasible region

-It helps the identification of the feasible area and optimal solution points, providing insight into how constraints interact with the objective function.

-It is useful for introducing LP principles and developing intuition on feasible regions, optimal solutions, and trade-offs.

· Constraint: Convert each inequality constraint into an equation by setting it equal. Plot this line, and then shade the area that satisfies the inequality.

· Objective Function: Plot the objective function as a line by assigning it an arbitrary value. Adjust its position parallel to itself to maximize or minimize the function while staying within the feasible region.

• The feasible area is the graph region that satisfies all constraints at the same time. It is usually a polygon created by the intersection of constraint lines. Any point inside or on the border of this region is a feasible solution.

• When the objective function's slope is the same as a constraint's slope, the objective function line is parallel to that constraint. This situation often results in multiple optimal solutions along a line segment if that constraint forms part of the boundary of the feasible region.

• Dimension Limitation: Only works with two decision variables (and sometimes three with complex 3D methods), which is impractical for most real-world LP problems with more variables.

• Accuracy and Precision: Visual methods may lack precision compared to algebraic or software-based solutions.

• Complexity in Constraints: When there are many constraints, interpreting the feasible region can become visually complex.

later we can create a video from this

Hope you guys got notice that Dr. already extend the due date until after mid semester break..

Since we finish our work already so i will upload our video here..

Thanks for your contribution..

Really appreciate it and hope we can work together afterwards..