Food Delivary Mathematical Optimization.pptx

Food Delivery Optimization
With mathematical
Optimization techniques

1. Food Delivery Optimization With mathematical Optimization techniques

2. Optimizing food delivery involves Minimizing costs Maximizing efficiency Ensure timely delivery. Convex and non-convex optimization techniques can be applied to Route Optimization Schedule Optimization Resource allocation Optimization

3. Theory Optimization techniques

4. Optimization algorithms aim to find the best solution according to a given criterion, often under a set of constraints. Objective Function: The function that needs to be optimized (minimized or maximized). Denoted as f(x) When you optimize the area of a rectangle , Objective Function would be a X B Minimize or Maximize f(x) = a X b M athematical optimization

5. Decision variable is a variable that represents a decision to be made in an optimization problem. These variables are the unknowns that the optimization algorithm seeks to determine in order to optimize the objective function subject to given constraints. Constraints: Constraints are also expressed in terms of decision variables. They define the feasible region within which the decision variables can lie Gradient The gradient is a vector of partial derivatives of a function with respect to its variables. In the context of optimization, the gradient of the objective function indicates the direction and rate of the fastest increase of the function.

6. The approach to optimization 1) Define the objective function f(x) that needs to be minimized or maximized. This function represents the goal of the optimization. 2) Identify the decision variables x=(x1,x2,…, xn ) that can be controlled or adjusted to optimize the objective function. 3) Determine the constraints gi (x)≤0 hj (x)=0 that the decision variables must satisfy. These constraints define the feasible region. 4) Selection of Optimization Method Based on the problem characteristics, select an appropriate optimization method. Optimization methods can be broadly categorized into: Convex and Non Convex Optimization

7. Convex and Non Convex Optimization If a cost function has only one Minimum point It is a Convex function and if the cost function has More than one minimum points that is a Non- convex Function

8. Food Delivery Route Optimization

9. Decision variable Constraints

10. Objective Function Formulate the Optimization Problem

11. Add Constraints This can be solved using linear programming or other optimization techniques