How to Create a JSON File for Optimizing Logistics with Consistent Pickups and Variable Deliveries to send a OptimizeTourRequest in google cloud?

Question

I'm working on optimizing logistics operations for shipments where products are picked up from a single factory but delivered to different locations. I need to create a JSON file that specifies these shipments with consistent pickup locations and variable delivery locations, along with flexible time windows. Each shipment should include details such as weight demands, pickup and delivery durations, and geographical coordinates. Additionally, I want to outline vehicle specifications including load limits, start and end locations, and cost per traveled hour for efficient logistics management.

I've looked into Google CFR's transportation model JSON file, which describes a similar scenario but doesn't precisely match my requirements. How can I structure my JSON file to achieve this optimization goal effectively? Any insights or examples would be greatly appreciated. text

I attempted to maintain uniform pickup locations across all shipments, setting a general time window for all deliveries, such as requiring all deliveries to be completed within a n-hour (n=any number) timeframe. However, I encountered difficulties with the concept of a global time window, which I'm struggling to grasp. Given that I haven't defined specific time windows for each delivery, I recognize that this criterion needs adjustment as well.

Answer 1

To solve this problem, you can use the Vehicle Routing Problem (VRP) algorithm, which is a combinatorial optimization problem aiming to find the best routes for a fleet of vehicles to deliver to a given set of customers.

1. Calculate Demand and Capacity in CFT: Convert order carton load to CFT for each location.
2. Formulate as a VRP: Formulate the problem as a VRP where each location is a customer, and the vehicles have limited capacity. Implement the objective function to minimize total travel distance.
3. Select Appropriate Algorithm:Choose an appropriate algorithm to solve the VRP. Common choices include metaheuristic algorithms like Genetic Algorithms (GA), Ant Colony Optimization (ACO), or exact algorithms like Branch and Bound.
4. Route Optimization: Use the selected algorithm to optimize routes. This involves assigning locations to vehicles while considering capacity constraints and minimizing total distance traveled.
5. Determine Vehicle Sizes: Based on the optimized routes, determine the number of required vehicles for each size (23ft, 16ft, 12ft).
6. Ensure Capacity Utilization: Adjust routes to ensure maximum utilization of each vehicle's capacity. This may involve redistributing some orders among vehicles to avoid underutilization or overloading.
7. Implement and Validate: Implement the solution and validate it by simulating deliveries or using real-world data if available. Verify that the total distance traveled is minimized, and the capacity constraints are satisfied.