An Optimization Strategy for Amazon’s Uber-like Delivery Service

Arsalan Qadri, Hanzhuo (Andrew) Li, Tao FengInstructor: Dr. Ted Stohr

Introduction Methods and Algorithms1.Use Logistic Regression to select a set of suitable delivery agents for each shipping task.

Predictive Variable: P - The probability successfully completing the delivery task.

Independent variables:X1 – Distance from agent’s current location to seller site.X2 – Agent’s reliability.X3 – Agents punctuality.X4 – Agents customer rating.

2. Calculate the distance between available delivery agents selected from above and the corresponding sellers using GPS coordinates. Use Assignment Model to assign one or more (closest) agents to each seller’s shipment task.

Assignment Model for minimizing the total distance from sellers and agents.

3. Find the shortest routes for the shipments from a seller to one or more buyers, using Genetic Algorithm.

This model is aimed at minimizing the total distance (Z) travelled by one or more agents involved in a single shipment task. The constraints for each agent in this model includes capacity (Q) and the number of shipping sites (L).

(This model is adapted from: Study of the optimizing of physical distribution routing problem based on genetic algorithm by LANG Mao-Xiang. We adapted this model by changing vehicle capacity, route description and other variable meaning of prior model)

4. Use the weights from AHP method to calculate the rating of each vehicles for their compensation ratio

The compensation for the drivers is calculate using the agent rating from the AHP model. This is done on the basis of customer ratings and efficiency factors. e and customer ratings on factors such as reliability, punctuality and courtesy, the compensation for a driver is calculated.

Business Intelligence & Analytics

http://www.stevens.edu/howe/academics/graduate/business-intelligence-analytics

Optimization Methods

“Flex, Amazon’s new on-demand delivery service, promises to get your packages to you even sooner by hiring independent drivers to bring them to your house” (Washington Post, Sep 30th , 2015).

The optimization model for Amazon’s Uber like delivery service minimizes the delivery costs by allowing anyone with a car to work as a delivery agent. After the necessary background checks, agents are assigned ratings in aspects such as reliability, punctuality and behavior. Every time a shipment is completed the customer rates the delivery agent, these ratings become the basis for deciding the compensation of the delivery agent.

The model is primarily based on minimizing the distance travelled by the delivery agent to make the product deliveries. This is coupled with the cost of delivery per mile that is derived from the customer ratings and quality of service records.

The model selects the closest delivery agents to the seller on the basis of the former’s GPS coordinates, checks their availability and introduces them into the model for further processing.

A seller with multiple shipments can have one or more than one delivery agents allocated, to ship orders to multiple buyers.

Run every ½ hour

Run every delivery

task

Run every ½ hour

Run every ½ hour

i: The buyer (shipping site)K: The number of delivery agents involved in the task.Z : Total distance travelled by all agents involved in a delivery task.Qk:Capacity of the agentQr ki:The weight of goods to be delivered to site i, via agent k’s delivery route.dij:Distance between buyer i and buyer j.doi:Distance between seller o and first shipping site j.nk: Agent k needs to ship to nk buyers in a trip.L: Number of buyers in the shipping task.Rk: Agent k’s route.Rki: The position of shipping site i in agent k’s route.Sign(nk) : When agent k has more than one buyer to deliver to, it shows that Sign(nk)=1 and otherwise Sign(nk)=0.

Formula to calculate compensation

C=(K+C1M+C2H)R

C: Compensation for agent per task.K : Fixed compensationC1:Compensation rate for total distance in the task.C2:Compenstation rate for driving time in the task.H: Driving time (minutes)R: Agent rating