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Optimal food delivery operation plan with periodic delivery and multi vehicle routing with time windows
- 1. Optimize Food Delivery Operation Plan with Periodical Delivery and Multi Vehicle Routing with Time Window Xiyuan Ren, Weili He, Mingyi He TR-GY 7013 course project
- 2. 1. Introduction 2. System Design 3. Methodology 4. Data Description 5. Result & Conclusion Content
- 3. ● Jardine Restaurant Group (JRG) Hong Kong operates the Pizza Hut, PHD and KFC franchise in Hong Kong and Macao with combined store footprint of over 200 stores. ● Currently, more than 60% orders are delivered and this number is growing during the COVID-19 epidemic. ● However, JRG have about 3-7 riders in each store, and riders only serve for orders in their own stores. In peak-hour, ready-to-be-delivered order could wait a long time in a store running out of riders, while meanwhile riders at nearby stores are available. Introduction — Background
- 4. Introduction — Problem Statement
- 5. Introduction — Data Used 1) From June to September 2020 2) 147,507 records after cleansing 3) 62 KFC stores, 104 Pizza Hut stores, 36 PHD stores.
- 6. System Design — benchmark system description Use case diagram for benchmark system “Incomplete and Irrational” Benchmark system： ● Most of the orders are delivered one to one by riders of each store, each delivery cost the store 9$. ● Rider-sharing occurs infrequently and spontaneously. Current rider-sharing rate is low Current rider-sharing isn’t well-organized
- 7. System Design — alternative system Alternative system： ● A new platform to generate organized rider-sharing scheme. ● Provide 3rd party riders when stores are running out of their own riders. ● Consider rider-sharing in peak- hour when one-to-one assignment leads to long waiting time. Use case diagram for alternative system “Rider-sharing Platform”
- 8. System Design — objective In order to ... 1. Decrease customers’ total waiting time for food delivery 2. Increase rider-sharing rate to save labor costs for stores 3. Decrease the wasted travel distance for riders Provide food delivery rider-sharing schemes
- 9. Scenario 1— rider-sharing within the same store Order 1 KFC PHD Pizza Hut Residential Order 2 Order 3 Order 4 Original delivery route Ridesharing delivery route Order 1&2, 3&4 can be delivered together within the time window
- 10. Scenario 2— sharing between two adjacent stores Order 3 Order 4 Order 1 KFC PHD Pizza Hut Residential Original delivery route Ridesharing delivery route Order 1&4, 2&3 can be combined within the time window, while Order 1&3 cannot Order 2
- 11. System Design — KPI comparison for systems Table. System Validation Average waiting time Number of shared orders Average delivery duration Number of 3rd party riders Number of Deliveries Benchmark — — — — — Scenario 1&2 5min / 55min ？ — Scenario 1&2 3min / 45min ？ Scenario 1&2 1min / -- ？
- 12. Methodology 1. Mathematical Formulation Decision Variables Parameters
- 13. Methodology 1. Mathematical Formulation Objective Function Constraints
- 15. Methodology 2. Main Model Settings ● We set the scheduling period as 1min/ 3min/ 5min and run the model to solve the pickup and delivery courier routing problem using the data inside this period. It’s can actually satisfy the dynamic routing given the solving period. ● After specifying the constraints, we use OR-Tools[1] VRP package to solve the problem. For the first strategy, "PATH CHEAPEST ARC" method is used. The method starts from a route "start" node, connects it to the node which produces the cheapest route segment, then extends the route by iterating on the last node added to the route. ● Then we use default local search strategy Greedy Descent to do the further searching. Greedy Descent is a strategy accepting cost-reducing local search neighbors until a local minimum is reached. [1]https://developers.google.com/optimization/routing/routing_options.
- 16. Data Description ● Data Source: Jardine Restaurant Group Hong Kong, 1st June to 30th September ● 62 KFC stores, 104 Pizza Hut stores, 36 PHD stores. ● Info contains store address, delivery address, order generation time, delivery assign time, rider arrival time, rider departure time and order complete time ● Records from 06/29/2020 - 07/04/2020 were selected for analysis and modeling
- 17. Data Description Statistics ● Average waiting time 42.0 min (order preparation time 21.3 min + trip duration 20.7 min) ● Estimated waiting time 47.8 min > average actual waiting time 42.0 min ● Peak hour is at 18:00 - 19:00, with 100 orders in 6 days, 19 orders per hour
- 18. Time window avg waiting time Avg trip duration 3rd party rider Shared order trips orders Benchmark - 42:01 20:44 0 195 316 411 5 55 40:13 17:28 15 202 301 411 3 45 39:12 17:05 30 173 318 411 1 - 38:25 16:51 54 56 382 411 Results — KPI of Scenario 1
- 19. Time window avg waiting time Avg trip duration 3rd party rider Shared order trips orders Benchmark - 42:01 20:44 0 195 316 411 5-55 55 38:30 15:30 0 267 251 411 3-45 45 37:23 15:11 0 240 274 411 1 - 37:03 15:30 0 221 291 411 Results — KPI of Scenario 2
- 20. Scenario 1 Scenario 2 Results — Trip Composition in Scenario 1&2
- 21. Discussion 1. Lack the timestamp of when orders are prepared, order dispatch time could be earlier T1 Range a lot Job Assign Duration Job Assign Time T2 ~ Ave. 0 min Rider Assign Duration Rider Assign Time T3 ~ Ave. 5 min Rider arrive at stores Pick Up Time T4 ~ Ave. 10 min Rider Go to the Scooter Trip Dispatch Time T5 ~ Ave. 18 min Rider On the Way Delivered Time T0 Does not matter User Explore the Menu Quote Time Food preparation time
- 22. 2. When to enable periodical delivery? No order 1 order 2 orders and above Discussion
- 23. Conclusion 1. Converting dynamic routing problem to static, the more jobs in each assignment, the higher efficiency (less cost in delivery labors). 2. Delayed dispatch time in periodical delivery is not always lead to a later arrival time.
Editor's Notes
- food delivery is inherently suitable for rider-sharing since before a rider’s departure, there is a long waiting time for order preparation. In this time window, we can keep receiving new orders and calculating combination choices
- Maximize number of shard orders within acceptable waiting time
- Here we can choose two to compare the result and running efficiency, for heuristic, generally we have genetic algorithm, tabu search, simulated annealing
- Here we can choose two to compare the result and running efficiency, for heuristic, generally we have genetic algorithm, tabu search, simulated annealing
- Here we can choose two to compare the result and running efficiency, for heuristic, generally we have genetic algorithm, tabu search, simulated annealing
- Here we can choose two to compare the result and running efficiency, for heuristic, generally we have genetic algorithm, tabu search, simulated annealing
- Here we can choose two to compare the result and running efficiency, for heuristic, generally we have genetic algorithm, tabu search, simulated annealing
- Here we can choose two to compare the result and running efficiency, for heuristic, generally we have genetic algorithm, tabu search, simulated annealing
- Basically, the more rigorous operation plan requires more labor but customers, of course, gonna have a shorter waiting time. It is a little surprising that in the 1 min, no time window operation plan, we still have approximately 20% orders are delivered in shared trips, as routes of orders could be totally overlapped, and we ignored the dropoff time in each order.
- In scenario 2, the greatest improvement is no extra 3rd party riders are needed in all operation plans. And average waiting time from each operation plan are shorter comparing to scenario 1. But scenario 2 might cause a higher management cost as it requires two stores using the same delivery management system.
- Here are two plots for number of orders per trip in different scenario and operation plan. The max number of orders in one trip is 4 in scenario 1, and 5 in scenario 2.
- We have some problem cannot be solved in this project. The first one, in fact, the order dispatch time could be early. As we discussed it before, currently, the lateness might be caused by lack of available riders, so the order cannot be delivered until a rider came back. Or due to unwisely ridesharing plan, an order have to wait a long time find a shareable order. From the dataset provided by JRG, we could know when did a rider arrive at a store to pick up, and when did the rider left, but the time when an order is ready to go could be anytime from T3 to T5. So in a nutshell, we believe our design could have a better performance in reality.
- In our simulation, periodical delivery method is no always enabled. When the order volume is sparse, we just deliver orders immediately as it is unlikely to find a shareable order in a short time. However, the problem is, when to enable periodical delivery. This plot shows the order volume in every 5 minutes, grey means no order, yellow means only one order, red means multiple orders. It is clear that the peak of order volume is not the same in each day. In the future, prediction algorithms like ARIMA could be implemented to predicted when to enable periodical delivery to decrease meaningless waiting time.
- We have two main takeaways from this project