1 / 12

Production Scheduling

This study by Benoît Lagarde, Soufiane Ahallal, and Malek Ben Sliman explores the challenges faced by restaurant owners in managing delivery services across multiple locations. It addresses critical questions on reducing customer waiting times and delivery costs. The proposed solution is to centralize the delivery process by utilizing a single fleet of delivery staff for several restaurants. Through simulation-based analysis and various scenarios, the research showcases the effectiveness of this centralized model in improving delivery efficiency while maintaining customer satisfaction.

lalasa
Télécharger la présentation

Production Scheduling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Production Scheduling Delivery Service - Restaurants Benoît Lagarde – bl2506 SoufianeAhallal– sa3103 Malek Ben Sliman– mab2343

  2. Agenda I- Background II- Algorithm III- Simulation & Results

  3. I- Background • 1) The problem • Questions: - How can we decrease customers’ waiting time? - How can we decrease costs of delivery? • Current situation: - A restaurant owner owns N restaurants - Each restaurant has its own fleet of delivery men and each faces problems with their delivery service. • The idea: Centralize delivery by only having a unique fleet of deliverymen that would work for the whole network of restaurants

  4. I- Background • 2) Inputs • Average Restaurant: - Frequency: 50 orders/lunch (Normal Distribution over lunch) - Nb of delivery men: 3 delivery men - Cooking time: 18 minutes • Distances from a restaurant to its customers

  5. II- Algorithm • How it works • Input: Number of restaurants, number of simulations, number of delivery men for each case (centralized and decentralized), restaurant locations • Process: Model 1: Decentralized Generate Orders Outputs Model 2: Centralized • rj: time of order • Cj= rj+CT+ travel time • rj: time of order • (xi, yi): customers’ coordonates

  6. II- Algorithm • How it works • Models: At each unit of time t Update Customers List Outputs • (rj, Cj) Assign a delivery man Update delivery men positions YES Order at t? NO Update delivery men positions

  7. III- Simulation & Results • 1) Simulation • Scenarios: - Samenumber of delivery men: How doesit impact the waiting time? - Fewerdelivery men: How muchcanwedecrease the number of delivery men whilekeeping the sameaveragewaiting time? • Parameters - Different restaurant densities: 1 restaurant/ 0.1 mile, 0.3 mile and 0.5 mile - Differentnumber of restaurants: 4, 9, 16 and 25 restaurants (on a square 3x3…) - Average on 3000 simulations

  8. III- Simulation & Results • 2) Results – Delivery ONLY • Same # drivers – Mean(Lj) • 55%

  9. III- Simulation & Results • 2) Results – Delivery ONLY • Same # drivers – Variance(Lj) • 87%

  10. III- Simulation & Results • 2) Results – Delivery ONLY • Lower # drivers -Still 33% improvement of the variance • 19%

  11. Conclusion • It works pretty well: • To go further: • Have a more complex model: • - More than 1 order/ delivery man • - Possibility to takeordersfromdifferent restaurants at the same time • - When a delivery man is free, where to go (not to the closest restaurant) • - StochasticParameters: cooking time, travel time, number of orders Samenumber of delivery men Lowernumber of delivery men

  12. Thankyou!

More Related