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Production Scheduling

Production Scheduling. Delivery Service - Restaurants. Benoît Lagarde – bl2506 Soufiane Ahallal – sa3103 Malek Ben Sliman – mab2343. Agenda. I- Background II- Algorithm III- Simulation & Results. I- Background. 1 ) The problem. Questions :

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Production Scheduling

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  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!

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