SIMULATING ENVIRONMENTAL IMPACTS OF URBAN DELIVERIES IN CATERING SECTOR

Transcription

1 SIMULATING ENVIRONMENTAL IMPACTS OF URBAN DELIVERIES IN CATERING SECTOR 7 th METRANS International Urban Freight Conference 2017 Track 4-2 Data and Sarra JLASSI, Arthur GAUDRON, Simon TAMAYO & Arnaud de LA FORTELLE URBAN LOGISTICS RESEARCH CHAIR MINES ParisTech, PSL Research University, Centre de Robotique 60 boulevard Saint-Michel, Paris, France 1

2 MOTIVATIONS Goods movements represent between 20 and 30% of vehicle kilometres, corresponding to 16-50% of the emissions of air pollutants, depending on the pollutant considered, by transport activities in a city (Dablanc, 2007) Objective: Use simulation to understand the relationships between a given decision and its expected impacts (economic, social and environmental) Research question: What are the impacts of using different types of fleet for the catering sector? 2

3 CATERING SECTOR Significant seasonality: touristic periods, holidays, seasonal products Highly perishable products: Fast (next day delivery) and traceability constraints Just in Time (low inventories) Two types of clients: Commercial (restaurants) Collective (schools, companies, etc.) Loading rate of vehicles is limited by: The time windows (delivery during the morning) The weight rather than the volume 3

4 TIME WINDOW CONSTRAINT 7:00 11:00 DELIVERY TIME REQUESTED BY CLIENTS 6:00 13:00 APPROACH RETURN DROP OFF INTER-CLIENT COMMUTE 4

5 MODEL FOR CATERING DELIVERIES KPI Loading rate Number of customers Demand Distributors Fleet Uniform law (100 kg 500 kg) Uniform law (200 kg 600 kg) 1 distributor 2 distributors Trucks only Vans only Mixed Driven kilometres per ton of goods Number of vehicles Polluting emissions 5

6 SIMULATION: THREE DAYS OF ORDERS AND DELIVERIES IN PARIS 6

7 RESULTS 48 scenarios Corresponding to all combinations of the input parameters 7

8 RESULTS SUMMARY Selection of 10 scenarios considering the best case for each parameter NUMBER OF CUSTOMERS NUMBER OF WHAREHOUSES DEMAND BEST TRUCK SCENARIO BEST VAN SCENARIO High density Max. nb of cust = 400 High consolidation Min. nb of w = 1 High massification [200, 600] WORST TRUCK SCENARIO WORST VAN SCENARIO Low density Min. nb of cust = 100 Low consolidation Max nb of w = 2 Low massification [100, 500] 8

9 LOADING RATE Improvement of the loading rate From 100 to 400 customers From 2 to 1 warehouse From kg to kg orders 0% 1% 2% 3% 4% 5% 6% 9

10 DRIVEN KILOMETRES PER TON Reduction of driven kilometres per ton From 100 to 400 customers From 2 to 1 warehouse From kg to kg orders -10% -5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 10

11 EMISSIONS FACTORS Total emission = factor * total distance VAN TRUCK CO 2 (g/km) CO 2 (g/km) NO x (g/km) NO x (g/km) SO 2 (mg/km) 1.75 SO 2 (mg/km) 5.55 From CE TU Delft report STREAM Freight Transport

12 POLLUTING EMISSIONS CO 2 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Worst truck scenario Best truck scenario Worst van scenario Best van scenario SO 2 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Worst truck scenario Best truck scenario Worst van scenario Best van scenario 12

13 POLLUTING EMISSIONS 100% NO x 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Worst truck scenario Best truck scenario Worst van scenario Best van scenario 13

14 BEST SCENARIOS DIRECT COSTS cost drivers = number of drivers 83 cost fuel = number of km cost trucks = number of trucks 50 cost vans = number of vans 40 Daily cost for trucks 24 drivers trucks L of fuel 526 TOTAL 3718 Daily cost for vans 114 drivers vans L of fuel 614 TOTAL

15 CONCLUSION (1/2) Improvements came from: High customer density Massification Inventory consolidation Model shows the limiting factor of the trucks: Time windows for the trucks 16

16 CONCLUSION (2/2) From the environmental perspective, truck and van scenarios are clearly distinct, although difficult to compare: Best truck scenario compare to best van : -20% CO 2, -20% SO 2, +33% NO x From the economic perspective, trucks are less expensive: Truck fleet s daily direct cost is 75% cheaper than a van fleet From the infrastructure perspective, 75% less km driven in the city with trucks 17

17 LIMITS & PERSPECTIVES Model does not use real data Real data set to calibrate the model Environmental data are rough estimations Physical model for consumption Less kilometres driven with trucks, but what are the impacts in terms of congestion? Traffic simulation Case study restricted to the catering sector Apply simulation to other activities: express delivery, pharmacies, etc. 18

18 SIMULATING ENVIRONMENTAL IMPACTS OF URBAN DELIVERIES IN CATERING SECTOR 7 th METRANS International Urban Freight Conference 2017 Track 4-2 Data and Sarra JLASSI, Arthur GAUDRON, Simon Tamayo & Arnaud de La Fortelle URBAN LOGISTICS RESEARCH CHAIR MINES ParisTech, PSL Research University, Centre de Robotique 60 boulevard Saint-Michel, Paris, France 19