Shared Car Network
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Transcript of Shared Car Network
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SHARED CAR NETWORKPRODUCTION SCHEDULING PROJECT – SPRING 2014
Tyler Ritrovato (tr2397)Peter Gray (png2105)
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THE IDEA Google’s Driverless Car began design in 2005
and continues to advance Advent of Uber and Lyft services in late
2000’s
We see an opportunity…
New Driverless Car Technology+
Efficient Dispatching Algorithms__________________________
Shared Car Network
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SHARED CAR NETWORK
Instead of owning multiple cars per household, individuals or families become a member of a Shared Car Network (SCN)
Cars dispatched based on an efficient algorithm
BENEFITS Less cars on road is better for
environment Reduced traffic (at scale) No more hassle of owning and
maintaining personal cars
RISKS Not as flexible for on-demand trips Potential for late or missed pick-ups
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RELATING TO A SCHEDULING PROBLEM
Machines All the cars in the network Regular Job Picking up a customer and dropping that customer off. Defined by the
following inputs:o Origino Destinationo Pick-up Time o Time due at destination
Processing Times: Unoccupied Car time from last drop-off to next customer pick-up Occupied Car time from pick-up of customer to drop-off
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OPTIMIZATION DECISION
# of Machines
% of Requests ServicedMax Lateness
Minimize # of Machines
Constrain on Max Lateness and Minimum % of Requests Serviced
Therefore, our problem boils down to the following production scheduling problem:
P | rj , Lmax | m
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SAMPLE DATA Downloaded September, 2013 data
from Citibike.com Focused on the morning rush hour (8:00
AM- 10:00 AM) on Monday, September 9th.
Limited data to nine citibike ids (machines)
Release date Start of trip Due date Trip Duration plus
20% 24 Total Jobs
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ALGORITHM STRUCTURE
Utilizing a Greedy Algorithm: Step 1: List job requests in ascending order (morning to night)
Step 2: For each job, choose the machine with the lowest metric score Metric Score Remaining processing time of current job + time to reach customer – time since
availability Add a machine if all of the possible machines lead to an undesirable lateness value
Step 3: Continue until all jobs are assigned
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ALGORITHM EXAMPLE
Job 1: Starts at 8:01 AM at W 25 St & 6 Ave and ends at 8:12 AM at Broadway & W 51 St
Add job 1 to machine 1
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ALGORITHM EXAMPLE
Job 2: Starts at 8:04 AM at 11 Ave & W 41 St and ends at 8:33 AM at John St & William St
Must add a second machine because using just machine 1 would lead to being late by 15 minutes Lateness= 8 minutes remaining processing
time from job 1 + 7 minutes to travel from job 1 ending point to job 2 starting point✓
Add job 2 to machine 2
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METRIC SCORE EXAMPLE
Job 11: Starts at 9:00 AM at Fulton St and Grand Ave and ends at 9:04 AM at Lafayette Ave and Classon Ave
At this point in the algorithm, there are 5 machines
What machine should job 11 be assigned to?
Machine 1: Available since 8:34 and is 24 ½ minutes away from pickup location
Metric Score = 0 + 24 ½ - 26 = -1 ½ Machine 2: Busy until 9:09 and is 11 minutes away
Metric Score = 9 + 11 - 0= 20Machine 3: Busy until 9:07 and is 5 minutes away
Metric Score = 7 + 5 - 0 = 12 minutes awayMachine 4: Available since 8:55 and is 33 minutes away
Metric Score = 0 + 33 - 5 = 28Machine 5: Busy until 9:09 and is 0 minutes away
Metric Score = 9 + 0 - 0 = 9Machine 1
Metric Score = Remaining processing time of current job + time to reach customer – time since availability
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GANTT CHART
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RESULTS & NEXT STEPS
Results: Citibike required 9 bikes needed for 24 job instances Our shared car network algorithm required only 6 machines for 24 job instances
No late jobs We service 100% of all requests
Next Steps: Try out our algorithm with more data (what happens when there are 100, 1000 jobs?) Play with max lateness and % of requests serviced parameters to see affect on machine
requirements Create a program to compute algorithm
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QUESTIONS?
“General Solutions get you a 50%
tip.”
Source: xkcd.com