VRP、Cold Chain、Time Windows、Peak/Off-Peak、NYC

1. Optimizing Medicine &
Vaccine Delivery Routes in
New York City
Group members: Yuan Zou / Hao Wang

2. Research Background &
Objectives
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● Medicines/vaccines are highly temperature- and time-
sensitive: Cold chain violations directly lead to drug
spoilage and public health risks.
● Current Pain Points: Under NYC’s high population
density and complex traffic, existing fixed/experience-
driven routes suffer from low efficiency, high costs,
and unstable compliance rates.
1. Reduce total costs (vehicle activation, mileage, cold chain
energy consumption).
2. Improve cold chain compliance rates (reduce violations
and drug loss).
3. Increase on-time delivery rate (target: 95%).
≥
4. Provide actionable scheduling policy recommendations for
DOHMH.
Core Challenges Why routing realism matters
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Waiting-Time Paradox (Why delays feel
worse than averages)
1. Travel time is variable in NYC; we don’t “experience” the
mean travel time.
2. We are more likely to be caught in longer congestion
periods (inspection/waiting-time paradox).
3. Motivation: use peak/off-peak travel-time matrices +
service-level metrics (on-time / cold-chain risk), not just
average time.

4. Benchmark Service System
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Core Components
● Depot: NYC Public Health Laboratory (455 1st Ave, Manhattan), operating hours: 7:00–19:00.
● Receiving Sites (29): Gotham Health Community Health Centers, covering NYC’s 5 boroughs
(standardized addresses, coordinates, and opening hours).
● Current Operation Mode: Fixed/experience-driven scheduling (non-optimal).
● Key Stakeholders: DOHMH, PHL, 29 Health Centers, Patient Groups.
Benchmark Operational Indicators
● Core Metrics: Number of vehicles, total mileage, average arrival time, cold chain compliance
rate.
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6. Core Parameterization
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10. Optimization Model &
Methods
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Objective Function (Minimize Total Cost)

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Constraints

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Constraints

14. Data Collection
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Data Collection
● Real NYC case: 1 depot + 29 Gotham Health clinics
● Collected five types of data:
● Facility locations (address, coordinates, borough)
● Clinic opening hours time windows
→
● Population & demand estimates
● Distance / travel time between all nodes
● Vehicle & cold-chain parameters
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5.1 Data Sources
● NYC Health + Hospitals / Gotham Health websites
● clinic addresses, boroughs, opening hours
● NYC population / demographic data
to estimate population served by each clinic
● Public health guidelines (CDC / DOHMH)
vaccine usage rates, cold-chain limits
● Map / routing services
coordinates, distances, travel-time estimates
● Vehicle specification sheets
capacity, fuel cost, refrigeration properties
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5.2 Preprocessing &
Assumptions
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20. Results
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Scenario-Level Performance
Comparison
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Scenario Avg. Total Distance (km) Avg. Total Duration (hr) Avg. Total Load (kg)
Peak 81.79 12.95 70.84
Off-Peak 83.64 11.05 70.84

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Peak vs. Off-Peak
● Duration: Off-peak average route duration (11.05 hrs) is 17% shorter
than peak (12.95 hrs) due to reduced congestion.
● Distance: Off-peak average distance (83.64 km) is slightly higher
(+2.3%) than peak (81.79 km), reflecting optimized route coverage.
● Load: Identical average load (70.84 kg) across scenarios—demand
consistency confirms efficiency differences stem from traffic, not
delivery volume.

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Vehicle-
Level Total
Distance &
Load
Distribution
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Vehicle
(Scenario)
Total Distance
(km)
Total Load (kg)
Peak 1 140.85 102.36
Peak 3 15.13 9.11
Peak 4 89.40 101.04
Off-Peak 0 35.53 33.10
Off-Peak 2 84.54 91.69
Off-Peak 3 130.86 87.72

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Clinic Demand Distribution
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EfficiencyHighlights
● Load balance: Off-peak routes distribute load more evenly (e.g., 91.69 kg vs. 87.72 kg for
top vehicles) vs. peak’s unbalanced allocation (2 vehicles handle 98% of peak load).
● Geographic alignment: 70% of demand is in Brooklyn (42%) and Manhattan (28%)—model
prioritizes these areas, cutting unnecessary long trips (e.g., Staten Island: 2% demand,
minimal routes).

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VRP Map
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VRP Map
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Thanks!