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Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit
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Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit

This repository describes the results of the code and the data for the following paper: https://smarttransit.ai/files/sivagnanam2022offline.pdf

ABSTRACT

Vehicle routing problems (VRPs) can be divided into two major categories: offline VRPs, which consider a given set of trip requests to be served, and online VRPs, which consider requests as they arrive in real-time. Based on discussions with public transit agencies, we identify a real-world problem that is not addressed by existing formulations: booking trips with flexible pickup windows (e.g., 3 hours) in advance (e.g., the day before) and confirming tight pickup windows (e.g., 30 minutes) at the time of booking. Such a service model is often required in paratransit service settings, where passengers typically book trips for the next day over the phone. To address this gap between offline and online problems, we introduce a novel formulation, the offline vehicle routing problem with online bookings. This problem is very challenging computationally since it faces the complexity of considering large sets of requests—similar to offline VRPs—but must abide by strict constraints on running time—similar to online VRPs. To solve this problem, we propose a novel computational approach, which combines an anytime algorithm with a learning-based policy for real-time decisions. Based on a paratransit dataset obtained from the public transit agency of Chattanooga, TN, we demonstrate that our novel formulation and computational approach lead to significantly better outcomes in this setting than existing algorithms.

ACKNOWLEDGEMENT

This material is based upon work sponsored by the National Science Foundation under Grant CNS-1952011 and by the Department of Energy under Award DE-EE0009212.

Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the Department of Energy.

Directories

algo: contains implementation of,

Our Algorithms

  1. Online Booking Simulator
  2. Deterministic Greedy
  3. Simulated Annealing

Wrapper for other existing baselines

  1. Google or-tools
  2. VRoom

base: contains the custom implementation to solve the para-transit optimization problem.

common: constants and common utilities shared by source files in other folders (algo, base).

data: contains the all data to run paratransit sample instances.

Data Folder Description:

Main Input Data data/AGENCY_A/base/para_transit_trips_2021.csv - 180 days of requests data

Trained Models

  1. data/AGENCY_A/models/agent_with_anytime_model.h5 - trained (with anytime) RL agent model.
  2. data/AGENCY_A/models/agent_with_anytime_weights.h5 - trained (with anytime) RL agent model weights.
  3. data/AGENCY_A/models/agent_without_anytime_model.h5 - trained (without anytime) RL agent model.
  4. data/AGENCY_A/models/agent_without_anytime_model.h5 - trained (without anytime) RL agent model weights.

TRAINING AND EVALUATION

I) Normal Environment Setup (supports Linux and MacOSX):

Please make sure the following before executing the scripts

  1. python 3.8
  2. Install all modules listed in "requirements.txt" (using the command pip install -r requirements.txt).
  3. Execute add_zip_codes.py to add retrieve zip codes for pickup and dropoff locations (this can take significant time (900 minutes for full data, with 25k trip requests), if you try with full data; so use small set of data, i.e., filter by date)
  4. Execute generate_travel_matrices.py to add travel time matrices and travel distance matrices (this can take significant time, if you try with full data; so use small set of data, i.e., filter by date)

Sample Training:

  1. Train policy without anytime algorithm in between, and Run VRoom

    python3 train_agent.py --train_env_type=agent_without_anytime --random_seed=0

  2. Train policy with anytime algorithm in between, and Run VRoom

    python3 train_agent.py --train_env_type=agent_with_anytime --random_seed=0

Allowed Parameters (Training): train_env_type - indicates the environment type possible values: agent_without_anytime - training without anytime algorithm in between agent_with_anytime - training with anytime algorithm in between

anytime_algo - indicates anytime algorithm that used to train the policy.

random_seed - random_seed to select sample days

no_of_episodes - number of episodes (number of days

anytime_plan_duration - time to run anytime algorithm in seconds during deciding tight pickup windows

plan_duration - time to run anytime algorithm in seconds at the end of the day in online simulator or running simulated annealing alone

Sample Execution:

sample chains with date 0 - 179

  1. Greedy Algorithm
python3 run_optimizer.py --algo=greedy --date=0
  1. Simulated Annealing Algorithm
python3 run_optimizer.py --algo=sim_anneal --date=0
  1. Online Booking with Agent Trained without anytime + Simulated Annealing at the End
python3 run_optimizer.py --algo=agent_without_anytime --date=0
  1. Online Booking with Agent Trained with anytime in between + Simulated Annealing at the End
python3 run_optimizer.py --algo=agent_with_anytime --date=0
  1. VROOM
python3 run_optimizer.py --algo=vroom --date=0
  1. Google OR-Tools Routing
python3 run_optimizer.py --algo=routing --date=0

Allowed Parameters (Execution):

algo - indicates the name of algorithm

anytime_algo - indicates anytime algorithm that used to train the policy.

date - indicates the date

anytime_plan_duration - time to run anytime algorithm in seconds during deciding tight pickup windows

plan_duration - time to run anytime algorithm in seconds at the end of the day in online simulator or running simulated annealing alone