Plowing the Streets of Pittsburgh A Dynamic Route Planning System J. - - PowerPoint PPT Presentation

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Plowing the Streets of Pittsburgh A Dynamic Route Planning System J. Kinable 1 , 2 W-J. van Hoeve 2 . Smith 1 S.F 1 Robotics Institute - Carnegie Mellon University, USA 2 Tepper School of Business - Carnegie Mellon University, USA January 19,

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Plowing the Streets of Pittsburgh

A Dynamic Route Planning System

  • J. Kinable 1,2

W-J. van Hoeve 2 S.F . Smith 1

1Robotics Institute - Carnegie Mellon University, USA 2Tepper School of Business - Carnegie Mellon University, USA

January 19, 2016

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 1 / 33

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Motivation & Goals

"Drivers have printed routing directions laying on the passenger seat. Most of them know the route (or an interpretation thereof) by heart." "A lot of time is lost due to emergency plow requests." "People complain that primary roads in their district are full of snow, while in other districts tertiary roads have already been serviced."

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 3 / 33

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Motivation (2)

1

Consumable resource costs: 4,3M$ (2014/2015), 3.3M$ (2013/2014)

2

Labor costs: 3.3M$ (2014/2015)

3

Equipment costs: 800K$ (2014/2015)

4

Snow and ice control staff: 275 safety economy environment Generalizes to street sweeping, street maintenance, garbage collection, etc.

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 4 / 33

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Goal

Design a route planning system that issues optimized turn-by-turn instructions to the vehicles as they execute routes, and dynamically revises these plans as unexpected events force changes.

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 5 / 33

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Problem Description - Routes

Road segments: start/end # lanes priority class shape driving speed directionality

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 6 / 33

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Problem Description - Vehicles

Vehicles: Location Salt Capacity Fuel Capacity Salt economy Fuel economy

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 7 / 33

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Problem Description - Depots

Depots: Location Salt (y/n) Fuel (y/n)

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 8 / 33

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Related models

Chinese postman problem Vehicle routing with resource constraints Resource-constrained project scheduling with renewable and non-renewable resources For overview of Snow plowing literature, see N. Perrier, A. Langevin,

  • et. al: [1, 2, 3, 4]

Synchronized arc routing for snow plowing operations [5] City of Dieppe: 24,000 inhabitants, 462 intersections, 1,234 road segments (≤ 20% Downtown Pittsburgh)

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 9 / 33

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Model - Objective

Objectives:

1

Makespan

2

Minimize deadheading

3

Weighted objective of Completion times per priority class

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 10 / 33

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Model - Constraints

Constraints:

1

Vehicles start/end at depot

2

Every lane on a street segment must be covered, subject to traffic direction.

3

Renewable resource constraints: fuel, salt

4

Time constraints

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 11 / 33

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Model

Job:

1

ID

2

start location

3

end location

4

duration

5

fuel & salt requirement Job types:

1

source depot job

2

target depot job

3

plow job

4

refuel job

5

resupply salt job

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 12 / 33

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Mathematical models

Mixed Integer Programming Constraint Programming

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 13 / 33

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Mixed Integer Programming Model - Outline

Variables:

◮ Three-index formulation: Vehicle k performs job j after job i. ◮ Variables recording resource utilization: time, salt, fuel

Constraints:

◮ All plow jobs have to be performed, refuel/resupply jobs are optional ◮ Decide on direction for bidirectional jobs ◮ Resource constraints for fuel/salt/time

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 14 / 33

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Constraint Programming Model - Outline

Variables:

◮ Job (interval) variables. ◮ Job to vehicle assignment variables

Constraints (outline):

◮ Job sequencing constraints for each vehicle ◮ Alternative constraint for job to vehicle assignment ◮ Cumulative functions to manage resources

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 15 / 33

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Heuristic

Constructive Heuristic Late Acceptance improvement heuristic

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 16 / 33

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Constructive Heuristic - Phase 1

1

Order jobs based on their priority

2

Constructively build schedules - Insert jobs 1 by 1

?

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 17 / 33

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Constructive Heuristic - Phase 2

1

Insert Refuel and Resupply salt jobs

? ?

Fuel Time

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 18 / 33

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Late acceptance improvement heuristic

Iteration

Compare with solution L iterations ago

Quality (lower is better)

Candidate solution Solution L iterations ago

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 19 / 33

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Late acceptance heuristic

Moves:

1

Swap

2

Remove/insert Termination:

1

Time limit

2

Max number of non-improvements

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 20 / 33

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Experiments - Setup

1

Take 2 snapshots from OSM

2

Extract graph, pre-process and ensure Strong Connectivity

3

Execute routing algorithm

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 21 / 33

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Routing and Plowing Graph

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 22 / 33

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Experiments (4/22 instances)

Kamin St:

1

28 intersections

2

45 plow jobs

3

plowing: 4mi

4

bidirectional: 38% Residential:

1

2441 intersections

2

4073 plow jobs

3

plowing: 316mi

4

bidirectional: 64% mt Washington:

1

406 intersections

2

577 plow jobs

3

plowing: 52mi

4

bidirectional: 81% Downtown:

1

345 intersections

2

724 plow jobs

3

plowing: 38mi

4

bidirectional: 38%

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 23 / 33

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Experiments

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 24 / 33

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Experiments - Kamin

0.2 0.4 0.6 0.8 1 : : : 2 : : 4 : : 6 : : 8 : : 1 : : 1 2 : : 1 4 : : 1 6 : Coverage(%) Time LA range LA average Constructive Heuristic CP

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 25 / 33

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Experiments - Downtown

0.2 0.4 0.6 0.8 1 : : : 1 : : 2 : : 3 : : 4 : : 5 : 1 : : 1 : 1 : 1 : 2 : 1 : 3 : Coverage(%) Time LA range LA average Constructive Heuristic CP

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 26 / 33

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Experiments - Mnt Washington

0.2 0.4 0.6 0.8 1 : : : 1 : : 2 : : 3 : : 4 : : 5 : 1 : : 1 : 1 : 1 : 2 : 1 : 3 : 1 : 4 : 1 : 5 : 2 : : Coverage(%) Time LA range LA average Constructive Heuristic CP

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 27 / 33

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Experiments - Residential

0.2 0.4 0.6 0.8 1 : : 1 : : 2 : : 3 : : 4 : : 5 : : 6 : : 7 : : 8 : : 9 : : 1 : : Coverage(%) Time LA range LA average Constructive Heuristic

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 28 / 33

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Experiments - Residential Pittsburgh solution

Schedule completion time: 07:38:33

01:30:00 02:00:00 02:30:00 03:00:00 03:30:00 04:00:00 04:30:00 05:00:00 05:30:00 1 2 3 4 5 Time Vehicle Pittsburgh Residential - Makespan (runtime 2h) Plowing Deadheading 5 10 15 20 25 30 35 40 45 50 1 2 3 4 5 Value Vehicle Pittsburgh Residential - Makespan (runtime 2h) Refuels Resupply salt U-turns

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 29 / 33

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Summary of preliminary results

MIP not suitable: bounds are too weak CP works well for small instances Constructive heuristic finds reasonable solutions in ms. LA heuristic produces solutions of consistent quality Idea: Improve LA solutions with a CP-Large Neighborhood Search

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 30 / 33

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Future requirements

1

Road priorities

2

U-turns

3

Vehicle weight/width restrictions on roads

4

Efficient construction/updating of distance matrix

5

Adaptive system

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 31 / 33

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Bibliography I

[1] N. Perrier, A. Langevin, and J. F . Campbell, “A survey of models and algorithms for winter road maintenance. part i: system design for spreading and plowing.” Computers & Operations Research,

  • vol. 33, pp. 209–238, 2006.

[2] ——, “A survey of models and algorithms for winter road

  • maintenance. part ii: system design for snow disposal,” Computers

& Operations Research, vol. 33, no. 1, pp. 239 – 262, 2006. [3] ——, “A survey of models and algorithms for winter road

  • maintenance. part iii: Vehicle routing and depot location for

spreading,” Computers & Operations Research, vol. 34, no. 1, pp. 211 – 257, 2007.

  • J. Kinable (Carnegie Mellon University)

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Bibliography II

[4] ——, “A survey of models and algorithms for winter road

  • maintenance. part iv: Vehicle routing and fleet sizing for plowing

and snow disposal,” Computers & Operations Research, vol. 34,

  • no. 1, pp. 258 – 294, 2007.

[5] M. A. Salazar-Aguilar, A. Langevin, and G. Laporte, “Synchronized arc routing for snow plowing operations.” Computers & Operations Research, vol. 39, no. 7, pp. 1432–1440, 2012.

  • J. Kinable (Carnegie Mellon University)

January 19, 2016 33 / 33