Repurposing RouteSmart’s Park-

and-Loop Tool: Building Innovative

Litter Abatement Routes

Leonard Huggins, PhD

City of Charlotte

Solid Waste Services

Overview

Operational Problem

Goals

Approach

Results

Lessons Learned

Outline: Managing the Plan

Benchmarking all streets with litter control service

(desired and actual)

SWS consistently ranks below the state average in collection costs and complaints

320K residences served weekly

Collected in FY2013

250 thousand tons of garbage

49 thousand tons of yard waste

48 thousand tons of recycling

City of Charlotte | Solid Waste Services

City of Charlotte and Mecklenburg County Official

Government Website. URL: http://charmeck.org

Develop Efficient Litter Control Routes

No existing optimized routes

Different service levels required

Limited staffing (max 18; avg. 11)

Diverse, varying demand

Miles of road to service

Need to collection data to inform

Public service

Resource management

Routing or Operational Challenge

Litter control crews at work

Develop systematic, optimized litter control routes that meet current resource allocations;

Identify and provide minimum level of service in lightly littered areas;

Identify and provide an elevated level of service in litter-inflicted areas;

Collect data that provide statistics for neighborhood awareness and educational campaigns (over time)

Identify hotspots (over time)

Project Goals

Other working team members Technology Manager

Director of Operations

GIS Technician

Field Operations Supervisors

Several Litter Control Crew Members

Approach 1: Build a Team

Map all streets serviced

Iterative benchmarking process

Highlight streets on paper map

Update streets layer

Determine miles of road to be serviced

648 miles

Determine regions

Two; later three

Determine level of affordable service

2 vs. 3 person crews

Optimize & customize routes

Approach 2: Work Flow

Driver reviewing map at a park

point

Designing regions

Core (1): 2 regions (bi-weekly service)

5 days per region

5 routes per day

Periphery (2) 4 regions (service every 4 weeks) 5 days per region

1 route per day

Total 3 regions every day

7 daily routes

Dense Network of

Streets Bi-Weekly Park & Loop

Routing Strategies

Sparse Network of

Streets Monthly Leap Frog

Density Service Level Route Design

Route Design

Park and Loop

Crew flexibility

2, 3 or 5 person crews

Densely woven street network (Core)

Required Crew

3 person crews only

Mainly long corridors (Periphery)

Leap Frog

Week # Routes

Average # of Sides

per Route

Average # of Segments

Average # of Miles Per

Route

Miles per crew member per

day

1 5 98 49 9.9 5 or less

2 5 100 50 8.7 4.3 or less

Core (Park and Loop): Zone 1

9.6 miles per day per route

4.8 miles per crew member

½ to 2/3 miles per hour per crew

(service time 0.15; industry baseline 0.2)

Zone 1 – Week 1 - Mondays

Park and Loop: Grouping Park Points

Customization: “Worker Bee” Tools

Use to

assign the

listed

sequence

number to a

segment.

View

summary

statistics

table one

loop at a

time.

Use this tool

to change

the walking

direction on

a segment.

Use this tool

to move the

park point

to a

different

point.

Three person – crew

1 Driver; 2 Collectors

Periphery: Leap Frog

Litter control crews separating recyclables (yellow

bag) from garbage (black bag) as they collect litter.

Leap Frog

Periphery (Leapfrog): Zone 2

Week # Routes

Average # of Sides

per Route

Average # of Segments

Average # of Miles Per

Route

Miles per crew member per

day

1 1 76 38 7.2 3.6 or less

2 1 92 46 7.2 3.6 or less

3 1 86 43 6.7 3.4 or less

4 1 88 44 6.3 3.2 or less

Managing the Schedule Chaos

A single end-of-day weight would not suffice for neighborhood specific data (not granular enough)

Set count points on the route map:

Park and loop routes

At every loop

At every park point

Leapfrog routes

At every series of park points

Harnessing Data from the Collection Chaos

Counted number of bags

Redistributed total weight as a per bag weight (provided both weight and volume measures)

Total miles covered

Weighted:

Bags per loop per hour

Miles per hour

Change Element

Change in bags collected per loop over time (community level change)

Productivity Matters … Regardless of Chaos

Baseline critical to optimization success

Many iterations to find “true” benchmarks

Customization of optimization product essential to get user buy-in

Using block #s instead of sequence

Utilize granular measures that capture data relevant to route improvement and business efforts

Lessons Learned