IEEE Electrification - September 2019 - 40

agent-based models of trucks, central depots, and dumpsters to conduct a simulation of the proposed IoT-guided
routing system. This simulation model uses data collected
from the CDA, Islamabad. In the following, we present the
details of the components used in our simulation.

Dumpster Agent
A dumpster is modeled as an entity, as shown in Figure 15. It is switched on and remains in the in-use state
until the user switches it off. When in use, it randomly
changes its levels (red, green, and blue) to mimic the
behavior of waste accumulation. We use the data collected from the CDA to randomize the behavior of the population dumping the waste in different parts of the city.

Latitude
Longitude
Trucks
Color
Limit
Time
TotalDistance

getDLocation

Statechart
Idle

Processing

Dispatching

Figure 16. The central depot/recycling plant agent.

Depot
Capacity
Message

Statechart
Idle

Available
Color
Route
System

Moving

Counter
GetAgent

ProcessingDumpster

CurrentX
CurrentY
Distance

Returning

StartTime
StopTime

Stopped

Figure 17. The truck agent.

40

I E E E E l e c t r i f i cati o n M agaz ine / SEPTEMBER 2019

The dumpster levels are reset when the truck or recycling
vehicle picks up waste.

Central Depot/Recycling Plant Agent
The central depot (Figure 16) collects the current state of
all of the dumpsters at fixed time instants and schedules
trucks to serve specific dumpsters. It runs an optimization
algorithm to optimize the routes and minimize the number of vehicles used to collect waste. In the case of the
central depot, the waste transportation vehicles are dispatched once per day. The dispatch starts at 9:00 a.m. and
moves to designated locations. When the vehicle arrives
at a dumpster, that dumpster is emptied manually using
human labor. This resets all of the dumpsters levels. The
vehicle then moves to the next dumpsters in its assigned
route. When the vehicle as at full capacity, it goes to the
landfill to empty the waste. If there are further jobs
remaining, it returns and continues. Otherwise, it returns
to the depot. In the case of a recycling plant, the dispatch
is made at any time to collect the recyclable material of
choice. When the dispatch is made, the goal is to collect n
number of units from different dumpsters of the same
type of material (e.g., glass, paper, or tins).

Optimization Algorithm
To solve the problem of vehicle routing for multiple pickup
locations and multiple trucks, we transformed the problem
into the capacitated vehicle routing problem with time
windows (CVRPTW), a well-defined problem in the literature. In the CVRPTW, three types of tasks are identified:
1) delivery, in which a load taken from one or more places is to be delivered to a certain destination
2) pickup, wherein multiple items are to be picked up
from many places
3) shipment, when items are taken from different locations and off-loaded to multiple locations.
We selected shipment because, in this task, waste is collected from different dumpsters and off-loaded to a landfill site within a given time window.
The CVRPTW problem can be modeled as a graph
G = " V, E ,, where V vertices are connected with E undirected edges. The vertices represent customer sites (smart
dumpsters) with demand d i (the waste level as communicated by sensor boards), and the edges represent the cost.
A fleet of M k | k ! " 0, f, m , vehicles is located in a depot
v 0, each having a capacity d k . The problem is thus
reduced to finding vehicle trips at the lowest cost in such
a way that each customer site is served exactly once by a
single vehicle, M k, while each trip is a loop starting from
the depot v 0, serving enough customers under the vehicle
capacity d k, unloading at a landfill, and ending at the
same depot v 0 .

Waste Collection Truck Agent
A truck (Figure 17) is modeled as an entity that is initially
at rest until it is scheduled for a trip by the central depot.



IEEE Electrification - September 2019

Table of Contents for the Digital Edition of IEEE Electrification - September 2019

Contents
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