WASTE TRUCK ROUTING AND SCHEDULING IN LOS BAÑOS LAGUNA
USING VEHICLE ROUTING HEURISTICS
A Research Proposal
In Partial Fulfillment for the Requirements in Eng 10 – Writing of Scientific Papers
Submitted by:
CARLO LUIS M. BATION
BS in Computer Science
Submitted to:
ERIC P. PALIGAT
Instructor
UNIVERSITY OF THE PHILIPPINES LOS BAÑOS
First Semester School Year 2011-2012
I.
INTRODUCTION
A. Significance of the Study
The study of waste management involves many fields. One of the main
aspects of waste management is waste truck routing and scheduling. It greatly
involves graph theory and route optimization on its background. We can say
that each stop represents a node and the ways to each node represents the
edges in graph theory. Solving the graph problem can lead to route
optimization. This leads to the main theme of the study which is to improve
scheduling and routing of waste truck in a given area using real world maps.
In the Philippines today, usually waste truck management is taken care
by government offices and usually by the city of the mayor office. Most don’t
have a system to schedule the routes of the waste trucks in their area. A
routing system is needed in order to minimize cost, manpower and time. It
minimizes cost by giving the shortest paths, the minimum way to traverse a
group of clients or nodes and this will minimize the usage of gasoline. It also
minimizes the possibility the truck to be damage because of the fact that the
truck will traverse the minimum route and travel for a short time period.
Manpower because we can come up with a solution of minimizing the number
of trucks then if the number trucks to be used are minimized, the number of
drivers and assistants will also be reduced. The use of time will also be
minimized when we consider the weight and type of waste in each node as a
parameter. When we know the estimated amount of waste per node the
number of going back of the trucks to the depot will be reduce so cost and
time here will be reduced.
In foreign countries there is a competition in waste collection. Many
companies like SA Waste Holdings, Greenstar and WSN Environmental
Solutions competes for customers. One of the factors that can be used as a
main weapon on other companies is to have a better way of routing in order to
traverse more nodes and to have minimum company expenses in the collection
system.
The study also uses the real world maps. So that if a road or something
was changed in the topography of the concerned area, the manager will just
again run the system and it will produce new results of routes and schedules
for the new map.
The map of Los Baños Laguna will be the first input in the system.
This map has interesting routes and has many ways to traverse a given node or
place meaning there are more than one way to go to a place. There is also one
depot or dump site in the map so the problem will be simplified. The types of
garbage in the map also vary. Because of the existence of hospitals in the map,
the hospital waste will be included in the constraints of types of wastes. The
map also includes the University of the Philippines Los Baños and inside the
university there is a building called Physical Science building that produces
toxic or chemical waste that is another type of waste. So, not just
biodegradable and non-biodegradable types of wastes will be the constraint of
the study.
The focus of this paper is to develop software that can return near
optimal routes and schedules for the waste truck to follow. The solutions will
be based from vehicle routing heuristics like branch and bound and traveling
salesman algorithms. The map of Los Baños Laguna will be the starting point
of the study and other maps will be chosen as input to train the software to be
generic and adapt in any kinds of map in the future.
B. Review of Related Literature
Waste truck routing and scheduling can be solve using vehicle routing
heuristics and other related concepts such the Traveling Problem. Heuristics
are those methods that can be program in polynomial time and give results that
are near to the optimal solution to the problem. Optimal solution can be
achieved in exponential time, meaning as the number of input increases the
amount of time also increases exponentially. Heuristics were developed in
order to have an acceptable solution to a problem. It runs in polynomial time,
meaning as the number of input increases the amount of time only increases
linearly. In this part of the paper related literature about waste truck routing
and scheduling in terms of combinatorial optimization will be presented.
The first is the Traveling Salesman Problem (TSP). It is based from the
1857 game called Icosian Game invented by Sir William R. Hamilton (J.
Dalgety, 2009). The rule of the game is to visit 20 connected points with going
to a point only once. We can relate traveling salesman to our problem by
making the number of trucks equal to one. It means that the single truck will
traverse all nodes or customers.
Branch and Bound algorithm can give a near optimal solution to TSP.
The idea of branch and bound algorithm is that you will make a tree. Find the
row minimum of a given matrix of constraints then it will be the starting node
of the tree. After finding the row minimum detect the indexes of the matrix
that are involve in the row minimum Then compute the different sub tours of
the remaining indexes of the matrix and again add the nodes to the parent and
repeat the process until an stopping criteria is detected. Branch and Bound
algorithm was first presented by A.H. Land and A.G. Doig in their book. The
book also contains other combinatorial problems.
Many papers were publish and presentations were made regarding to
the solution of TSP using the Branch and Bound Algorithm. The concept of
parallelization was also introduced to make the computation of nodes of the
tree in the algorithm (S. Tschoeke and et al, 1995). This is done by assigning
different task to each of the processors and because of this the result
generation will be fast as like doing the computation in a sequential process
with few data.
A presentation made by Busby, Dodge, Fleming and Negrusa about
Backtracking and Branch and Bound algorithm tells how the concept of
backtracking helps to know the solution to the TSP in a faster way by
implementing some methods related to backtracking. Remember that in
Branch and Bound algorithm sometimes if the cost of the current child node is
greater than the cost of the previous child nodes then it is needed to go back
one higher level in order to come with another better solution and concepts of
backtracking can help to find a better path. This is how the combination of
backtracking and branch and bound algorithm works: (1) Branch and bound
will create the tree of possible paths, (2) as it creates the tree a pointer will
check if the current node has the lowest cost (3) then if not a backtracking
algorithm will be used and the pointer to the current node will go to another
node which have the lowest cost until near optimal path or a stopping criteria
is found.
An implementation of the said algorithm written in a programming
language called java was made by Pawel Kalczynski in 2005. Pawel made a
package of classes in java based from the processes involved in the algorithm
and in TSP. Pawel based his package in the Branch and Bound Algorithm
made by Balas(1985).
As what we can see solving TSP using Branch and Bound Algorithm
requires too much computing power and takes a lot of time. So we need some
other methods that can solve TSP in a “serial” way or a simpler way. The
paper of Christian Nilsson presents many different heuristics about TSP
(2003). One of the methods that were presented was the nearest neighbour
algorithm. The idea of this method is to find the nearest accessible node then
go to it if it is not yet visited. In waste truck problem with the number of
trucks equal to one the starting point is the depot or the garage of the truck and
from the depot it will go to the nearest area assigned to it.
The next graph combinatorial problem related to the waste truck
routing and scheduling is the vehicle routing problem (VRP). Given number of
customers with each having certain demands (time and cost), each customer
have distances on each other and each have weights (garbage in terms of waste
collection). The goal of the VRP is to (1) minimize the number of vehicles to
be use or to optimally use the existing number of vehicles (2) to satisfy the
customers by getting the requirements on time and (3) to have a near optimal
path to traverse all the customers(Garn, 2003).
Vehicle routing problem with time windows (VRPTW) is a type of
VRP which added time as a constraint. The idea is that each node or customer
has “starting time” and “end time”. The “starting time” is the earliest time the
vehicle should arrive to the node and the “ending time” is the latest time the
vehicle should arrive to the node. Solomon on his paper on 1987 lists some
heuristics on how to come up with a solution to the VRPTW. One of the
heuristics that was presented on the paper is the time oriented nearest
neighbour algorithm. The algorithm was similar to the TSP’s nearest
neighbour algorithm the VRP version just added the number of vehicles and
the time windows as additional constraints. Another heuristic that was
presented by Solomon is the insertion heuristic. The goal of the heuristic is to
insert a new node that is not yet in the current partial path. The insertion of the
new node can be in any part of the partial path and the resulting partial path of
the insertion should be feasible.
In terms of waste truck routing not only time windows can be added as
a constraint but also the lunch break of the drivers also needs to be consider
(Byung-In and et al., 2005). Extension of Solomon’s insertion algorithm was
made so that a better time window handling will be done and to have an
optimal lunch break for the drivers.
Another constraint that can be added to the problem is the number of
available vehicles that already existed (Hoong and et al., 2003). The problem
here is that what if the number of vehicles available where not yet sufficient in
order to have the optimal path for waste collection so some customers will be
not served. Holding list is used to store customers that were not served in the
resulting path. Maximization of customers into the vehicles was made so that
all customers will be served. Tabu search was used to insert customers in a
vehicle schedule. Tabu search is an enhanced version of local search. It uses
data structures to store all visited candidate solutions to avoid repetition in
searching.
Evolutionary methods such as genetic algorithm and coevolutionary
genetic algorithm approach can be used in solving VRP (Penousal, 2002).
These methods can come up with better solutions than normal heuristics. Both
algorithms came from the concept of genetics in science mainly the theory of
evolution of Charles Darwin. The algorithm can be simply describe in this
steps: (1) generate possible solutions (2) evolve this solutions to new set of
solutions (3) loop until a stopping criteria is found.The solutions will evolve as
the given constraints changes. The coevolutionary genetic algorithm add some
heuristics, like nearest neighbour algorithm, to generate some of the possible
solutions while the simple genetic algorithm uses random possible solutions at
the starting phase of the genetic algorithm. So the result of the coevolutionary
genetic algorithm is nearer to the optimal solution than the result of the simple
genetic algorithm.
In this study we will use the data generated by Open Street Map
(http://www.openstreetmap.org/) an open source online mapping system. In
Open Street Map you can convert a given map into xml and download it. This
xml file have attributes like “lat” (latitude) and “long” (longitude) that can be
used into knowing the position of a place in the map this is from. In the
Philippines there are two websites that are using Open Street Map as their
mapping service for their users. Those are Ortigas Online and Red Cross Rizal
Chapter. The Wikipedia page(http://wiki.openstreetmap.org/) of Open Street
Map is the source for this data.
Traveling Salesman Problem algorithms are essential for path finding
and can help solving the problem with the number of trucks is equal to one.
Vehicle Routing Problem with Time Windows algorithms are essential for
routing with capacity and time as main constraints. The basics and some
applications of openstreetmap is essential in order to have a real world nodes
represented by the places in the given map. These concepts can help me
accomplish my objectives in this study.
C. Objectives of the Study
The main objective of this study is to make software that produces near
optimal routes and schedules for waste trucks given an input map from
openstreetmap.org.

To solve the problem using vehicle routing problem heuristics.

To make a graphical user interface from the map that can accept inputs
from user. Examples of inputs are name of the area, weight of garbage in
an area and time window for that area.

To produce routes lines in the given map and schedules as an output.
Routes lines are lines that connect one area from another area based from
the resulting path. An assignment colour for a truck will be given to
visualize the route of a truck in the map. For example for truck A colour
yellow will be given.

To extend the constraints into heterogeneous trucks. Meaning that a truck
can have different capacities and specs.

To start to the map of Los Baños Laguna as the first input to the software.
D. Date and Place of Study
The study will be done at the Institute of Computer Science, College of
Art and Science University of the Philippines Los Baños Laguna from August
2011 to January 2012.
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