Centre for Geo-Information
Thesis Report GIRS-2016-14
OPTIMIZING SUGAR CANE TRANSPORT
IN RWANDA
28 April 2016
Laura Wools
i
Optimizing Sugar Cane Transport in Rwanda
L.M.A. Wools
Registration number: 90 11 09 972 100
Supervisor:
Dr. Ir. S. de Bruin
A thesis submitted in partial fulfilment of the degree of Master of Science
at Wageningen University and Research Centre,
The Netherlands.
28 April 2016
Wageningen, The Netherlands
Thesis code number:
GRS-80436
Thesis Report:
GIRS-2016-14
Wageningen University and Research Centre
Laboratory of Geo-Information Science and Remote Sensing
ii
Abstract
The production of sugar in Rwanda is a growing sector, as up to 80% of the consumed sugar
in Rwanda has to be imported. The largest cost component of sugar production is the cane
transport. Transport costs can account up to 30% of the total production costs. The current
sugar transport in Rwanda is mainly done by trucks. Implementing a multimodal transport
network by using the road network and the river to transport the sugar cane can reduce the
costs of transport. The main objective of this research is optimization of sugar cane transport
in Rwanda by assessing different transport modes conditional to factory constraints and field
and network conditions.
Along the Nyabarongo river 42 landing and loading points were created. Within a distance of 1
km a landing or loading point can be reached from all sugar cane fields. From these 42
landing and loading points, the optimal routes to the sugar mill were calculated. The optimal
routes were computed as the least-costs paths using Dijkstra’s algorithm.
An average reduction of 40% of the current transport costs can be realised when
implementing transport over the river. These optimal routes from all 42 landing and loading
points uses the river and switch at the landing point near the Ruliba bridge to the road
network.
Further improving the efficiency of the total supply chain can be done by researching each
component of the supply chain in detail. Additionally, the costs of different transport modes
could be predicted more accurate when assessing different variables.
Keywords; Dijkstra’s algorithm, Kabuye Sugar Works, multimodal transport, network
analysis, least-cost path, R, sugar cane, transport optimization
iii
Table of Content
1
Introduction ..........................................................................................................1
1.1
Context and background ......................................................................................1
1.2
Problem definition ................................................................................................3
1.3
Research objective...............................................................................................4
2
Current situation ...................................................................................................5
2.1
Factory constraints ...............................................................................................5
2.2
Field conditions ....................................................................................................5
2.3
Network conditions ...............................................................................................8
3
Methods ............................................................................................................. 11
3.1
Conceptual Model .............................................................................................. 11
3.2
Loading points.................................................................................................... 11
3.2.1 Methods ............................................................................................................. 11
3.2.2 Implementation .................................................................................................. 12
3.2.3 Assign sugar cane fields to loading points .......................................................... 13
3.3
Optimal route to the factory ................................................................................ 14
3.3.1 Dijkstra’s algorithm ............................................................................................. 14
3.3.2 Pre-processing ................................................................................................... 15
3.3.3 Implementation .................................................................................................. 18
3.4
Economic feasibility............................................................................................ 19
3.4.1 Capital investment.............................................................................................. 19
3.4.2 Breakeven analysis ............................................................................................ 19
4
Results ............................................................................................................... 21
4.1
Location of loading points................................................................................... 21
4.2
Costs of different transport modes ..................................................................... 21
4.2.1 Transport by road ............................................................................................... 21
4.2.2 Transport by river ............................................................................................... 22
4.3
Optimal Route .................................................................................................... 23
4.3.1 Optimized route on the road network.................................................................. 23
4.3.2 Reduction of transport costs ............................................................................... 24
4.3.3 Efficient use of river transport ............................................................................. 26
4.4
5
Feasibility ........................................................................................................... 27
Discussion.......................................................................................................... 29
5.1
Current situation ................................................................................................. 29
5.2
Landing and loading points ................................................................................ 30
iv
5.3
Costs of transport network ................................................................................. 30
5.4
Optimal Route .................................................................................................... 31
5.5
Feasibility ........................................................................................................... 31
6
Conclusion ......................................................................................................... 32
6.1
Research objective............................................................................................. 32
6.1.1 What is the current situation of cane transportation in Rwanda? ........................ 32
6.1.2 Which method is suitable for this cane transportation optimization problem? .... 32
6.1.3 Which variables of the transport network can predict the costs of the road
network?........................................................................................................................ 32
6.1.4 Which conditions are important to be considered when implementing the results of
this research in KSW circumstances? .......................................................................... 32
6.2
7
Further research and recommendations ............................................................. 33
References......................................................................................................... 34
v
1 Introduction
1.1 Context and background
The price of sugar has been in a downward trend for the last couple of years. International
competiveness, low prices of commodity goods and increased interest in alternatives to sugar
put a lot of pressure on the sugar price (Higgins, 2006b, 2007). In the summer of 2015, the
price of sugar reached its lowest point since six and a half years (Durisin, 2015; Wernau,
2015). For producers the best option to maintain a profitable business is by reducing their
production costs (López-Milán, 2006). The largest cost component of sugar production is cane
transport (López-Milán, 2014). Transportation from the fields to collection sites and from
collection sites to the mill can be very costly due to several production conditions. Depending
on the case, transportation costs can account for about 25-30% of total production costs
(Higgins, 1999, López-Milán, 2014).
The costs of sugar cane transport are high due to the fact that there are some strict conditions
which have to be dealt with when processing sugar cane. The first and most important
constraint is that the mill needs enough supply to process 24 hours a day. This means that
during the day, a stock must be made for the night. This first constraint does not only concern
the transport component, since sugar cane also has to be harvested during the day. The
second condition concerns the quality of the cane. In order to achieve high sugar content, the
cane has to be harvested at maturity. The timing and length of the period of maturity depends
on crop cultivar and climatic conditions of the environment. For example, a hot climate and
large seasonal variations can lead to overmaturity of the crop. Overmaturity leads to loss of
sucrose and juice quality (Fauconnier, 1993; James, 2008). A loss of quality also occurs after
the cane is burned or cut. Therefore the cane has to be processed as soon as possible after
being harvested (Fauconnier, 1993; López-Milán, 2006). The speed at which the quality
decreases highly depends on environmental conditions; in warmer climates there is a bigger
rush to process the cane (Fauconnier, 1993).
These two constraints, the urge for constant milling and the cane quality, make the transport
of sugar cane a complicated but interesting topic. A lot of research has been done on this
topic and most of it has focussed on large sugar cane producing countries such as Australia,
Brazil, Cuba and South Africa (Higgins, 2003; Lejars, 2008; López-Milán, 2006). Research
focussed on the Australian sugar industry is targeted primarily on cooperation between
growers, haulers and the mills. There have been several projects that have led to a reduction
of transportation costs by applying modelling when scheduling road transport (Higgins, 2006a,
2006b). According to those studies, it is important to approach this challenge with a holistic
solution. The supply chain must be seen as a whole instead of focussing on individual parts of
the production chain (Higgins, 2003; Le Gal, 2009; Lejars 2008). Next to the technical aspects
of the supply chain, socio-economic features such as collective participation play an important
role in improving the supply chain (Le Gal, 2009; Higgins, 2007).
The possibilities of optimizing the supply chain depend on the structure of the sugar supply
chain. When the total supply chain is property of one company, the efficiency of the complete
process is the main goal. This is the case in for example Cuba, where the whole process is
owned by the government (López-Milán, 2006). However, when more farmers deliver to a mill,
sometimes even with a haulier company in between, there are different interests. In that way
1
the optimization process requires more managing, such as in Australia (Higgins, 2007). It also
matters whether the harvesting is done mechanically or manually. Mechanically cut cane
requires a different way of transportation than manually cut cane. The first are long stalks
which are transported in bundles, while the latter are cut into short sticks and is transported in
baskets. (Grunow, 2007).
The studies performed by Higgins (1999, 2006a, 2006b) and López-Milán (2014) showed that
transportation costs, in different contexts, can be reduced by using a technical solution.
However, every sugar cane company is different and has different aspects and constraints.
These companies all have the same goal; reducing transportation costs in order to compete
with the falling sugar prices.
In Rwanda, the sugar industry is purely meant for own consumption. The more sugar is
produced in the country, the less has to be imported. Currently, up to 80% of the sugar
consumed in Rwanda is imported (“Sugar: make it work”, 2014). The single player in the sugar
industry in Rwanda is Kabuye Sugar Works (KSW). The single producing factory of the
country is located near the capital Kigali (Figure 1). In 1998, the Madhvani Group acquired this
sugar factory from the government and the industry has been growing from that point. All
sugar cane fields are located near the Nyabarongo River. 400 employees are working in the
factory and around 5.000 employees on the fields. KSW leases approximately 3.700 ha of
land which can be used for cultivation (Kabuye Sugar Works, Rwanda, 2015). However,
currently they can only use 1.700 ha because of regular flooding. Another approximately
3.100 ha is leased by independent out growers, farmers who sell their yield to the factory
(Royal Haskoning, 2015).
Figure 1: Location of the KSW Factory, the landing points and the sugar cane fields
2
Because of heavy flooding and the subsequent low yield, 60% of the processed sugar cane at
the KSW factory originated from out growers in 2008. Unfortunately this still was not enough to
keep the factory going 24 hours a day and the mills had to be stopped for some time. Soon
they picked up again at 12 hours a day (Mukaaya, 2008). In 2011 80% of the sugar cane
fields flooded again which led to unaffordable sugar prices for the Rwandan population (Royal
Haskoning, 2015). In 2013, the project ‘Sugar: make it work’ started with as main goal;
drainage of sugar cane fields to remove water in case of floods as efficiently as possible.
Because of the flood and the importance of the sugar cane industry for Rwanda, the focus of
the project is mainly on reducing the effects of floods. However, an optimization of cane
transport can potentially bring a major decrease in production costs, which is a great
opportunity for the sugar industry in Rwanda.
1.2 Problem definition
Within the project ‘Sugar: make it work’, Milan Innovincy and the WUR work on both crop
development monitoring and information to support sugar cane logistics. This research intents
to investigate the transport component of the sugar cane value chain. More efficient transport
would contribute to reducing sugar production costs in Rwanda. When the components of the
supply chain have been researched, the production process can be optimized by scheduling
the harvest and crop cycling in line with factory or even market demands.
The climate conditions in Rwanda are of major influence on the sugar cane production
(Fauconnier, 1993). The climate influences the crop itself; also cultivar, the crop cycle and the
harvest cycle are all a function of the environment. The weather in combination with the
geographic location of the fields is also influencing transportation. The sugar cane fields are
located close to the Nyabarongo River (Figure 1). This river is prone to flooding and the roads
connecting the fields with the factory are therefore not always transitable. Furthermore, the
road network is in suboptimal conditions. The fields are hard to reach, even under normal
conditions. The areas on the west and south side of the river are the hardest to access, since
the road network is less dense on those sides (Figure 1). In addition there are only few
options to cross the river. However, transport over water seems to provide a feasible option
that has hardly been explored to date. Most of the sugar cane fields are located close to the
river, so using the water network could be a good alternative.
Three main issues that transport has to deal with are: factory constraints, field conditions and
network conditions. The first factory constraint is the demand of the factory. The mill needs
enough cane so they can process 24 hours a day during the harvesting season. Storing
capacity and circumstances have to be kept in mind. For instance, storage over longer periods
(24 hours), affects the quality of the sugar cane juice. Next, the transport is subject to the field
conditions. The cane must be mature in order to achieve highest recovery rates. In a climate
like Rwanda, the cane is not easily overmature, which means that the harvest period can be
spread over a longer period (Fauconnier, 1993). This is very convenient for planning
purposes. The exact field conditions are of major importance; crop age, crop cultivar, area and
the status of the crop are only some examples of essential information. Last the transport
network conditions have an important influence. The condition of the road network partly
depends on the weather conditions and the river is also subject to weather circumstances.
The purpose of this research is to develop and implement a method for assessing river and
road transport options and deciding about the most efficient transport for fields delivering to
the KSW mill.
3
1.3 Research objective
The main objective of this research is optimization of sugar cane transport in Rwanda by
assessing different transport modes conditional to factory constraints and field and network
conditions.
This will be achieved by answering the following research questions:
Q 1:
What is the current situation and what are the;

factory constraints

field conditions

network conditions
to be considered in optimization of cane transportation in Rwanda?
Q 2:
Which optimization of network method is suitable for this cane transportation
optimization problem?
Q 3:
Which variables of the transport network can be used for predicting the costs of
transport over the road network?
Q 4:
Which practical conditions are important to be considered for implementing the results
of this research in KSW circumstances?
4
2 Current situation
In order to explore the possibilities of the optimization of the cane transport, a detailed
overview of the current situation is necessary. Based on data provided by KSW and Royal
Haskoning DHV (RHDHV), and a visit to Kigali, the three main factors affecting the transport
optimization problem are described in this chapter.
2.1 Factory constraints
The operational period of the factory is 9 to 10 months every year. The heavy rain season in
the months April and May make it impossible to keep the harvesting, transportation and
factory going. Besides those two months, the factory is occasionally shut down for some days
for example during heavy rainfall outside the main rain season and during public holidays.
Otherwise, the factory operates twenty-four hours a day, seven days of the week. During the
operational period the rate of sugar cane processing is virtually constant (M. Thiru, personal
communication, December 8-12, 2015).
Ideally, the factory would run at full capacity, which requires a constant supply of cane each
day. The KSW factory can process 600 tonnes of sugar cane per day (S. Jayakumar, personal
communication, November 10, 2015). Currently, most of the sugar cane is transported from
the field to the factory by trucks. The capacity of these trucks varies from 5 to 12 tonnes. With
an average capacity of 8 tonnes, this implies that over 60 times a day, a truck brings sugar
cane from the field to the factory (KSW, 2015b).
Planning of cultivation, harvesting and transportation is managed at the factory in Kigali.
Harvesting is scheduled based on crop age, optimal harvesting period and mill demand.
Transport is scheduled, based on the actual harvest (S. Jayakumar, personal communication,
December 8-12, 2015).
2.2 Field conditions
The sugar cane fields are located in the marshlands of the Nyabarongo river. Due to this
location, the area is prone to flooding (Royal Haskoning DHV, 2015). Since sugar cane is not
thriving well in the swampy lands along the river, the groundwater management in this area is
to be improved. Rehabilitation of former drainage routes and old river courses makes it
possible to drain the flood plains much faster. The improved water management infrastructure
will eventually account for the reclamation of an area of 1.500 ha. Currently, a third of this area
is reclaimed of which 82% is in use as sugar cane plantation (“Kabuye Sugar award winning”,
2015; “The Sugar: Make it Work Project”, 2016).
Figure 2 shows the difference of the area close to the Ruliba Bridge, near the city centre of
Kigali. The area has changed from papyrus to sugar cane between July of 2014 and
September 2015. The location of this site is indicated in Figure 3.
5
Figure 2: Aerial imagery of an area near de Ruliba bridge with mainly papyrus in July 2014 (left) and sugar
cane cultivation in preparation in September 2015 (right). Source: Google Earth.
Currently, KSW has access to 3773 ha of land on which sugar cane is cultivated. This area is
divided in the fields leased by KSW and fields cultivated by out growers (Figure 3).
Figure 3: Sugar cane fields cultivated by KSW and out growers
Close to 65% of the 3773 ha is leased and cultivated by out growers (KSW, 2015a).
6
Harvesting these sugar cane fields is the responsibility of the out growers. Transport of the
sugar cane from those fields to the factory is arranged by KSW. The other 36% of the sugar
cane fields is leased by KSW and the people working on those lands are employed by KSW.
KSW can manage the workforce on these fields more flexibly if necessary. For example, when
due to circumstances such as flooding, the sugar cane fields have to be harvested as soon as
possible or fields cannot be harvested at all.
The crop variety, crop cycle and crop age for the KSW fields is documented, which makes it
possible to time the harvesting of each field at the most optimal moment. Crop information is a
key factor in efficient chain supply management. The sugar cane fields are divided in circles,
compartments and zones. A zone consists out of several sugar cane plots or fields. Every
circle consists out of several zones and the compartments can consist out of multiple parts of
different zones and circles. The data is based on the subdivision in circles and zones, but in
practice, the division in compartments is mostly being used.
The crop cycle varies between plant and different ages of ratoon. Due to the climate in
Rwanda, planted sugar cane takes around 21 months to mature. Ratoon crops take between
16 and 18 months to mature (S. Jayakumar, personal communication, December 8-12, 2015).
Ratoon crops are grown out of stubbles of the previous crop. This is a lucrative way of growing
sugar cane, since the investments have to be done for another crop cycle are much lower
than those of replanting. Ratoon crops yields slightly less sugar cane per hectare compared to
planted sugar cane (James, 2008).
With the combination of crop variety, crop cycle and the crop age, the expected month of
harvest can be estimated. Figure 4 presents the month and year of harvest for the sugar cane
fields of which the information is complete. Comparison of Figure 3 and Figure 4 show that the
information of the sugar cane fields cultivated by the out growers is not complete.
Furthermore, the crop data is outdated. Figure 4 shows fields of which the date of harvest has
already passed, which means that either the fields are empty, or more likely, new sugar cane
is planted or a ratoon crop is grown on these sites.
7
Figure 4: Expected month of harvest of sugar cane fields
In general, each hectare of sugar cane is claimed to yield 100 tonnes of fresh cane stalks (S.
Jayakumar, personal communication, December 8-12, 2015). For the area which is in use at
this moment, this means that the area would yield approximately 3.77 × 105 tonnes of sugar
cane. With an average growing season of 18 months, this means that each year, about 2.52 ×
105 tonnes can be harvested. Such productivity exceeds the capacity of the mill and therefore
KSW plans to expand the factory (S. Jayakumar, personal communication, December 8-12,
2015).
2.3 Network conditions
The transport network is divided into the road network and the river. Currently, the road
network is being used most frequently. The roads around Kigali can be divided into tarred and
very well accessible roads and untarred or dirt roads on which driving is more difficult and
driving speed is limited. About 50% of the route the trucks travel is untarred. These untarred
roads are mainly located close to the fields and with that close to the river (Figure 5).
Accordingly, those roads are prone to flooding. A flooded road is even harder to traverse and
after flooding the road condition is worsened. Keeping the untarred roads in the floodplains in
good condition is costly and is not considered a main priority of KSW (S. Jayakumar, personal
communication, December 8-12, 2015).
8
The period between harvesting and milling the sugar cane is at most 72 hours. Bringing the
maximum time back to 36 hours increases the quality of sugar with 10% (S. Jayakumar,
personal communication, December 8-12, 2015). Transportation is mainly done by truckers
who aren’t employed by KSW. The cost of transport is based on the length of the route the
trucks have to cover and the weight of the sugar cane which is being transported. In the year
2015, a total amount of 592.319.822,- RWF is spend on the transport, which an average of
5393,- RWF per metric ton of sugar cane (KSW, 2015c). KSW itself owns seven trucks which
are mainly used to pick up the sugar cane from the most difficult to reach places (S.
Jayakumar, personal communication, December 8-12, 2015).
Figure 5: Condition of the road network around the sugar cane fields and the KSW factory
The second part of the transport network is the river Nyabarongo. The river flows through the
sugar cane area over a length of 85 km. The river is 30 to 50 meters wide and the river depth
varies from 1,5 meter in the bends in the dry season to up to 6 meters in the middle of the
river during the rainy season. Half way the sugar cane area, the river confluences with the
Akanyaru river. The Nyabarongo river is rain-fed and therefore a dynamic river which erodes
mostly in the outer bends. Downstream, the river is eroding less, due to the confluence with
the Akanyaru river, which is a perennial river. Although the bends of the river are eroding over
the years, the straight sections of the river have remained stable over the last years (de Boer,
2016).
9
Part of the sugar cane fields is not accessible by road or located at the south side of the river.
Currently, 35% of the sugar cane has to cross the river. The harvested sugar cane of these
fields is transported over small distances with boats across and over the river. This small scale
river transport is arranged by local out growers. These boats carry on average 4 tonnes of
sugar cane per trip and they are mostly moved by manpower. A trip of 12 km down the river
takes about 4 hours and the return journey, which is upstream, takes some 7 hours on
average (S. Jayakumar, personal communication, December 8-12, 2015). In order to fill one
truck, two or three boats trips are necessary. In this way transportation over the river is very
time consuming. Furthermore, the current of the river and the fact that most of the boats don’t
have an engine make these river trips very dangerous. In the past several fatal accidents have
occurred (Bucyensenge, 2015).
10
3 Methods
Transportation of the sugar cane from the fields to the factory is done in multiple phases. First,
harvested sugar cane is stacked at loading points. From those loading points, the sugar cane
is transported by truck to the factory. When the sugar cane fields aren’t accessible by road,
the cane is transported over the river by boats and loaded onto the trucks at landing points.
Currently, only when the transport is almost impossible by truck, the river is being used. This
research assesses the possibility of transport over the river in order to reduce the total
transport costs.
3.1 Conceptual Model
This optimization problem is presented in the conceptual model of Figure 6. Harvested sugar
cane is stacked at the assigned loading point. At the loading point, the cane is loaded on the
barges and transported over the river to the most optimal landing point. At the landing point
the cane is loaded on trucks and transported to the KSW factory.
Figure 6: The conceptual model illustrates the two options transport by truck over road or a combination of
barge and truck to the factory
The conceptual model indicates two factors; the location of the loading points and finding the
least-cost route from loading point to the factory. Lastly, the economic feasibility of
implementing river transport is calculated.
3.2 Loading points
3.2.1
Methods
The location of a suitable loading point is determined by several factors. First, a loading point
should be within one km walk of the fields it serves. Harvested sugar cane is transported by
foot to the loading points, therefore one km is the maximum distance (S. Jayakumar, personal
communication, December 8-12, 2015). Currently, loading points have to be close to a road,
since the cane is transported by trucks. The new loading points have to be on the river banks
and if possible near a road.
The second important factor in assigning locations of the loading points is the river. Due to the
dynamic character of the river, the river course tends to change over the years. The bends in
the river are the most dynamic parts of the river, which means that the straight sections of the
river are most suitable as location for the loading points. The future meandering of the river is
hard to predict, but satellite imagery of previous years shows the dynamics and the stable
sections of the river.
11
The barges that will be operating on the river will be around 30 metres long, which makes a
loading point in a river bend close to impossible. Furthermore, the depth of the river has to be
deep enough at the river banks at the locations of the loading points during the harvest
season. Therefore, the parts of the river which are suitable as a location of a loading point,
have to be chosen at straight sections of at least 50 metres long and the minimum water
depth needs to be 2 metres.
Detailed information of the river is not yet available and will be researched in the future. Due to
the specific requirements of the river, it is hard to automate the process of finding the right
location of the loading points. Based on satellite images, the current course of the river and
the range of the loading points, the loading points were assigned manually.
Since landing points are also deemed to serve as loading points, there are no new loading
points within 1 km of landing points.
3.2.2
Implementation
The data used for finding the locations of the landing and loading points are presented in
Table 1. A shapefile with the geometry of the river was also available, but it appeared to be
outdated. For that reason, the section of the river that flows through the sugar cane fields was
manually digitized based on satellite images and the geometry of the sugar cane fields.
Table 1: Used data for locating loading points
File
Source
Shapefiles Sugar Cane Fields
KSW
Barge landing map (PDF)
KSW
Satellite imagery
Google Earth
The locations of the landing points derived from the barge landing map were digitized as
landing points. These locations were visited during the field work and were found to be
feasible. These locations are easily accessible and there is enough space to develop these
sites as landing points.
The loading points were newly created. The first reason for this was that the current loading
points are only located near fields that aren’t or very hard to access by road. The optimal
location of loading points is likely to be different when all fields need access to a loading point.
Furthermore, the current loading points are designed to handle boats with a length of a mere
7m. In the future the length of the barge will be around 30m, which makes some locations
unsuitable in the future.
Both the fields on the north and the south side of the river needed loading points within 1 km
distance from fields. Therefore, a division was made between the sugar cane fields located
north of the river and the fields located south of the river. The landing points are easy
accessible from both sides of the river since they are close to a bridge.
Creation of loading points started at each of the four landing points. In order to indicate the
range of all landing points, a buffer of 1 km was created around the four landing points.
Locating the first loading point started at the landing point 4 (Figure 1). The location of the first
loading point upstream from the landing point was derived by the steps in Figure 7. First the
centroid of each field was computed. By visual assessment, the centroid closest to the buffer
of the landing point and the most far away from the river is selected (2a). A buffer of 1 km was
12
created around the selected centroid (3a). Close to the intersection of the sugar cane fieldcentriod buffer and the river, based on the requirements of a loading point, a suitable location
for the first loading point was chosen (4a). The next loading point was determined by creating
a 1 km buffer around this first loading point (5a) and repeating the previous steps (2b-4c). This
procedure is followed for each sugar cane field of both sides of the river.
Figure 7: Creation of new loading points along the river
3.2.3
Assign sugar cane fields to loading points
Data of the sugar cane fields was provided by KSW in different formats. The geometry of the
fields was presented in separate shapefiles for each zone. Data such as crop age, crop type
and owner of the land were available in excel files. Based on overlapping attributes these data
was combined in to one dataset. Additional attributes, such as expected month of harvest
were calculated from these data.
The fields were assigned to a loading point based on Euclidean distance, i.e. the fields were
assigned to the nearest loading point at the same side of the river. Landing points were
assumed to serve as loading points for fields within 1 km distance.
Next to river side and the distance from the sugar cane fields to the loading points other
factors influence the optimal loading point for each field. For example, crop condition can be a
key factor in the accessibility of the landing and loading points. Passing recently harvested
fields is different from walking to a field with 18 months old sugar cane. Unfortunately,
13
because dynamic and detailed information were not available of all fields, it was not possible
to use this information.
3.3 Optimal route to the factory
The next step of this optimization problem is calculating the optimal route from each landing
and loading point to the factory.
3.3.1
Dijkstra’s algorithm
The optimal route is calculated by a least cost path algorithm based on the costs of transport.
Of all algorithms used to compute the least cost path, the classical Dijkstra’s algorithm is used
the most (Yu, 2003). This algorithm is widely implemented in several route planning
applications (Delling, 2009). When computing the least-cost path in a road network, Dijkstra’s
algorithm performs the calculations the fastest (Deyfrus, 1969).
Dijkstra’s algorithm was developed by E. Dijkstra and published in 1959 (Dijkstra, 1959). The
second problem addressed in his paper ‘A Note on Two Problems in Connexion with Graphs’
is finding the path of minimum total length between two given nodes. Input of this algorithm is
a graph consisting out of nodes and edges with a weight attribute. The algorithm starts at the
source node of which the cost is set to 0. First the neighbouring node of which the edge has
the smallest value, i.e. the least-cost path, is visited. The weight of this edge is assigned to
this particular node. Next the neighbouring node of which the edge has the second lowest
value is visited. When all neighbouring nodes are visited, the algorithm starts again at the
node with the lowest value and visits the neighbouring node with the lowest edge weight. If
this node has already been visited and the weight assigned to this node is lower than the total
weight of the edges from the source node, the weight of the node stays unchanged. However,
if the total weight of the edges from the source node is lower compared to the weight of the
node, this new weight is assigned to the node. This procedure continues until the destination
node is reached and the total weight of the edge from the source node is smaller or equal to
the total weights of any other node. The nodes which are part of the path of minimum total
length are listed as the least-cost path. (Dijkstra, 1959). The result is the least-cost path from
the source node to the destination.
Several studies show that Dijkstra’s algorithm performs the best when computing the leastcost path with a network with nonnegative costs (Cherkassky, 1996; Dreyfus, 1969; LaValle,
2006; Zhan, 1998). This algorithm is especially suited in situation where a one-to-one leastcost path has to be computed (Zhan, 1998). This is mainly due to the fact that the algorithm
can stop when the destination is reached without visiting all nodes in a network. For large
networks and relative close proximity of the source and destination node, more efficient
methods exist (Zhan, 1998). However, Dijkstra’s algorithm was deemed suitable for the
relatively small network and well separated nodes used in this thesis research.
Dijkstra’s algorithm is widely implemented in programming languages like Python and R,
which make it very accessible to use. For both Python and R, the package igraph provides an
implementation of Dijkstra’s Algorithm. Next to Dijkstra’s algorithm, also Johnson’s algorithm
and the Bellman-Ford algorithm can be used when calculating the shortest path (Csardi,
2006). The Bellman-Ford algorithm and Johnson’s algorithm only out-perform Dijkstra’s
algorithm when the graph has negative edge weights, and therefore Dijkstra’s algorithm was
chosen to compute the optimal path to the factory.
14
3.3.2
Pre-processing
In order to perform the least-cost path analysis, a transport network including weights is
needed. The computation was done twice. The first time the current situation was simulated
and the transport network consisted of the road network provided by KSW and the roads from
the new loading points to the current road network, which were added based on aerial
imagery. The second time the least-cost path analysis was conducted, it was based on the
total transport network including the river. This second run calculated the optimal routes from
each loading point to the factory.
The aim of this research is to find to optimal routes, therefore the weight on which the leastcost path analysis was done were the costs of transport. The costs of each network segment
were calculated. These costs are the main component of the total transport costs. The costs of
the road segments were derived from the field visit and data provided by KSW and the costs
of river transport were derived from RHDHV (Boer, 2016).
The analysis was done from each loading point to the KSW factory, this means that the costs
of collecting the sugar cane at the loading points were not included in this analysis. These
costs differ between the two transport modes, because the sugar cane transported by barge
has to be collected at one of the loading points at the river bank and the sugar cane
transported by truck can be loaded in the truck next to the field. Although those costs are
supposed to be higher when using the river transport, these differences are deemed to be
negligible when comparing total costs.
Transhipment costs can be implemented when modelling a multimodal network by assigning
labels to each edge (Delling, 2009). Since the transhipment component is such a small
component of the total transport costs this is also deemed negligible. Furthermore, in this
particular case, the transhipment costs are unknown. Therefore, the costs of transhipment
between the transport modes are not included.
3.3.2.1 Road transport
The road network is provided by KSW and originates from the Rwanda Natural Resources
Authority. The road network part of the attributes of this data is the condition of the roads. The
costs of the road segments were calculated based on the current costs of transport provided
by KSW (KSW, 2015c). The slope of the road segments was obtained for the total road
network. The relation between the two variables, the condition and the slope of the road
segments, and the costs of the road network was being researched using regression analysis.
The costs assigned to each network segment are done based on GPS-tracks measured
during the field work. At the factory a GPS-tracker was placed on the trucks which saved a
waypoint every 30 seconds. The trucks tracked with the GPS-trackers were selected based on
the location of their destination. Ten different circles and compartments were visited during the
fieldwork (Table 2). Ten trucks were tracked of which 8 GPS-tracks could be used for the
analysis.
KSW provided data on the costs of transport of the year 2015 in Rwandese Francs (RWF) per
metric ton (MT) sugar cane (KSW, 2015c). Costs of the transport from the Ntarama landing
point and from Aja Paraya weren’t available, since those fields had not been harvested in
2015 (Table 2).
15
Table 2: Starting point and costs of GPS-tracked routes in Rwandese Francs per metric ton
Destination
DR2
5
x
Costs per MT
in RWF
7245
Rwesero 1
DB
3
GPS Failure
4772
Mwendo 3
DB
3
x
4082
Zone 24
BCR
2
x
3852
Ruramba 4
DR2
4
Accident
6382
CS
14
x
3565
EF2
6
x
6440
Ntarama
NA
7
x
NA
Burema
EF2
9
x
5807
NA
NA
x
NA
Rugalika 2B
Nzove
Nyarubande 2
Aja Paraya
Circle
Compartment
Results Measurement
The GPS-data consists out waypoints measured every 30 seconds of the tracks. Each track
was divided in the outward journey and the return. Of every outward and return journey line
segments were created between the GPS-points. The costs of the track divided by the number
of segments of that track was assigned to each segment of 30 seconds. Based on that, the
cost per km of each segment was calculated. This resulted in an overview of the more and the
less
expensive
parts
of
the
route
(
Figure 8).
The map shows that the road segments close to the river are more costly to travel compared
to the roads towards the city centre of Kigali and the KSW factory.
16
Figure 8: Costs per road segments in RWF of GPS-tracks
To extrapolate these costs to the total road network a function based on two variables was
developed. The first variable which is likely to influence the costs of the road transport is the
surface of the road. The tarred roads are very well accessible. The untarred roads on the
other hand are very hard to access. Details about the road network surface are included in the
provided road network and by visual assessment of Google Earth, the surface of the routes
tracked by GPS is assigned to those tracks as an attribute.
The second variable which is likely to influence the costs of the road transport is the slope of
the road. Roads with a very steep slope are harder and therefore costlier to travel than a
gradual or no slope at all. Based on the Digital Elevation Model (SwedeSurvey, 2010) of the
area the slope of each segment is calculated (Figure 9). The location of the roads is
influenced by the slope of the area, the height differences over the roads is low. The R script
of this computation can be found in appendix A.
17
Figure 9: The slope of the road segments in % on the DEM of the area
The relationship between the two variables and the costs of the road transport is tested in a
linear regression analysis. The regression equations were used to calculate the costs of the
total road network.
3.3.2.2 River transport
The costs of transport over the river are derived from the pre-feasibility study by RHDHV
(Boer, 2016). The pre-feasibility study is divided in two phases. The first phase is based on the
current capacity of the sugar cane fields. The second phase is an estimation based on further
development of the sugar cane area and therefore further expansion of the barge system
(Boer, 2016). Therefore, the costs of phase 1 are used in this research, since these concern
the current situation. An estimation of the operational costs of the barges of one year is
included in their report and presented in Table 3 (Boer, 2016).
Table 3: Yearly operational costs of river transport
Operational Costs (rounded on USD 10.000,-)
in USD
Staffing
150.000
Fuel and power
100.000
Maintenance
50.000
18
Total operational costs
300.000
The estimation of the operational costs is based on several assumptions. Firstly, there are 3
barges with a capacity of 112 MT. The calculations are done assumed that the barges are
loaded to their full capacity at any time. The second assumption is that the 3 barges in total,
can cover a distance of 80 km per day. Furthermore, the barge will only operate in harvest
season, a year was estimated at 286 operating days. The estimated costs of transporting a
metric ton sugar cane per km by barge is 29,13 Rwandese Francs (Table 4).
Table 4: Operational costs of river transport
Operational costs river transport
in RWF
Per Year
21.847.500
Per Day
7.645.860
Per Metric Ton
Per MT/km
2.272
29
With this average costs per km, the costs of each river segment was calculated and added as
an attribute to the transport network.
3.3.3
Implementation
The shortest path analysis was done on two transport networks. First, a simulation of the
current situation only assessed the road network. The optimal route when including the river
as transport possibility is calculated after this. The costs of transport of the different transport
network segment are added as an attribute to the shapefile and used as weight for the
shortest path analysis.
The implementation of the shortest path analysis was done in R using the packages
shp2graph (Lu, 2014) and igraph (Csardi, 2006). The full script of the analysis can be found in
appendix B. In order to conduct the shortest path analyses, the format of the network had to
be converted from a SpatialLinesDataFrame to a graph. With the function readshpnw from the
package shp2graph the SpatialLinesDataFrame is split in an edgelist, a nodelist and an
attribute table of the edgelist. The function nel2igraph produces an igraph out of the edgelist,
the node list and the column of the attribute table which contains the cost per segment (Lu,
2014). The result is an igraph of the transport network with the costs of each segment as
weight of the graph. Both the transport network containing the roads and the transport network
including both the roads and the river were converted to graphs.
For both networks, the least-cost path was calculated with the function get.shortest.paths from
the package igraph (Csardi, 2006). This function calculates the shortest path from the graph to
perform the analysis on, the edgeID of the starting point of the path to be calculated, the
edgeID of the destination of the path, and the weight on which the analyses in being done and
the algorithm used in this function. The edgeIDs of the starting points are the edgeIDs of the
landing and loading points (appendix C). The edgeID of the destination is the edgeID of the
KSW factory. When creating the graph, the cost of each segment is set as the weight edge
attribute.
3.4 Economic feasibility
Implementing the river barges in the current transport system needs capital investment.
Calculations of the reduction of transport costs in the optimal routes analysis were based on
19
the operational costs of the river transport. The capital investment was not taken into account
in this analysis. Therefore, based on an estimation of the yearly possible reduction the
breakeven point of the yearly reduction and the capital investments is calculated.
3.4.1
Capital investment
The capital investment is an estimation derived from the pre-feasibility study of RHDHV. Due
to uncertainties in this stage of the project, a lower and an upper value of the investment costs
of the project are given (Table 5). The investments costs consist of the construction of the
barges including a gantry crane and the construction of landing points (Boer, 2016).
Table 5: Capital investments of river transport
Phase
Capital investment
Phase 1, lower value
1.490.000 USD
Phase 1, upper value
2.460.000 USD
3.4.2
Breakeven analysis
Based on the estimation that on average, the sugar cane can be harvested after 18 months
and the average production of one hectare sugar cane is 100 tonnes, the yearly transport
costs are calculated.
Of all the sugar cane fields, the average production and the estimate of yearly production is
calculated (Figure 10). For both the current transport costs as the costs of the optimal routes,
the transport costs for each field are calculated. The total amount spend on transport in the
current situation is compared to the total costs when using the optimal routes. This results in
an estimate of the possible yearly reduction of transport costs.
Figure 10: Calculating yearly reduction of transport costs
The breakeven point was calculated by dividing the capital investments (Table 5) of the river
transport system by the yearly reduction of transport costs.
20
4 Results
4.1 Location of loading points
In total 42 points along the 85 km that the river flows through the sugar cane fields were
created. The four landing points, near the Ruliba Bridge, the Confluence with the Akanyaru
river, at the Kanzenze Bridge and downstream at the beginning of Phase 2 were copied from
the barge landing map (River Transportation, 2015). Both at the north and the south bank of
the river 19 loading points are created (Figure 11).
Figure 11: Location of landing and loading points
4.2 Costs of different transport modes
4.2.1
Transport by road
The relationship between the costs and slope is tested in a linear regression analysis. The
tarred and untarred road segments are separated from each other. From both the scatterplots
it is hard to derive a clear relationship between the costs and the slope of the trucks (Figure
12).
21
Figure 12: Scatterplot of the relation between the slope in % and the costs per km in RWF for tarred and
untarred roads
The statistical results of the regression analysis are presented in Table 6. Both the data on the
tarred road segments as the data on the untarred road segments show a negative relation
between the costs of the road segments and the slope. The standard error of the coefficient
shows the average difference between the predicted value by the estimate of the coefficient
and the actual data. The adjusted R² shows the percentage of the costs that can be explained
by the slope of the segments. Less than 1% percent of the costs can be explained by the
slope of the road segments.
Table 6: Results of regression analysis
Intercept
Coefficient
Std. error coefficient
Adjusted R²
Tarred
176,217
-4,554
1,834
0,013
Untarred
428,506
-0,979
1,906
-0,001
The results of this statistical analysis show that the slope is cannot be used as a variable
when predicting the costs of the road segments. Firstly, the relation between the slope and the
costs was expected to be positive. The cost of road transport was expected to increase when
the slope of the road increased. In contrast to the expectation, the regression shows a
negative relation between the slope and the costs. Furthermore, the adjusted R² shows that at
most 1% of the costs can be explained by the slope. Therefore, the costs of the road segment
are calculated based on the mean costs of the surface of the road segments (Table 7).
Table 7: Costs of road transport per km in RWF
Costs per KM in RWF
Tarred
Untarred
4.2.2
85,5
215,5
Transport by river
The costs of transport by river were derived from the feasibility study of RHDHV. The river is
separated in different segments between the different landing and loading points and to all
segments the same costs per km is assigned. Table 4 presents the costs per metric ton per
km when the sugar cane is transported over the river.
22
4.3 Optimal Route
4.3.1
Optimized route on the road network
The GPS-tracks and the optimized route on the road network of these routes are presented in
Figure 13. For these 6 starting points, the optimized route is almost similar to the GPS-tracks.
Only at Rugalika 2B, the first part of the route is different and the trucks coming from
Nyarubande and Burema follow a slightly different route close to the city centre of Kigali. The
differences of the first part of the route of Rugalika 2B is due to the fact that this part is
manually added to the road network based on aerial imagery and the route the GPS-tracks
shows is not included in the road network. The different routes in the city centre of the routes
coming from Nyarubande and Burema could be explained by the fact that these are very well
maintained untarred roads in the centre of Kigali and the optimized route is following the
tarred roads.
Figure 13: Difference between current route and the optimized route on road network.
The costs and the length of the current tracks and the optimized route are presented in Table
8. The costs and length of the current tracks are originating from the data provided by KSW.
Five of the six simulated tracks have a higher costs compared to the current situation.
Comparison of the costs of the currents routes from the 42 landing and loading points to the
simulated routes shows that on average the simulated costs are 11% higher than the original
costs. Although the lengths of the tracks do not differ much on the map, the numbers in Table
8 show some significance difference.
23
Table 8: Difference in costs and length between current and optimized route on the road network
Name
Current
Costs in RWF
1782,50
Current
Length in KM
15,00
Optimized
Costs in RWF
2186,02
Optimized
Length in KM
16,10
Costs current optimized
-403,52
Zone 24
1926,00
15,55
2071,65
17,93
-145,65
-7,56
Mwendo 3
2041,00
17,18
2235,54
18,11
-194,54
-9,53
Rugalika 2B
3680,00
59,00
4849,37
33,12
-1169,37
-31,78
Nyarubande 2
3220,00
39,00
3996,28
34,72
-776,28
-24,11
Burema
2990,00
29,00
2481,96
28,47
508,04
16,99
Nzove
4.3.2
in %
-22,64
Reduction of transport costs
The optimal routes from all the 42 landing and loading points are presented in Figure 14. The
most striking result from the shortest path analysis based on the costs of the road segment is
that the optimal route from each landing and loading point goes over the river to the landing
point near the Ruliba Bridge. From that point the cane is transported over the road since the
KSW factory is not located along a navigable river.
Figure 14: Optimal routes on road and total network from all landing and loading points to the factory
The costs of the calculated optimal routes compared to the current transport costs are on
average reduced by 40% of the current costs. Comparing the costs of transport calculated by
the least-cost path analysis of the total network with the least-cost paths of the road network,
24
the costs can be reduced by 45% on average. The 5 routes with the highest possibility of
reduction are presented in Figure 15. The percentage showed in the map is the percentage of
the current transport costs which can be reduced when using the optimal route. For this top 5,
the costs could be reduced with more than 50% of the current transport costs. The top 3
originates from loading points on the south side of the river.
Figure 15: Optimal routes and reduction of current routes in %
Table 9 shows the costs of the current transport, the optimal route by road, the optimal route
using the complete transport network and the reduction of the optimal route compared to the
current situation in percentages. The costs of the optimized routes of the road network are on
average an overestimation of the current transport costs. This also results in higher reduction
rates when comparing the costs of the optimal routes. Therefore the reduction of the optimal
routes compared to the current transport is presented in the last two columns. The top 5 of
routes on which the most can be reduced is therefore based on the last column.
25
Table 9: Costs of transport routes in RWF
Name
Loading Point
Costs
Current
New 12
Zone Ruramba
New 10
Zone Mwongo
Nyarubande 4
Zone Nyarubande
New 9
Zone Mugina
Rwesero 3
Zone Rwesero
4.3.3
Difference
Difference
Difference
Optimal Route
total network
1448,37
Current –Road
network
Road – Total
Network
Current- Total
Network
3191
in RWF
Optimal Route
road network
3421,38
-230,38
1973,01
1742,63
54,61
3622,5
4516,71
1703,41
-894,21
2813,30
1919,09
52,98
3422,5
3865,70
1646,30
-443,20
2219,40
1776,20
51,90
3680,00
4849,37
1773,99
-1169,37
3075,38
1906,01
51,79
3047,50
3008,13
1476,27
39,37
1531,86
1571,23
51,56
Efficient use of river transport
Although transport from all sugar fields could be more efficient by using the barges on the
river, the capacity of the barges cannot transport all harvested sugar cane of one day. When
planning the transport, the tracks on which the most money can be saved should be done by
barge and the relative less costly tracks have to be transported by trucks from field to factory.
Figure 16 presents an overview of the sugar cane fields to be harvested in April 2016, the
according loading points and the current and most optimal route. The current route is
classified according to the amount which can be saved when using the river transport instead
of the current transport mode. On the transport of the cane coming from the loading points
Rwesero 3, New 9 South and New 10 South more than 1000 RWF per MT/km can be saved.
The reduction of the transportation costs of the sugar cane originating from the fields near the
loading points downstream the river, at the Kanzenze Bridge, New 2, F07 and Phase 2 is at
most 500 RWF per MT/km. In order to make the most efficient use of the barge system, the
transport routes with the highest reduction rate should be prioritized when changing to river
transport.
26
in %
Figure 16: Reduction of transport routes of sugar cane to be harvested in April 2016
4.4 Feasibility
The capital investment was not taken into account in the least-cost path analysis. Therefore,
based on an estimation of the yearly possible reduction the breakeven point of the yearly
reduction and the capital investments is calculated.
Table 10 presents the yearly transportation costs of the current situation and the optimal
route, when using the river barges. The possible yearly reduction is calculated as the
difference between the transport costs.
Table 10: Yearly transport costs and possible reduction
in RWF
in USD
(rounded on 10.000 RWF)
(rounded on 10.000 USD)
Transport costs Current Situation
420.240.000
550.000
Transport costs Optimal Route
276.210.000
360.000
Reduction
144.030.000
190.000
Table 11 shows the breakeven points of the capital investments for each phase. The
estimation of the capital investments is given in an upper and lower value due to the
uncertainty in this stage of the project. Phase 1 is based on the current capacity of the sugar
cane fields, therefore, the costs of river transport used in the shortest path analysis were
27
based on the operational costs of phase 1. Based on these operational costs, the breakeven
point of phase 1 will be reached after 8 to 13 years.
Table 11: Breakeven point of capital investment
Phase
Phase 1
lower value
Phase 1
upper value
Capital investment
Yearly Reduction
Breakeven point
1.490.000
190.000
8 years
2.460.000
190.000
13 years
28
5 Discussion
This chapter discusses the results of this research. First the outcomes of the second chapter
are reviewed. Similarities and differences between the situation in Rwanda and examples in
literature will be discussed. Furthermore the results on location of the landing and loading
point, the costs of the transport network, the optimal route and the feasibility of river transport
will be discussed.
5.1 Current situation
The current situation of the sugar cane production is described based on the data provided by
KSW and a visit to Rwanda in December 2015.
Currently, the sugar cane is processed within 72 hours after harvesting. Reducing this time
frame to 36 hours is possible by implementing the barge system. This would increase the
cane quality with 10% (S. Jayakumar, personal communication, December 8-12, 2015).
According to several studies (Fauconnier, 1993; James, 2008), the quality increases with the
speed of the process. Ideally, the sugar cane needs to be processed within 9 hours after
harvesting. In that way, the quality of the cane is the most optimal. At Kabuye Sugar Works,
processing the sugar cane within this time frame is impossible. Another reason why reducing
the processing time from harvest to processing is not one of the major focuses in Rwanda
could be the climate conditions. According to Fauconnier (1993), in a relative cold and stable
climate like the climate of Rwanda, the quality of the sugar cane is not decreasing as fast as
for example in Brazil.
Furthermore, compared to other studies and cases (Higgins, 2003; Grunow, 2007), both the
urge for constant milling at the factory and the speed of the total process from harvest to
processing are less important in Rwanda. Although the factory of Kabuye Sugar Works tends
to process 24/7, this is not always possible and this is accepted by KSW. In Rwanda, there is
almost no focus on reducing the speed between the harvest of sugar cane and the processing
of cane.
Next to this, over 60% of the sugar cane processed by the KSW factory is originating from out
growers fields. In contrast to the sugar cane inventory on the fields cultivated by KSW, the
crop data on the out grower fields is far from complete. These data is essential when
managing planting, harvesting and transport. A good relationship between Kabuye Sugar
Works and the out growers is therefore an important factor when optimizing the total supply
chain. Especially when not the whole supply chain is owned by one company, good
communication is key (Higgins, 2007). Several studies show the importance of a holistic
approach when it comes to optimizing the supply chain (Higgins, 2003, 2006a,b; Le Gal 2009;
Lejars 2008).
Last, socio-economic factors play a role in the production and transportation of sugar cane.
When visiting the people who currently transport little amount of sugar cane by boat, their
main concerns were their safety and their jobs. The current way of transporting cane over the
river can be dangerous due to the strong current of the river and the manual handling of the
relative small boats. In the past, some fatal accidents have happened. The safety of river
transport would be assured when implementing the barge system by RHDHV. Furthermore, it
is important that no jobs will be lost when implementing the river transport. Increasing
employment is a major component in ‘The Sugar; Make it Work’-project (“The Sugar: Make it
29
Work Project”, 2016). Studies of Higgins (2007) and Le Gal (2009) show that the socioeconomic factors play a major role in the sugar cane production.
5.2 Landing and loading points
The location of the landing points were copied from the already existing landing points. KSW
assigned these locations also as future landing points and these points were visited and
visually assessed by the consultant of RHDHV during the field visit. According to RHDHV,
these locations are suitable to develop a landing point. The landing points will be permanent
and require capital investment and development. Further research on those exact locations is
therefore recommended. Furthermore, the optimal path analysis showed that for every field,
the landing point at the Ruliba Bridge is the most optimal landing point to use (Figure 14).
Developing the three other landing points might not be necessary.
The location of the loading points is derived from a manual procedure. This procedure based
on several assumptions on the character of the river and the main factor was the distance of
from the fields to either a landing or a loading point. Visual assessment during the field visit
and research of previous years are fragile arguments to base the locations on. Therefore,
further research on the river dynamics should be conducted before the final locations of the
loading points is defined.
For the four landing points and even more for the 38 loading points the number of points also
needs to be discussed. As said, for every loading point, the landing point at the Ruliba Bridge
is part of the most optimal route to the factory. A result from the optimal path analysis could be
that other landing points are not necessary with the current area of sugar cane fields. Also, the
number of loading point is with 19 on each side of the river, a bit high. From some fields, the
sugar cane will be transported by foot to the loading points, while others could better be
transported by truck to the loading points. However, this means that the sugar cane has to be
transhipped twice during the total transportation. Neither the costs of transhipment nor the
transport from the fields towards the loading points are included in the analysis. Decisions on
how to arrange the transport to the loading points and data on the costs of this transport and
transhipment should be done prior to further decision making. The uncertainty of the final
amount and location of the landing and loading point does not influence the result of the
analysis, since the optimal route of all locations go through one single landing point.
5.3 Costs of transport network
The costs of the road transport are based on the surface of the road (Table 7). The costs of
the road network were calculated based on the facts whether the road was tarred or untarred.
It should be noted that a single variable to make an estimation of the costs is rather minimal.
Unfortunately, the second variable, the slope of the road network could not be used because
this variable did not show a significant relation with the costs of the road segments.
The costs of the river transport are based on estimations of the operational costs by RHDHV.
Although these costs are based on experiences with other projects, an uncertainty analysis
has not been done, since the there is no other data than the estimate. An uncertainty analysis
could describe the range of the output and therefore how well the estimation fits the real
situation.
The outcome of the shortest path analysis based on only the road network was compared to
the costs of the current situation and showed that the simulated costs were slightly
30
overestimating the current costs. These differences can have two possible reasons. First, the
noted length for the distance to cover from each field, of which the data was provided by KSW,
did not always correspond with the actual length measured for this track; optimal path based
on the road network. The length of several tracks is underestimated compared to the
measurements, which can cause an underestimation of the costs. Furthermore, the simulation
is done based on the assumptions that in the current situation all sugar cane is transported by
road. However, parts of the harvested sugar cane of some of the fields is already been
transported by river. The exact fields were not known, and therefore, this is not implemented
in the least-cost path analysis. For those fields, this possibly results in higher costs of the
optimized route over the road network compared with the current situation.
Although both the costs of the road transport, as the costs of river transport seem to be
calculated based on minimal data, this is not considered to have big influence on the final
results. The differences between the costs of different transport modes are that big, that even
large error ranges will not change the final outcome of this research.
5.4 Optimal Route
Transportation over the river is for all 42 landing and loading points cheaper compared to the
current costs of transport (Figure 14). The possible reduction of transport costs varies from 8%
up to 54% of the current transport costs. These results are based on several assumptions.
First, the calculations were done assuming there are 3 barges with a 112 tonnes capacity
each. The barges were assumed to be fully loaded at any time. Furthermore, transhipment
costs are not included in the analysis. Even though this research is a simplification of the real
situation, the results of the analysis clearly indicate that a substantial decrease in transport
costs can be realised.
RHDHV proposed barges with a total capacity of 336 tonnes a day. Since the current factory
can handle 600 tonnes a day when milling on maximal capacity, not all sugar cane can be
transported over the river. Therefore, either the capacity of the barges needs to be
reconsidered or the capacity must be used in the most efficient way. The routes on which the
most money can be saved, should change to river transport. Every month, depending on the
fields to harvest, decisions on the choice of transport mode can be made.
5.5 Feasibility
The economic feasibility is calculated on the capital investments of implementing the river
transport (Table 11). The estimation of the lower and upper value of the capital investments
varies almost 1.000.000 USD. This range is due to the insecurity of this phase of the project
(Boer, 2016). Furthermore, the breakeven point is calculated based on the assumption that
the total capital investment finally has to be financed by the reduction of transport costs. There
is an opportunity that a part of the capital investments are funded by other parties (“The
Sugar: Make it Work Project”, 2016). Therefore the estimation of the breakeven point is
probably an overestimate of the final breakeven point.
31
6 Conclusion
6.1 Research objective
The main objective of this research is optimization of sugar cane transport in Rwanda by
assessing different transport modes conditional to factory constraints and field and network
conditions.
This research has shown that implementing river transport can contribute in a major decrease
in transport costs. Along the 85 km that the river Nyabarongo flows through the sugar cane
fields, 42 locations were assessed to find the most optimal route to the mill. All optimal routes
follow the river until the landing point near the Ruliba bridge and from there they follow their
route by road to the factory. Based on operation costs, the costs of transport can be reduced
with values varying between 8 to 54% with an average of 40% of the current transport costs.
6.1.1
What is the current situation of cane transportation in Rwanda?
The sugar cane fields are mostly located on the river banks of the Nyabarongo river. In the
current situation the harvested sugar cane is transported by road from the fields to the factory.
The condition of the roads is very poor, what results in slower transport and damage to the
trucks. Especially near the fields and therefore near the river, the road is in poor condition.
After harvesting, sugar cane is processed within 72 hours, which influences the quality of the
cane. A part of sugar cane fields is not accessible by road and is transported over the river in
small boats. The transport is time consuming and can be dangerous due to the characteristics
of the river.
6.1.2
Which method is suitable for this cane transportation optimization problem?
Based on the shortest path algorithm developed by Dijkstra based on the costs of the
transport network, the optimal routes from each loading point to the factory were calculated.
Based on the characteristics of Dijkstra’s algorithm and this optimization problem, the
algorithm was preferred over other methods. Next to that Dijkstra’s algorithm is widely
implemented and therefore a convenient choice.
6.1.3
Which variables of the transport network can predict the costs of the road network?
This research showed that the condition of the road network as a variable is a main predictor
of the final costs of the road network. In contrast to other research, the slope of the road did
not show any relation with the costs of the transport network.
6.1.4
Which conditions are important to be considered when implementing the results of
this research in KSW circumstances?
The implementation of river transport can account for a major decrease in transport costs.
When taking the future into account, this can be a first step in optimizing the whole supply
chain. For further optimizing the supply chain, the cooperation of out growers and
transporters, which are part of the supply chain is necessary. Therefore, a good relationship
between these two parties is considered to be important.
Optimizing the total supply chain requires complete crop data on all sugar cane fields.
Therefore, efficient management of planting, harvesting and transportation demands up to
date crop data on the sugar cane fields cultivated by out growers.
32
6.2 Further research and recommendations
Transport of sugar cane is a component of the total supply chain. Comprehensive research on
all other components is required to optimize the total supply chain.
First, up to date crop information on all the sugar cane fields is essential for further
optimization. In this research, primarily crop information of sugar cane fields cultivated by
KSW was linked to the geometry. Crop information on sugar cane fields cultivated by out
growers could not be used in this research. Based on those data, a constant supply to the
factory can be planned. Efficient harvest and therewith the transport of sugar cane can be
calculated with complete and up to date crop information.
Second, the location and the number of loading points were based on several assumptions.
The river dynamics influence the possible locations of the loading points. Before assigning
possible locations, further research needs to be done on the river dynamics. The exact
location and number of loading points can determine based on the outcome of river research.
The analysis of this research shows that one landing point, near the Ruliba bridge is used in
all optimal routes. Creating one instead of four landing points means that the planned capacity
of that landing point should be increased.
Furthermore, the costs of the transport network could be researched in more detail. In this
research, the costs of river transport were based on the estimation by RHDHV. The costs of
the road network are based on the condition of the roads. The prediction of the costs of both
transport modes could be more accurate when these are based on more variables. An
uncertainty analysis of these variables on both road and river transport could show how well
the predicted costs fit the real situation.
Last, the socio-economic effects of implementing river transport on a larger scale should be
researched. Although this project would create more jobs in the near future, the effect on the
current trucks drivers and boat owners should be kept in mind.
33
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35
Appendix A: Computation of slopes of the GPS-tracks – Rscript
36
Appendix B: Computation of Shortest Path - Rscript
37
Appendix C: Edge IDs of Landing and Loading Points
Landing/Loading Point
Phase 2
Side
NA
Type
Landing
Edge ID Optimal Route
3552
Edge ID Current Situation
3536
New 1
South
Loading
3480
3450
New 1
North
Loading
3497
3534
New 2
South
Loading
3386
3360
F07
North
Loading
3383
3384
New 3
South
Loading
3377
3355
New 2
North
Loading
3370
3382
Kanzenze Bridge
NA
Landing
3320
3306
New 3
North
Loading
3297
3357
New 4
South
Loading
3271
3266
New 4
North
Loading
3284
3276
New 5
South
Loading
3314
3303
New 6
South
Loading
3359
3337
Nyarubande 1
North
Loading
3357
3344
New 7
South
Loading
3277
3272
Nyarubande 2
North
Loading
3302
3294
Confluence Akanyaru
NA
Landing
3217
3217
New 8
South
Loading
3247
3247
Nyarubande 3
North
Loading
3281
3292
New 9
South
Loading
3295
3284
New 5
North
Loading
3379
3364
New 10
South
Loading
3422
3396
Nyarubande 4
South/North
Loading
3542
3511
New 11
South
Loading
3745
3881
New 7
North
Loading
3721
3686
Rwesero 3
North
Loading
3951
3910
New 12
South
Loading
3989
3944
New 8
North
Loading
4158
4204
New 9
North
Loading
4744
4695
New 13
South
Loading
5424
5941
Ruliba Bridge
NA
Landing
6449
7004
New 14
South
Loading
7414
7359
New 10
North
Loading
7990
8530
New 15
South
Loading
8700
8654
New 11
North
Loading
9072
9070
New 16
South
Loading
9732
9677
New 12
North
Loading
9914
9881
New 17
South
Loading
10212
10157
New 13
North
Loading
10262
10273
New 18
South
Loading
10258
10209
New 14
North
Loading
10370
10321
New 19
South
Loading
10559
10498
38
39