293

22

ANALYSIS OF THE EFFECTS OF A COOPERATIVE
DELIVERY SYSTEM IN BANGKOKAli Gul Qureshi, Asian Institute of Technology, Thailand.
Shinya Hanaoka, Asian Institute of Technology, Thailand.

ABSTRACT
Freight companies, particularly small companies, may fear losing their identity if they
participate in cooperative delivery systems (CDS), which use third party distribution, once thegoods are collected at common stock points for their cooperative distribution. This study
introduces a CDS, which consists of a common stock point for goods consolidation and use thetrucks of participating companies for distribution instead of third party or neutral carriers.
Objectives of the study also include the development of Truck Assigning Module to assign the
consolidated routes of CDS to the trucks of participating companies. In the case study in
Bangkok, the results estimated a considerable reduction in delivery costs that may be used in
evaluating future implementation of CDS.

INTRODUCTION
With the increase in the economic and industrial activities, large cities are attracting more andmore population due to better opportunities for jobs, education, health facilities andentertainment. All this is coupled with simultaneous increase in transportation requirements
both in terms of passengers and freight. A high proportion of total goods movement occurs
within cities (Taniguchi, et al., 2001). Most of this movement is based on road transport.
Traffic congestion, noise, vibrations, generation of NOx, CO2 and other environmental
problems, accidents, loading and unloading on streets are common problems caused by theroad based freight transport in urban areas.
Public logistics terminals, load factor controls, underground freight transport systems andcooperative delivery systems (CDS) are some city logistics schemes to improve the urban

294 Recent advances in city logistics
freight transport. CDS is a city logistics technique aimed at solving the urban freight transport
problems by seeking the cooperation in delivery and/or collection of the goods between manylogistics firms in order to increase the load factor and reduce the number of trucks required todeliver/collect goods. Cooperative use of information and cooperation in building andoperating a common depot are other parts of cooperative freight transport systems (Taniguchi,
et al., 2001).
A growing number of private trucks are registered every year in Thailand (Figure1). The
growing percentage of goods being transported by road in Thailand shows that Bangkok also
suffers from the same problems, being the economic center of Thailand. Large numbers of thetrucks have to enter and leave of Bangkok for collection and distribution of goods.

No. of Private Trucks

4,000,000
3,500,000

3,000,000
2,500,000
2,000,000
1996

1997

1998

1999

2000

2001

2002

2003

2004

Year

Source: MOT
Figure 1 Number of private trucks in Thailand
In response to the above mentioned city logistic problems, the Government of Thailand has
introduced measures to reduce these negative impacts of urban freight movement such as theconstruction of public logistics terminals and a ban on large trucks in the central area of
Bangkok. Along with improvement in environment, the Department of Land Transport, inThailand described the benefits of these truck terminals including as “to reduce the loss by thenon-full loaded trucks, since truck terminal is the place to combine and transfer goods and thetruck companies can share the trucks with same destination”, so as the idea of cooperative
delivery system was aimed (Sridurongkatum, 2002). A simulation study done by Takahashi
and Srikupanichkul (2001) showed that these terminals will decrease the large truck traffic
inside Bangkok but light truck traffic will increase.
This study aims to investigate the concept of a Cooperative Delivery System (CDS) in
Bangkok to increase the load factor and reduce the number of trucks. Many stakeholders may
get benefits such as reduced distribution costs for freight carriers, reduced traffic andenvironmental problems for residents and administrators coupled with better management oftraffic. The study area is the Central Business District (CBD) of Bangkok, comprising a total
area of 28.9 sq. km, and with a population of 0.45 million people (2001 figures) (Angkana,2002). 84 UTDM (Urban Transport Database and Model Development project, Bangkok)(UTDM, 1998) zones of the CBD were used in this study.

Effects of a cooperative delivery system in Bangkok 295
There are many forms of CDS, including the CDS where third party logistic is used to
distribute the goods to the clients, once the goods are delivered to common stock point by
participating companies. Examples of this type of CDS are the one followed in Kassel,
Germany (Kohler 2004); and the social experiment conducted in Marunouchi, Tokyo, Japan(Sinarimbo et al, 2004). A neutral carrier is employed to collect and distribute the bundledgoods in the centre of Kassel. In Marunouchi, natural gas trucks were used to distribute thegoods from the common stock point to the clients rather than the trucks of participatingcompanies.
Freight companies, particularly small ones, may fear losing their identity, when they participate
in such a type of CDS. Therefore, this study has considered a CDS, where the distribution of
goods from the stock point to the clients would be undertaken by using trucks of participatingcompanies. This type of CDS may restore the confidence of small companies in CDS as well as
provide them the chance to advertise using their trucks.
Many researchers have modelled CDS (Ieda et al., 1992; Duin and Jagtman, 1999; Taniguchi,
et al., 2001; Sinarimbo et al, 2004). Yamada and Taniguchi (2004) modelled CDS in urban
settings but their study considered a third party cooperative organisation. There was no model
for assigning the routes developed in a cooperative environment and a common stock point for
the trucks of the companies taking part in CDS. Therefore this study also aims at developmentof Truck Assigning Module (TAM) that is able to develop and assign the most economic routeto the company that has the greatest number of the clients and/or highest client demand on thatparticular route for that particular instance.

METHODOLOGY
GeneralData required to model or simulate urban freight transportation systems includes, the numberof carrier firms, number of clients for each firm, their location, location of depots or common
stock point, number and capacity of the trucks available for delivering the goods at eachcompany, the frequency and size (weight) of the deliveries and specified time windows by theclients to receive the goods. Unfortunately this data is not available for the present noncooperative delivery system of freight movement in Bangkok. For that reason the above
mentioned data was randomly generated for each firm whereas actual road network data of
Bangkok’s CBD area was used from the UTDM report. Vehicle running cost, waiting time
penalties and late arrival penalties were based on the vehicle operating cost (VOC) as
calculated in another traffic study in Bangkok (MOT, 2004).
The objective of this study is to compare the relative advantages and potential of CDS over thepresent non-cooperative system used for the intra city movement of goods. Therefore thegeneral approach of the methodology is to compare the effects of both the systems in terms ofnumber of trucks required to deliver the goods and the cost of delivery. This was done byrunning the vehicle routing and scheduling problem with time windows (VRP-TW) model bothin non-cooperative and cooperative environment and comparing the output from both of them.

296 Recent advances in city logistics
Data Generation
Five hypothetical companies A-E were used to represent freight carriers participating in the
CDS. The number of the clients for each company was restricted from 10 to 50, where as zone
one was set as common stock point, and the remaining 83 zones were designated as client
zones. Medium size trucks with capacity 2000 kg (MOT, 2004) were used as delivery vehicles.
Two hours time window for each client was randomly generated within 0800 (8 AM) to 2000
(8 PM). The delivery time (service time) was set as 10 minutes plus 0.05 minutes per kg of
demand. The vehicle operating cost for medium sized truck is 6.774 Baht/Vehicle-Kilometre(MOT, 2004). This was converted to 3.474 Baht/minute ($5.21/min.) by multiplying the ratioof total kilometres to the total working hours of the vehicle over its life. The waiting time costis mostly taken as truck running cost where as late arrival cost varies from 4 to 5 times thetruck running cost. The fixed cost of trucks was not available for Bangkok but it was estimatedon the basis of the ratio of VOC to fixed cost used in Taniguchi et al. (2000).

VRP-TW Model
The VRP-TW model was developed inspired from the one presented by Taniguchi andThompson (2002), which minimises the total cost of delivering the goods with truck capacityand designated time windows constraints. The objective function of the model is as:
k

n

1

i =1

Minimize[C f × k + ∑ ∑ C d ]

(1)

Where
Cd
i:
Cf :
k:
n:
Cd :
voc :
ti,i+1 :
tdi :
tai :
ei :
li :
Pl:

voc x (ti,i+1 + tdi)
voc x (ti,i+1 + tdi + (ei - tai))
voc x (ti,i+1 + tdi)+Pl x voc x (li - tai )if ei < tai < li
if tai < ei
if li < taiclientfixed cost of the vehicle utilisation
number of the routes in solution
total number of the clients in the routedelivery cost
vehicle operating cost
travel time from client i to i+1
delivery time (service time) at client i
arrival time at client istart of time window of client i
end of time window of client i
late arrival penalty factor

Usual VRP constraints such as each client can only be visited once and total demand along aroute must be less than the capacity of delivery vehicle, were incorporated in the model. The
used VRP-TW model is simple since the idea is only to compare the relative advantages of
CDS over non-cooperative environment. Nonetheless if more refined VRP-TW models as
VRP-TW with probabilistic times (Taniguchi and Thompson, 2002), VRP-TW with spare time

Effects of a cooperative delivery system in Bangkok 297(Thompson, 2004) would be used to compare the cooperative and non-cooperative delivery
systems, the advantages of cooperative delivery systems could be more emphasised.
A Matlab program was developed to solve the VRP-TW model using a Genetic Algorithm(GA). Integer population with order based crossover and swap mutation was adopted.
Stochastic universal selection method was used with fitness based reinsertion technique using
Matlab Genetic Algorithm Tool Box developed by Chipperfield et al.

Non-Cooperative System

When applied to the non-cooperative environment, VRP-TW model searches for the mosteconomical routes for the trucks of various companies depending upon their capacity,
satisfying the time windows constraint set by clients. Route starting time (ie. departure times)
from depot (assumed to be located in same zone as of common stock point) for each route of aparticular company as well as the cost of the route is also calculated by the model.

Cooperative Delivery System

In a cooperative environment, the resources (trucks) and clients of all the companies taking
part in CDS were pooled together to form another virtual firm “F”. Traffic zones themselves
were considered as the clients in the model, so combining all the clients of various firms
presented the problem of repeated client zones in two or more companies’ client lists. This
problem was tackled by inserting dummy zones instead of the repeated client zones, forexample if client zone “13” is repeated in the client lists of company “A” and company “B”
then while listing the clients for virtual cooperative firm “F”, client zone “13” was listed once
as being the part of client list of company “A” and it was renumbered as client zone “85” (asthe original number of zones were “84”), while considering the client list of company “B”.
This specially occurs due to the time windows as same client zone with changed time windows
represents two clients. Inter zonal travel time matrix was also expanded accordingly.
As the number of clients for this virtual cooperative firm was sum of the number of clients ofindividual companies, comparatively greater optimisation effort was used while running the
VRP-TW model in cooperative environment. This is consistent with the NP-hard nature ofvehicle routing problems where the complexity increases as the number of nodes increases.
Output of the VRP-TW model in cooperative environment is an ordered chain of clients fromvarious companies participating in the CDS, grouped in many routes; cost of the complete
solution and the start time of each route from common stock point.

Truck Assigning Module

As shown in the Figure 2, we have considered a CDS, where the distribution of goods from thestock point to the clients, using trucks of participating companies. The output of thecooperative application of VRP-TW model is a long chain of clients numbers grouped in routes
and it does not specify which company’s truck has been assigned to what route. The Truck
Assigning Module (TAM) was developed for handling this aspect. TAM assigns routes to thetrucks of various companies based on the highest number of clients of an individual company,
present in a particular route.

298 Recent advances in city logistics

Trucks of various companies bring goods to thecommon Stock Point

B

A

C

D

E

Cooperative Delivery System’s common Stock Point.
Goods are consolidated and reloaded on trucks.

A

C

E

D

B

Consolidated goods are carried by the trucks ofcompanies participating in Cooperative Delivery System
Figure 2 Proposed cooperative delivery system
As shown in the Figure 3, TAM takes the solution of cooperative application of VRP-TW
model as well as the renumbered clients lists (due to repeated client zone numbers) ofindividual companies as its input. It then takes each route from the cooperative solution andsorts it by matching the client zone numbers to the renumbered client lists of individualcompanies. It then assigns the route to the truck of that participating firm which has the
maximum numbers of clients on that route. In case of ties between two or more firms thedemand (weight) to be delivered for each firm along with maximum number of clients is
considered.
VRP-TW (Cooperative Environment) Output
R1

R2

1,4,8,12

9,3,7,85

R3………………

Ri……………… Client list & order,

Randomly Generated Company Data
Company A Company B... Company E
ClientClientClient
1
3
7
5
9
10
13
85
14
12
15
19

Truck Assigning Module
Check the client list of route Ri for each company and assign the truck of thatcompany which has most clients on that route.

Figure 3 Flow chart of truck assigning module

TEST CASES
Three different cases are considered as follows:
I. Freight carriers put more emphasis on load factors (Base case)
II. Freight carriers put more emphasis on over all cost reduction.
III. Maximum demand at each client is reduced and penalty cost is increased.

Effects of a cooperative delivery system in Bangkok 299
Case IThe first case guides the models to find the best possible solutions where the load factors are
maximised and the number of the routes kept to minimum. Clients were added to the route
until the next insertion violates the capacity constraint of the delivery truck. The early arrival
penalty cost and late arrival penalty cost are added to the cost of solution. The higher demandin this case may be mapped for the distribution systems where the goods to be delivered arebulk and massive.
Case IIIn this case all other settings were kept same as case I, only with one change i.e. clients wereadded to a route until the next insertion either violates the capacity constraint or its penalty costis more than the fixed cost of the truck used (cost of starting a new route). This case looks foran overall cost reduction and gives less emphasis on load factors.
Case IIIIn this case the penalty cost for late arrival was increased from four times to five times that of
the VOC and the range of demand was also decreased. The minimum demand at each clientzone was set as 50 kg and the maximum as one sixth of the truck capacity i.e. 333 kg. The
client insertion condition in the route was kept same as in case II. This case was set by keeping
in view the distribution of relatively small sized goods of more valuable nature thus the latearrival penalty was also increased.
Table 1 Parameters for different cases
Settings
Parameters
Case I
Case II
No. of client zones
Vehicle Capacity
VOC
Min. demand at client
Max Demand
Waiting penalty
Delay Penalty
Fixed Cost of Truck use
Population Size For Individual Company
Population Size For cooperative mode
No. of Generations for individual company
No. of Generations for cooperative mode
Max. no. of clients for individual company
Min no. of clients for individual company

83
2000 Kg
3.47 Baht/min
100 Kg
666 kg3.47 Baht/min
13.9 Baht/min
3500 Baht
40
80
100
1000
50
10

83
2000 Kg
3.47 Baht/min
100 Kg
666 kg3.47 Baht/min
13.9 Baht/min
3500 Baht
40
80
100
1000
50
10

Case III83
2000 Kg
3.47 Baht/min
50 Kg
333 kg3.47 Baht/min
17.3 Baht/min
3500 Baht
40
80
100
1000
50
10

RESULTS
Fifty random data sets were generated, 25 for case I and case II (same data settings) and 25 forcase III, thus overall model was run 75 times for in both the cooperative as well as the noncooperative environment. Figure 4 compares the average cost of non-cooperative and CDS
solutions in all the cases and Figure 5 shows the percentage cost savings in the case of CDS

300 Recent advances in city logistics
over non-cooperative delivery system. Figures 6 and 7 show the distribution of costs due toroutes (fixed cost), running time, early arrival penalty and late arrival penalty in noncooperative system and CDS respectively. There was a consist decrease in average costs forboth systems in all the cases. In non-cooperative system the reduction in cost in case II
compared to case I was mainly due to the reduction in early and late arrival penalty costs
owing to the new route start condition of case II where new route was started if the penalty cost
was too excessive. Case III, where the demands at clients were kept low, has least cost among
them all, basically due to less number of routes.

15

170

% Cost Reduction

Ave. Cost (Thousands)

Similar traits were found with the CDS, as in the non-cooperative system, when performanceof CDS was compared in different cases. Cost of CDS was less than non-cooperative delivery
system in all three cases as shown in Figure 4. Compared to non-cooperative environment,
CDS was found very efficient in reducing late arrival penalty costs as it almost eliminated latearrival penalty (Figures 6 and 7). There was a decrease in cost savings in CDS in case II
compared to the savings in case I, even though the same datasets were used as input. Thissignifies the effectiveness of the optimisation condition used in case II, which focused on overall cost minimisation. The cost savings in CDS again rose to 12.93 % in case III; higher
average load factors from the CDS was the main factor influencing these results.

13

150

10

130
110
90
70

case I

case II

Non-Cooperative

case III

30case I

case IIcase III

Figure 5 Percentage cost savings in CDS

120
100
80
60
40
20
0

Ave. Cost
Thousands

Ave. Cost
Thousands

5

Cooperative

Figure 4 Average cost

Routes8case I

case IIcase III

Running T ime

Early Arrival

Late Arrival

Figure 6 Shares inaverage cost of non-cooperation

120
100
80
60
40
20
0

Routescase I

case IIcase III

Running T ime

Early Arrival

Late Arrival

Figure 7 Shares inaverage cost of cooperation

Figures 8 and 9 show the load factors for the non-cooperative environment and CDS in all
three cases. Initially it was perceived that load factor would decrease in case II compared tocase I but the results showed that load factor has improved a little in case II compared to case I.
The reason is that overall cost optimisation condition gives lower cost routes much earlier inEffects of a cooperative delivery system in Bangkok 301
the optimisation process as compared to the case in which load factor maximisation is the main
objective and the penalties are just added to the cost. As Genetic Algorithm keeps the bestsolutions as the benchmarks, this guides the optimisation process in much batter way, which
results in not only lower costs but also improved load factors. In case III, load factor went
down as was perceived due to smaller delivery sizes (clients’ demands). In the non-cooperativeenvironment penalty costs are reduced and reliability is improved due to freight carriers often
compromising load factors in order to satisfy the time windows.

1.00

1.00

0.95

0.95

Load Factor

Load Factor

CDS was able to increase the load factor by a little percentage in cases I and II (Figure 10),where the load factor of even non-cooperative environment was quite high above 0.85 (Figure8), and little further increase was possible. It was in case III, where CDS showed its promise byconsiderably improving the load factors by more than 12 %. Improving load factor isconsidered to be an important evaluation measure for any city logistic scheme and the proposed
CDS satisfies this well. The effect of increasing load factors in the CDS is also reflected in the
improvement in proportion of savings due to lower number of routes in CDS compared to noncooperative environment.

0.90
0.85
0.80

0.90
0.85
0.80

0.75

0.75

0.70

0.70

case I

case IIcase IIIcase I% Increase

12
10
8
6
4
2
0

case I

case IIcase III

Figure 10 Percentage increase in load factors
in CDScase III

Figure 9 Load factor (cooperation)% Share in Cost savings

Figure 8 Load factor (non-cooperation)case II

60
50
40
30
20
10
0

Routescase I

case IIcase III

Running T ime

Early Arrival

Late Arrival

Figure 11 Shares of various elements
in cost savings of CDS

As shown in Figure 11, the distribution in cost savings due to lesser running time in CDS wasvery small. It means that the VRP-TW model also optimised the distance travelled very
efficiently in the non-cooperative environment. Figure 12 shows the distribution of costsavings due to lesser running time in all three cases. Percentage contribution in total savings of
CDS, due to lesser running time was more than doubled in case II compared to case I, but again
it declined in case III.

302 Recent advances in city logistics

3.0
50

2.0

40

% Share% Share

2.5
1.5

30

1.0

20

0.5
0.0

10

case I

case IIcase IIIcase I

Figure 12 Share of less running timein cost savings of CDS

case IIcase III

Figure 13 Share of fewer routes
in cost savings of CDS

The proportion of cost savings increases due to the lower number of routes in cooperativeenvironment (Figure 13). It started from a moderate value of 21.47 % in case I and eventuallybecame the largest factor in cost savings in case III (Figure 11). This result signifies that the
CDS can be very successful in distribution environments with lower demands and tighter time
windows, by reducing number of routes. Reducing number of routes means reduced number oftrucks on the roads, with corresponding improvement in traffic conditions and decrease in onstreet parking. This could be a very important factor considering the narrow roads of Bangkok.

60

40

50

30

% Share% ShareWhile fewer routes were the major contributing factor in the cost savings of the CDS, the CDS
was very efficient in reducing the penalties in all the cases, as well. Figure 11 shows that boththe early and late arrival penalties reduction was the major source of cost savings for the CDS
in case I. This was due to the fact that while non-cooperative system was unable to produce
better penalty optimised solutions in case I (where major emphasis was only on load factor
optimisation), CDS had a very large number of clients (as clients of all firms were added
together) and the VRP-TW model was able to search less penalty prone solutions by trying
many possible combinations to satisfy time windows of clients.

40
30

20
10

20
10

0

case I

case IIcase III

Figure 14 Share of less early arrival
penalty in cost savings of CDS

case I

case IIcase III

Figure 15 Share of less late arrival
penalty in cost savings of CDS

Figures 14 and 15 show the percentage share of reduced penalty costs in savings obtained in
CDS. Lower early arrival penalties have the maximum share (Figure 11) in CDS savings incase II, whereas it dropped slightly in case III (Figure 14). Both these cases were using the
optimisation condition which prohibits excessive penalties. These results were due to lower

Effects of a cooperative delivery system in Bangkok 303
penalty factor (equal to VOC) for early arrival penalty and hence a larger penalty could be
tolerated rather than to start a new route. With the late arrival penalties the trend was reversed
i.e. immediately after introducing the new optimisation condition, even non-cooperativeenvironment reflected very good results as far as late arrival penalty was concerned and aconsiderable decrease was found in late arrival penalty costs for the non-cooperativeenvironment in case II and III compared to that of case I (Figure 6). Higher penalty factors (4times VOC in case II and 5 times VOC in case III) made even a moderate late arrival penalty
time, a heavy cost and thus the optimisation process tried to minimise this cost as much as
possible. This caused the share of cost savings in CDS due to reduction in late arrival penalty
cost to decline considerably in case II and III as shown in Figure 15.
A high reduction in early and late arrival penalties in CDS compared to non-cooperative
system does not only save cost but it also improves the reliability of the companies
participating in the CDS. A lot of emphasis is given to improve the reliability and in doing so
freight carriers even scarify their profits at some time by sending deliveries with lower loadfactors. As seen in above discussion the CDS was found to be very proficient in dealing withthis problem of lower load factor at the same time improving reliability with savings in over all
delivery costs as an added advantage.
Table 2 shows the effects of the CDS compared to the non-cooperative environment for all the
scenarios.
Table 2 Effects of cooperative delivery system
Parameter
Percentage Change due to CDS
Case I
Case II
Case III-12.56 %-8.09 %
-12.81 %Average Cost
+ 4.24 %+4.22 %
+12.43 %Average Load Factor-3.92 %
-4.08 %
-10.32 %Average Fixed Cost (Routes)-0.96 %
-1.20 %
-1.32 %
Average Running Time Cost
-45.66 %-48.64 %-34.25 %Average Early Arrival Penalty Cost
-90.80 %-79.91 %-89.26 %Average late Arrival Penalty Cost

Truck Assigning Module’s Performance

The Truck Assigning Module (TAM) results of case I are shown in Table 3. Almost in all cases
except two, every company has at least one route allocated in cooperative environment.
Companies C and D received at least one route 96 % of the times (in 24 out of 25 data sets)
whereas corresponding results for companies A, B and E were 100 %. Also at some instances,
a company in cooperative environment has to take more routes than in non-cooperativeenvironment. But as the reduction in cost was in overall solution of the system, benefits to thecompany would always be more than what extra resources and cost a company has to bear incooperative environment.
The overall performance of the TAM is shown in Figure 16 for all the three cases. It can beseen that in all the cases every company received a due share of the routes. This shows that
proposed CDS, with the help of the TAM, can offset the fear of small participating companies
that their identity as fright carriers will be lost if they join a CDS as well as giving them an
opportunity to advertise through their trucks operating in the community.

304 Recent advances in city logistics
Table 3 Truck assigning module results for case I
Non – Cooperative System
Companies

Data
set
No.

A

B

C

D

E

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

9
10
6
5
10
8
7
6
5
3
3
9
4
9
8
5
7
3
9
7
3
7
3
3
4

4
7
2
8
8
5
8
6
7
4
7
7
3
7
5
3
8
5
9
4
10
5
8
6
3

7
5
5
9
8
7
10
6
3
8
4
9
6
8
8
5
8
7
5
3
6
8
7
8
6

6
9
3
3
6
4
5
4
7
9
3
4
4
8
7
6
9
8
4
7
4
7
5
6
3

8
3
3
5
6
3
7
5
4
9
4
8
6
5
5
5
8
8
6
5
7
8
9
10
9

Cooperative System (TAM – Results)
Net
Companies
reduction
A
B
CD
E in routes
11
1
5
5
11
1
12
9
1
8
2
2
9
1
7
1
1
0
3
9
13
1
3
1
9
8
13
5
2
1
8
4
9
3
2
1
7
9
12
3
5
1
6
6
6
5
3
1
4
9
9
3
1
0
3
1
10
8
10
1
3
10
1
2
3
2
11
7
6
5
7
1
3
4
6
2
7
1
11
7
7
7
3
2
10
6
8
3
5
1
6
2
4
7
4
1
8
8
7
6
10
1
2
5
8
9
7
0
12
7
4
3
6
1
4
5
1
9
5
2
1
12
5
4
7
1
5
2
10
9
7
2
2
6
7
6
10
1
2
5
8
3
14
1
2
3
6
3
9
2

Percentage

100
90
80

A

B

C

D

E
A

70

B

C

D

E
A

B

C

D

E

60
50

case I
Company A

Company B

case II
Company C

case III
Company D

Figure 16 Performance of truck assigning module

Company E

Effects of a cooperative delivery system in Bangkok 305

CONCLUSION
Like every project starts by first making its plans and drawings, this study was aimed at
introduction of a CDS system and its probable effects in Bangkok. The results obtained showthat a considerable reduction in delivery cost may be realised by implementing CDS in
Bangkok. As Bangkok has already three truck terminals, its implementation would be also notdifficult.
Results of case III showed that CDS was very successful in the delivery environment where thesizes of deliveries were small. In this case, the non-cooperative environment forced acompromise on load factors to maintain a certain level of reliability. The CDS thrived in thissituation not only by increasing the load factor and reliability by a considerable amount, it also
saved delivery costs which can boost the profits of participating firms. As well, the CDS wasable to significantly reduce number of routes compared with the non-cooperative environment.
Reducing number of routes not only reduces distribution cost but also reduced the number oftrucks on roads, with corresponding improvement in traffic conditions and decrease in on-street
parking. This could be a very important factor considering the narrow roads of Bangkok.
Overall the TAM was able to assign the consolidated routes back to the participatingcompanies quite competently. It showed that it could offset the fears of small participating
freight companies of being side lined in the CDS, and of losing their identity if they join the
CDS. Assigning routes back to the trucks of participating companies means they can also use
their own trucks for their advertisement and publicity within the community.

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