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Estimating trade-off among logisticscost, CO2 and time: A case study ofcontainer transportation systems inKoreaDongjoo Park a , Nam Seok Kim b , Hyeongjun Park a & KyeongsooKim ca Department of Transportation Engineering , The University ofSeoul , Seoul , Republic of Koreab Department of Transport Engineering and Logistics , HanyangUniversity , Ansan , Kyeonggi , Republic of Koreac Division of Railway Research , Korea Transport Institute ,Kyeonggi , Republic of KoreaPublished online: 30 Mar 2012.

To cite this article: Dongjoo Park , Nam Seok Kim , Hyeongjun Park & Kyeongsoo Kim(2012) Estimating trade-off among logistics cost, CO2 and time: A case study of containertransportation systems in Korea, International Journal of Urban Sciences, 16:1, 85-98, DOI:10.1080/12265934.2012.668322

To link to this article: http://dx.doi.org/10.1080/12265934.2012.668322

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Estimating trade-off among logistics cost, CO2 and time: A case study of

container transportation systems in Korea

Dongjoo Parka*, Nam Seok Kimb, Hyeongjun Parka and Kyeongsoo Kimc

aDepartment of Transportation Engineering, The University of Seoul, Seoul, Republic of Korea;bDepartment of Transport Engineering and Logistics, Hanyang University, Ansan, Kyeonggi,Republic of Korea; cDivision of Railway Research, Korea Transport Institute, Kyeonggi,Republic of Korea

(Received 27 January 2012; revised version received 9 February 2012; final version accepted 28February 2012)

One of the basic necessary conditions for successful implementation of policieswhich encourage the use of intermodal freight transportation systems is tounderstand ‘‘what the desired mode share of the intermodal freight transportationsystems is’’ and also ‘‘what the trade-off relationships among various concernssuch as logistics cost, time and CO2 emissions are’’. The objective of this study is toestimate the trade-off relationships among logistics cost, time and CO2 emissionsof the freight transportation systems of Korea. For this, container cargo data, theroad network for trucks and the railway network are used as a case study. Therelationships are estimated by assigning container cargoes between productionzones and consumption zones and by solving linear-programming-based tran-sportation problems. This study clearly shows the trade-off relationships betweenattributes. The desired levels of modal split of the railway-based intermodal freighttransportation system with respect to different aspects are identified. It isconsidered that the findings of this study would be valuable as anchor points forsetting national policy directions on freight transportation system developmentand determining the level of subsidies for shippers or carriers who shift fromtrucking to a railway-based intermodal freight transportation system.

Keywords: trade-off; logistics cost; CO2; time; inter-modal

Introduction

Most of the logistics systems seek to minimize logistics cost and time. This type ofefficiency oriented logistics systems has resulted in a high degree of dependency onthe truck-only system. The road freight mode share in tkm in 2009 is about 77.5% inthe European Union (EU) (Eurostat, 2012). In Korea, the trucking’s mode share offreight transportation is about 76%. The high dependency on the truck-based freighttransportation system has made trucking a major contributor to CO2 emissions(European Commission, 2007). In Korea roughly 6% of CO2 emissions come fromfreight transportation by truck.

In order to reduce CO2 emissions from road freight transportation, manycountries, international organizations and local governments have been implement-ing policies to encourage non-trucking freight transportation modes, focusing on

*Corresponding author. Email: djpark@uos.ac.kr

International Journal of Urban Sciences

Vol. 16, No. 1, March 2012, 85–98

ISSN 1226-5934 print/ISSN 2161-6779 online

� 2012 The Institute of Urban Sciences

http://dx.doi.org/10.1080/12265934.2012.668322

http://www.tandfonline.com

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intermodal freight transportation systems (European Commission, 2001; Bontekon-ing et al., 2004). In Korea, a number of greenhouse gas reduction programmes havebeen implemented during the last decade. They include: (i) paying subsidy forshippers or carriers shifting from trucking to intermodal freight transportationmode, similar to the Marco Polo programme of the EU; and (ii) imposing additionaltaxes based on CO2 emissions.

One of the basic necessary conditions for successful implementation of policieswhich encourage the use of intermodal freight transportation systems is to understand‘‘what the desired or optimal mode share of the intermodal freight transportationsystems is’’ and also ‘‘what the trade-off relationships among various concerns such aslogistics cost, time and CO2 emissions are’’. The existing studies on this area havelimitations. First of all, most of them are only concerned about two attributes, mostlylogistics cost andCO2 emissions, without considering time, which is considered as one ofthe important decision-making criteria of shippers or carriers. Second, the trade-offrelationships were usually estimated not by a sophisticated large-scale transportationnetwork model but by econometric-based approaches. In this context, the objective ofthis study is to estimate the trade-off relationships among logistics cost, time and CO2

emissions of the freight transportation systems ofKorea. For this, container cargo data,the road network for trucks and the railway network of the intermodal freighttransportation system in Korea are used as a case study.

This paper first discusses research scopes, findings and limitations of the existingstudies on the topic of interest, including the uniqueness of this study. Then datacollection, transportation network, formulation, and solution methodology andprocedure are outlined in detail. Analysis of results is presented, focused on thetrade-off relationships among decision-making attributes and the desired level ofmode share of the intermodal freight transportation system. Finally a concludingdiscussion including a summary of findings and implications and recommendationsfor future extensions follows.

Literature review

Kim et al. (2011) identified the relationship between logistics costs and CO2

emissions by transferring transportation modes to single mode or intermodal freighttransportation systems. They built a hub and spoke network and an origin anddestination (O/D) table between a seaport in Rotterdam, the Netherlands and aseaport in Gdansk, Poland. To analyse the relation between logistics costs and CO2

emissions, they not only find freight mode and route for containers by minimizinglogistics costs and CO2 emissions but also estimated the monetary values of CO2

emissions using the gradients of a trade-off graph.Bagajewicz andCabrera (2003) proposed two Pareto optimal solutions visualization

techniques for multi-objective design and upgrade of sensor networks. The firstmethodology is based on the projections of a Pareto optimal set onto specific two-dimensional surfaces, and the second is the representation of the problem in parallelcoordinate systems. Winebrake et al. (2008) presented an energy and environmentalnetwork analysismodel to explore trade-offs associatedwith freight transportation. Themodel can be used to explore trade-offs among alternative route selection acrossdifferent modal combinations, and to identify optimal routes for objectives that featureenergy and environmental parameters (e.g. minimize carbon dioxide emissions). Themodel was demonstrated with three case studies of freight transportation along the USeastern seaboard.

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Chang et al. (2009) analysed an intermodal transportation problem of interna-tional container cargoes while incorporating the external costs of the modes into anoptimization model in Korea. The objective of the analysis was to minimize the totallogistics costs as well as external costs such as air pollutants and greenhouse gases(CO2). To optimally solve the problem, they employed a linear programming model.Agrawal et al. (2004) made Pareto optimal solutions by using an n-dimension graphwhile applying a multiple-purposed optimization model into the parameters. Theydemonstrated the usefulness of visualization that was shown to aid in the finaldecision of what potential optimal design point should be chosen amongst all possiblePareto solutions. Jia et al. (2006) proposed a multi-objective robust optimizationmodel to deal with the problem of uncertainty in scheduling, considering the expectedperformance. They utilized normal boundary intersection technique to solve themulti-objective model and successfully produce Pareto optimal surfaces that wouldcapture the trade-off among different objectives in the face of uncertainty.

Data collection and analysis methodology

Building container cargo data and transportation network

There are 83,521 production/consumption (P/C) pairs (i.e. intermodal origin anddestination pairs) for container cargoes in Korea, as the Korean TransportationData Base (KTDB) consists of 289 traffic analysis zones (i.e. 2896 289 ¼ 83,521).For making the analysis simpler, this study chose the upper 65% of them for theanalysis. The P/C table of container cargoes was estimated from the O/D table ofcontainer cargoes by Kim (2010). Among six ports treating container cargoesincluding Busan, Incheon, Kwangyang, Pyeongtaek, Masan and Gunsan, threeports, Busan (67%), Incheon (15%) and Kwangyang (12%) were chosen. Twenty-one inland nodes were chosen as well. They were Kwangyang, Kwangju, Gumi,Gunsan, Daegu, Daejeon, Busan, Seosan, Seoul, Suweon, Ansan, Yangsan, Yeosu,

Figure 1. Simplified container transportation network of Korea.

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Yongin, Ulsan, Uiwang, Incheon, Jeonju, Changwon, Pyeongtaek and Hwaseong. Itwas assumed that seven inland nodes (Seosan, Seoul, Suweon, Yongin, Incheon,Pyeongtaek and Hwaseoung) transport container cargoes through Uiwang railroadstation where container cargoes clear the customs at the Uiwang inland containerdepot. The other 13 inland nodes send or receive container cargoes to/from their ownnearest railroad stations. The reason of using the P/C table rather than the O/D tableis to reflect the characteristics of the rail-based intermodal system which passes viainland container depot (ICD) or railroad station. It is assumed that containercargoes are exported or imported by two transportation modes: truck or rail-basedintermodal system (i.e. truck þ railway).

Table 1 shows the unit of travel time, logistics costs and CO2 emissions that areused for estimating the route’s overall travel time, logistics costs and CO2 emissionsof each P/C pair for trucking and intermodal container transportation system.Regarding the travel time, 1.22 minutes/km was used for truck main haul and shuttletransportation. In the case of railways, 60km/h was assumed to estimate the traveltime. For loading and uploading of container from/to truck or freight car, 120minutes was used. For the logistics cost estimation, container cargo surface fare listsdeveloped by the Korea Trucking Association (KTA, 2008) was used for trucking.That is, for trucking 1,726 won/km was assumed and for shuttle service 24,000 wonfor a service was applied. For railway, unit cost of 741 won/km developed by KoreanRailway Company was assumed. The unit cost for loading and uploading wasassumed as 10,500 won for a service for trucking and intermodal system. For theestimation of CO2 emission, a unit emission of 1240g/TEU-km, developed byMckinnon and Piecyk (2010), was applied in this study. It was assumed that no CO2

emission occurs with the transshipments of container cargoes.

Network assignment

The traffic assignment of container cargoes was conducted by the linearprogramming-based transportation problem. The linear programming-based trans-portation problem is a method of determining the amount of transported cargoes inthe transportation network between supplying points and demanding points in orderto minimize the total cost. Equation 1 shows the linear programming-basedtransportation problem.

minX

CijXij ð1Þ

s:t: Xm1 þ � � � þ Xmj þ � � � þ Xmn ¼ am

X1n þ � � � þ Xin þ � � � þ Xmn ¼ bn

where, Cij: Cost between zones i and jXij: Amount of cargo between zones i and jam: Amount of cargo generating in zone m, andbn: Amount of cargo arriving in zone n.

In this study, Equation (1) was implemented by Microsoft Excel program. Thecost term was replaced by three cost components (i.e. logistics cost, travel time andCO2 emissions) whenever needed.

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Table

1.

Unitsoftravel

time,

logistics

costsandCO

2em

issions.

Travel

time

Logistics

costs

CO

2Emissions

Highwaynetwork

–Loading/uploading:120minutes

–Travel

timeforhighway:1.22minutes/km

–Loading/uploading:10,500won

–Logistics

costsforhighway:1726won/km

–62g/ton-km

–1240g/TEU-km

Rail-based

interm

odalsystem

Shuttle

–Loading/uploading:120minutes

–Travel

timeforhighway:1.22minutes/km

–Shuttle

cost:24,000won

–Loading/uploading:10,500won

–CY

usagesost:10,000won

–26g/ton-km

–520g/TEU-km

Highway

–Loading/uploading:120minutes

–Travel

timeforhighway:1.22minutes/km

–Loading/uploading:10,500won

–Logistics

costsforhighway:1726won/km

–CY

usagecost:10,000won

Rail

–Travel

speed:60km/h

–Logistics

costsforRailroad:741Won/km

Sources:KOTI(2003);KTA

(2008);KoreaDevelopmentInstitute

(2004);KORAIL

(2005);KORAIL

(2010);KMI(2002);MckinnonandPiecyk(2010).

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Figure 2. Conceptual framework for searching Pareto optimal solutions. Analysis result andsensitivity analysis

Solution procedure

When Equation (1) is solved, in general the solution minimizes only one objectiverather than minimizing three cost components all together. It is therefore needed toidentify Pareto optimal solutions with respect to each cost component. Theprocedure for identifying Pareto optimal solutions considering logistics cost and CO2

emissions, as demonstrated in Figure 2, is outlined as follows:

Step 1: Obtain an initial solution which minimizes logistics cost.

Step 2: Estimate CO2 emission of the initial solution and set the CO2 emission of it asupper limit.

Step 3: Obtain another solution which minimizes CO2 emission and set the CO2

emission of it as lower limit.

Step 4: Set the required number of Pareto optimal solutions. In this study, 50 Paretooptimal solutions were identified.

Step 5: Estimate the increment of CO2 emission: Increment ¼ (upper limit – lowerlimit)/required number of Pareto optimal solutions.

Step 6: Update the upper limit of CO2 emission: New upper limit ¼ old upper limit –increment. At the very beginning of the procedure, the old upper limit was set asinfinity.

Step 7: Identify a new solution which minimizes logistics cost while satisfying a newupper limit of CO2 emission.

Step 8: If the number of identified Pareto optimal solutions is equal to the desirednumber of Pareto optimal solutions or the upper limit is equal to or less thanthe lower limit, stop. Otherwise go back to Step 6.

Analysis results

Relation between travel time and logistics costs

It was found that total logistics costs were at the minimum when the mode share ofthe rail-based intermodal system was 35%, and total travel time in the network wasat the minimum when the mode share of highway was 99%. Also, a trade-offrelationship was formed when the mode share of the rail-based intermodal systemwas in the range of one through 36%, and 113,340 won was needed for decreasing

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one hour in travel time (see Figure 3). Travel time using the rail-based intermodalsystem was longer than that using trucking because of loading/uploading time,shuttle service, CY(C ¼ container yard) waiting time, and freight railway’s schedule(or headway) in the railroad station. For example, for a 40-ft container, it takesabout 18 hours for the rail-based intermodal system while travel time of the truckingservice is six hours from Ansan, Kyeonggi province to a container terminal in Busan.

The rail-based intermodal system is disadvantageous in travel time andadvantageous in CO2 emissions compared with trucking. This type of relativeadvantage of the rail-based intermodal system in terms of CO2 emissions overtrucking in Korea is attributable for the fact that travel distances of trucking andrail-based intermodal system are very similar, due to the dense highway and railwaynetworks. Therefore, the trade-off relationship between travel time and CO2

emissions is observed. It was found that the decrease of one hour in travel time wasequivalent to the increase of 0.094 ton of CO2 emissions (see Figure 4).

Relation between logistics costs and CO2 emissions

The total logistics cost was found to be reduced when the mode share of the rail-based intermodal system was 36%, and total CO2 emissions in the network was atthe minimum when the mode share of the rail-based intermodal system was 82%. Inaddition, a trade-off relationship was observed when the mode share of the rail-basedintermodal system was in the range of 36 through 82%, and 688,000 won was neededin order to save one ton in CO2 emissions (see Figure 5). In general, trucking’slogistics costs were lower than thoset of the rail-based intermodal system. Thelogistics costs using highway network for some zone pairs with shorter traveldistance and lack of railroad system were significantly lower than those of using therail-based intermodal system. However, there were some pairs of zones where thelogistics costs of the rail-based intermodal system were lower than those of using

Figure 3. Relation between travel time and logistics costs. COST ¼ 71.8396TIME þ 2557.

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trucking. Overall, it was found that the total logistics cost was minimized when theshared ratio of the rail-based intermodal system was 36%. Freight transportationusing the rail-based intermodal system generally produces less CO2 emissions thanthat using trucking (see Table 1; CO2 emissions using trucking is 62g/ton-km whilethat of the rail-based intermodal system is 26g/ton-km). There were some pairs ofzones where CO2 emissions using the rail-based intermodal system were more thanthose of using trucking. It is because the travel distance using the rail-basedintermodal system was significantly longer than that using highway network. The

Figure 5. Relation between logistics costs and CO2 emissions.

Figure 4. Relation between travel time and CO2 emissions.

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total CO2 emissions in the network was found to be minimized when the mode shareof the rail-based intermodal system was 82%.

Relation of travel time, logistics costs, and CO2 emissions

One more factor was added on the network after assigning container cargoes usingthe two factors in order to simultaneously analyse the relationship of three factors. Itwas found that there was no superior solution. It is because, since the trade-offrelationship between travel time and CO2 emissions always existed, Pareto optimalsolutions always existed as well regardless of the level of logistics costs.

When three factors were taken into consideration at the same time, total logisticscost was minimized when the modal split of the rail-based intermodal system was36%. As the modal split of the rail-based intermodal system increased from 1% to36%, the travel time increased and the CO2 emissions were found to decrease (seeFigure 6). As the mode share ratio of the rail-based intermodal system changed from36% to 80%, the travel time and the logistics costs were found to increase and theCO2 emissions to decrease (see Figure 7).

Sensitivity analysis

Application of social logistics cost

To date out-of-pocket costs that the users of trucking or rail-based intermodalservice need to pay were considered as logistics cost. This study considered socialcosts as logistics cost. The social costs were assumed to be classified intoenvironmental cost, traffic crash cost, and congestion cost. The monetary unit valuefor each sub-item, estimated by Korean Transportation Institute (KOTI), as shownin Table 2, was utilized in this study. Since there is no trade-off relationship betweenlogistics cost and CO2 emissions when applying the concept of social cost as logisticscost, only the relation between travel time and logistics costs was analysed.

Figure 6. Relation among travel time, logistics costs and CO2 emissions. (Mode share of rail-based intermodal system is between 1% and 36%.)

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The total social logistics costs was at a minimum when the mode share of the rail-based intermodal system was 83%, and total travel time in the network was found tobe minimized when the mode share of the rail-based intermodal system was 99%.The trade-off relationship was formed when the mode share of the rail-basedintermodal system was in the range of 1 through 83%. It was also found that 270,000won is needed for decreasing one hour in travel time. It may be seen that themarginal rate of substitution of travel time with respect to the social logistics costs ishigher than that of travel time with respect to the out-of-pocket logistics costs (i.e.110,340 won vs. 270,000 won). It is because the social logistics cost is significantlygreater than the out-of-pocket logistics cost.

Discount in railway fare

Another analysis, under the condition that the railway fare is discounted by asmuch as 20%, would be performed. In this case, that the total logistics costs wereminimized when the mode share of the rail-based intermodal system was 39%.Remember that at the basic scenarios, the optimal mode share of the rail-based

Table 2. Monetary unit values of social cost items.

Social cost

Highway (won/ton-km) Railroad (won/ton-km)

Environmental cost Air pollution 29.1 6.9Greenhouse gas 17.7 1.5Noise 4.1 1.6Sub-total 50.9 10.0

Traffic crash cost 5.0 0.01Congestion cost 38.7 0.0

Total 94.6 10.0

Source: KOTI (2007).

Figure 7. Relation among travel time, logistics costs and CO2 emissions. (Mode share of rail-based intermodal system is between 36% and 82%.)

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intermodal system was 36%. Due to the discounted railway fare, the optimal modeshare of the rail-based intermodal system was found to be increased by as much as3%. Since the amounts of travel time and CO2 emissions are the same as the basicscenario, total travel time in the network was at the minimum when the mode shareof the rail-based intermodal system was 99%. In addition, the total CO2 emissions inthe network were at the minimum when the modal split of the rail-based intermodalsystem was 82%. The trade-off relationship between the travel time and the logisticscosts was observed when the mode share of the rail-based intermodal system was inthe range of 1 through 39%. It was also found that about 150,000 won was neededfor saving one hour in travel time (see Figure 9).

Figure 8. Relation between travel time and social logistics costs.

Figure 9. Relation between logistics costs and CO2 emissions (in case of 20% discount ofrailroad fare).

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The trade-off relationship between logistics cost and CO2 emissions was observedwhen the mode share of the rail-based intermodal system was in the range of 39through 82%. For saving 1 ton in CO2 emissions 684,000 won were needed (seeFigure 10). It is considered that lower logistics cost results in a relatively lowermarginal rate of substitution between logistics cost and CO2 emissions (i.e. 688,000won for the basic scenario vs. 684,000 won for the discounted railway-fare case).However, it was found that the reducing effect of CO2 emissions was not significantbecause the mode share of rail-based intermodal system was relatively highcompared with the basic scenario.

Concluding remarks

The quantitative relationship between CO2 emissions and freight logistics costs hasbeen gaining interests in the logistics field because of global warming as well asrapidly increasing fuel costs (Kim et al., 2009). Different from the previous studies,this study identified the relationships among three important decision-makingattributes in freight transportation: logistics costs, CO2 emissions and time. Thehighway and railway networks of Korea and some selected container cargoesproduction/consumption data were used as an input for the analysis. Trucking andrailway-based intermodal freight transportation systems were compared in thiscontext. The relationships were estimated by assigning container cargoes betweenproduction zones and consumption zones by solving the linear-programming basedtransportation problem.

This study clearly showed the trade-off relationships between attributes andfound out the conditions under which the trade-off relationships are formed. Thedesired levels of modal split of the railway-based intermodal freight transportationsystem with respect to different points of view were identified. Some of the majorfindings are as follows. First, logistics costs were minimized when the mode share ofthe rail-based intermodal system was 35%, and total travel time in the network was

Figure 10. Relation between travel time and logistics costs (in case of 20% discount ofrailroad fare).

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at the minimum when the shared ratio of highway was 99%. For saving 1 hour intravel time 113,340 won were needed. Second, the decrease of 1 hour in travel timewas equivalent to the increase of 0.094 ton of CO2 emissions. Third, the logistics costwas found to be at the minimum when the mode share of the rail-based intermodalsystem was 36%, and total CO2 emissions in the network was at the minimum whenthe mode share of the rail-based intermodal system was 82%. In order to decrease 1ton in CO2 emissions 688,000 won were needed. Fourth, when the social externalcosts were applied, the total social logistics cost was minimized when the mode shareof the rail-based intermodal system was 83%.

It is considered that these findings may be used as anchor points for setting policydirections, especially for the determination of the desired level of mode share of therailway-based intermodal freight transportation system. They are also considered tobe valuable when determining the level of subsidies for the shippers or carriers whoshift from trucking to the railway-based intermodal freight transportation system.

Although this paper analysed the quantitative relationship among contributionfactors of freight transportation, a number of issues still remain to be investigated infuture research. First, this study took into account travel time, logistics costs andCO2 emissions as contribution factors. However, there is still one of the most crucialfactors in logistics decision making to consider: minimizing the lead time or ensuringjust-in-time arrival. It could compensate with the fourth and fifth objectivefunctions. In addition, improving the unit costs of attributes, developing asupporting tool for decision making of the tax levy for CO2 emissions may bevaluable as a series of this study.

Acknowledegments

This research was supported by Basic Science Research Program through the NationalResearch Foundation of Korea (NRF) funded by the Ministry of Education, Science andTechnology (NRF-2011-0004371).

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