GREEN SUPPLY CHAIN: INTERMODAL TRANSPORTATION MODELING WITH ENVIRONMENTAL IMPACTS Didier ANCIAUX Kun YUAN Laboratoire de Génie Industriel et de Production Mécanique; Université Paul Verlaine-Metz, Ile du Saulcy, 57045 Metz Cedex 1-France 1. INTRODUCTION Nowadays we are faced with lots of environmental problems: the erosion of biodiversity, the exhaustion of resources, the disorder of climate, the affection of human health by pollution and so on, as a result of industrial development and human unconsciousness. In all these respects, it is necessary for us to reorientate the progress, to use our technologies differently, to produce with rationality and no more take the way unslung. It has become a necessity for us to count in the costs of production the factor of environmental costs as social costs. And that is what we proposed to develop in this paper: how to integrate the environmental impacts into the transport function within the supply chain? Actually, the transport function, without the consideration to environmental impacts, remains outside of the integration of production system and is normally managed by external service provider. In fact, the flows between companies and performance supports are dissociated from the functions of production. The almost exponential increase of flows between companies thus requires nowadays a global evaluation of the performance and management of industrial transport. We proposed to show the issues for integrating the means of transport within the green supply chain, as well as a decision-aiding model, which allows optimising the solution choice of intermodal transport problems. 2. GREEN SUPPLY CHAIN AND INTERMODAL TRANSPORT The globalisation of economy and the universalisation of the exchanges give birth to multi-site companies who own their own production centres and distributions centres, which distribute on great geographical areas. For very long time, the distribution of products with various modes of transportation is not taken into account in the management of supply chain (Erenguc et al., 1999). On the contrary, it is the external service provider of the supply chain who always manages it, but it does not support the measurement of the performance to control the cost. The discounted growth of the goods carriage per mode in European Union would encourage the decision makers to take measures to limit their use of it and especially to limit their environmental impacts. Actually, in an era with more environmental conscience on a global level, (Kyoto, Göteborg,…), the companies and service providers could no longer reject indefinitely on the community of environmental costs and will be, in all probability, subjected to heavy environmental tax in next years.

© Association of European Transport and contributors 2007

1

Figure 1: Anticipated increase of goods shipment by modes

2.1 Green supply chain The integration of the environmental cost of transportation in the supply chain is rarely quoted in the literature. In Beamon (Beamon, 1999), the author proposed to redefine the current supply chain with the integration of environmental constraints through the concept of “Supply Chain Environmental Management” (SCEM) or “Green Supply Chain”. This article justifies the integration of the constraint in the model by the current state of environmental situation, the evolution of the legislation opposition to the problems generated by pollution (EURO 4 for European Union, for example), and the public pressure which is increasingly attentive with the environmental problems and the actions for reducing the pollution. Beamon (Beamon, 1999) proposes a reorganisation of the supply chain, however, without approaching clearly the transportation modes. Bontekoning (Bontekoning et al., 2004) underlined the importance of choosing the transportation modes but little work is known on the calculation of transportation cost, taking account the impacts. Tsamboulas (Tsamboulas et al., 2000) proposes an approach that combines the multi-criteria analysis and the cost benefit analysis to evaluate the environmental impacts and costs of transport initiatives. In the article of Anciaux (Anciaux et al., 2005), it is taken into account the transportation modes within the framework of a green supply chain and the environmental impacts of the means of transport are integrated into the model as a cost aspect. This aid-decision model, which takes into account the intermodal transport, aims at defining the optimisation methods of transport from the view of economics or environmental performance. It thus proves the importance of considering the means of transport on strategic level for various reasons: to control better the economic share of transport during the product development while considering all the consequences related to transportation.

© Association of European Transport and contributors 2007

2

Figure 2: The impact wheel

2.2 Integration models of production/transport To choose the modes of transportation in the integration of production/transport, it is necessary to take into account the external environmental constraints, the technical constraints, the commercial constraints, as well as the impacts on costs, qualities and securities of all the means of transport contributor in shipment. The difficulty lies in the fact that different constraints cannot be taken sequentially. Each decision depends and influences, in varying degrees, other choices all along the supply chain. And finally, the logistic choice of transport constitutes the essential outcome of the market policy of the company (Hu Qinghe et al., 2001). Many models have been suggested for the integration of production/ transportation, however, they aim either at proposing the shortest way between the initial and the final terminals or at reducing the transportation costs while ensuring acceptable delivery time. In Barnhart (Barnhart et al., 1993), the authors propose an evaluation model for minimising the cost routing for each shipment with respect to total transportation and inventory costs. Duallaert (Dullaert et al., 2005) introduces an evolutionary algorithm for determining the optimal mix of transport alternatives to minimize total logistics costs, including order costs, transportation costs, and inventory costs. Janic (Janic, 2006) develops a model for calculating comparable combined internal and external costs of intermodal and route freight networks. There is also some job focus on the minimisation of handling times to guarantee the safety of the transported goods and people (Boussedjra et al., 2003). Bürckert (Bürckert et al., 2000) develops a model of road and rail transport, which

© Association of European Transport and contributors 2007

3

bases upon the artificial intelligence and multi-agent, to choose the most economical way and to plan the order. 3. THEORETICAL MODEL The model developed here allows us to evaluate the internal and external transportation costs. In this model, we distinguish the costs and the time of shipments for satisfying the demands of producers, the costs in terms of gas emissions, as well as the costs in terms of other environmental impacts. 3.1 Hypothesis We suppose that there is no intermediate storage for long duration and the various modes of transport can be employed successively. So it is possible to transport goods either directly (with only one means of transport) or indirectly (with at least two means of transport). The means of transport considered here includes: train, lorry, ship and airplane. The general model developed here allows taking into account the specificity of each means of transport mentioned above. 3.2 Shipment Cost For the intermodal problem, the evaluation requires the determination of the routing cost for each shipment with respect to total transportation costs (including the drayage costs and the line haul costs), inventory costs (the intransit inventory costs and the costs of additional safety stock, which is usually ignored in the routing decision) and transhipment costs. 3.2.1 Transportation Cost The transportation cost here includes the drayage cost and the line haul cost during all the routing period. In the model of cost, we are using the following parameters:

c ik , fix : Fixed transportation cost on zone k with transportation mode i;

c ik (dik ) : Distance-depending cost on zone k with transportation mode i, like crew’s wages, insurance and so on; c ik (t ik ) : Time-depending cost on zone k with mode i;

c ik (dik ,t ik ) : Distance and time depending cost on zone k with mode i; dik : Covered distance on zone k with mode i; t ik : Travel time on zone k with mode i;

Q : The total weight of the products to be transported; V : The total volume of the products to be transported;

CQi : Weight capacity of transportation mode i;

© Association of European Transport and contributors 2007

4

CVi : Volume capacity of transportation mode i.

And the transportation cost is expressed as follows:

C1,ik = (µ i + ξ i + (1 − ξ i ) (Q / A − µ i B) / B )[c ik , fix + c ik (d ik ) + c ik (t ik ) + c ik (d ik , t ik )] With, 0≤ξi ≤1

µi = (µ1i −1)

si

 Q   V   Q V  max , , ,  = max  CQi CVi   CQi  CVi 

sinon

µi = µ1i

 Q   V  ,   CQi  CVi  Q V Q A =1 si > , Sinon A = CQi CVi V Q V B=CQ, si > , sin on B=CV . CQ CV

µ1i = max

3.2.2 Intransit inventory cost Inventory costs are the sum of the intransit inventory cost and the cost of additional safety stock while the cost of safety stock is typically very small compared to the intransit inventory costs and is usually ignored in the routing decision. So we take only the intransit inventory cost into consideration. As intransit inventory cost is typically proportional to transit time, we define the intransit inventory cost as follows: C 2 , ik = N ⋅ c 2, i ⋅ t ik

With, N : Units of products to be transported; c 2,i : In-transit inventory cost of transportation mode i for products per unit of product per hour.

3.2.3 Transhipment cost At contain terminals from zone k to zone k+1, containers are transshipped from one mode of transportation to another. Different types of material handling equipment are used for transshipment. So the transshipment cost is defined as follows:

C3,ik = Cik , j k+1 × hk,k +1 × f × ( µci + ξ ci + (1− ξ ci ) (Q / A − µci ⋅ B ) /B

)

With,

Cik , j k+1 : Transshipment cost per container from zone k, mode i to zone k+1, mode j;

© Association of European Transport and contributors 2007

5

f : Fragility factor of products;

hk,k +1 : Transship factor between zone k and k+1;

hk,k +1 =1, if there is transshipment operation from zone k to zone k+1; hk,k +1 =0, if not. With 0 ≤ ξ ci ≤ 1;

µci = (µ1ci −1)

si

 Q   V   Q V  max , , ,  = max  CQci CVci  CQci   CVci 

sinon

µci = µ1ci

 Q   V  ,  CQci   CVci 

µ1ci = max

3.2.4 Number of transshipment The number of transshipment is strictly constrained in intermodal strategies if the goods transported is fragile or perishable, like vegetables, flowers and so on. For the number of transshipment, there are two points worth noting: firstly, we do not take into consideration the transshipment at the charging part at the beginning and the discharging part at the end of the supply chain because it is exactly the same procedure for each intermodal design; secondly, for simplifying the calculation, the number of transshipment is not calculated according to the times of transship operation of containers, but according to whether or not changing the transportation mode at the terminal of each corresponding zone. With the hypothesis above, we get K −1

M = ∑ hk,k +1 k=1

For fragile products, M ≤2 3.2.5 Total cost With the models presented above, we can get the shipment costs in zone k with transportation mode i is: 3

Cik = ∑ C j,ik j=1

And the total shipment cost is: K

I

C = ∑ ∑ Cik x ik k=1 i=1

© Association of European Transport and contributors 2007

6

I

∑x

ik

= 1,

∀k ∈ K

i=1

3.3 Environmental impacts Three types of environmental impacts are taken into our consideration for modeling: the air emission, the noise pollution and the risks. 3.3.1 Air pollution Air pollution of transportation has the most direct environmental effects: local air pollution, global atmospheric pollution, etc. The air emissions considered here include CO2, NOx, SO2, HC and dust. And the total emission is described as follows: K

I

J

Ig = ∑ ∑ x ik (µi + ξ i + (1− ξ i ) (Q / A − µi B) /B) ⋅ dik ⋅∑ ei,t (Q,V ) k=1 i=1

t=1

With, Ig: Total air emission during the shipment; ei,t: Unit of air pollution t in weight per unit of weight transported per unit of distance shipped by transportation mode i. 3.3.2 Noise pollution Noise annoyance is another environmental effect, which arises more and more attention nowadays. And the noise annoyance cost is described as: K

I

B = ∑ ∑ x ik Q.dik .β i k=1 i=1

With,

B : Noise cost of the total shipment period;

β i : Noise cost per unit of weight transported per kilometer. 3.3.3 Accident Risk The risks assessment is extremely important for the transportation of dangerous goods when selecting intermodal strategies. In our paper, the accident risk is evaluated as follows: K

I

Cr = ∑ ∑ x ik c rγ ik dik k=1 i=1

With, c r : Accident cost per unit of distance of transportation mode i in zone k;

γ i : Frequency of accident of transportation mode i in zone k. k

© Association of European Transport and contributors 2007

7

3.4 Time assessment The consuming time of the shipment is the sum of transportation time and transshipment time. K

I

T = ∑ ∑ x ik (t ik + hk,k +1 ⋅ f ⋅ t h,ik : j k+1 ) k=1 i=1

With,

t ik : Transportation time in zone k by transportation mode i;

t h,ik : jk+1 : Transshipment time from zone k, mode i to zone k+1, mode j. 4. IMPLEMENTATION AND RESULTS OF AN EXAMPLE The modal is applied to an intermodal transportation problem between Paris and Marseille as shown in the following figure. PARIS

LYON

MARSEILLE

776 315 357

464

277

776

464

357

277 315 660

357

391

315 315

Figure 3 : The application exemple

We suppose that: • 1000 tons of goods are supposed to be delivered from the Peugeot factory in Aulnay-sous-Bois in Paris to the industrial centre in Marseille. There are 12 possibilities for intermodal combination of transportation modes. And the distance statistics used in the model are based on the report and research of Air France, Michelin, SNCF and Google Earth. • The delivered loading units for train and ship are supposed to be 20 feet (a TEU or 20 feet equivalent unit) as is common in Europe. Each unit has an average gross weight of 12 tones of goods (European

© Association of European Transport and contributors 2007

8

Commission, 2001a). And the load of freight truck is defined as 30 tons per vehicle. • The transshipment costs depend largely on the location and capacity of terminals. We suppose here the transshipment cost is 27 €/ TEU at the rail terminal, 50€/TEU at the seaport and 45€ per loading unit on the airport (European Commission, 2001a). • The pollution statistics is defined according to the study (Knorr W., 2005). Adapting the parameters above to the model, we can get the calculation results as shown in the following table.

M

C1

C3

T

Ig

B

(euros) 0 2242,670 8 4153,094 0 1487,784 6 2242,670 8 6395,764 8 3730,455 4 2242,670 8 4153,094 0 5640,878 6 3730,455 4 1487,784 6 0

(hours) 38,8

(kg) 1838,70

(euros) 5742,4

5.90

50,85

1107,93

4463,8

3.55

58,8

1105,80

3448,4

3.54

31,35

2660,06

6098,7

3.73

54,16

53,1767

2638,6

0.165

72,43

58,1752

1655,0

0.180

46,62

1690,62

4549,5

0.619

52,43

791,074

3949,0

2.53

9,27

3660,80

6170,0

0.304

44,99

2235,76

3857,3

0.390

38,24

2294,07

5050,3

0.580

23,34

2889,78

5907,1

2.49

9,27

53,1767

1655,0

0.165

Route 01

0

(euros) 43445,6495

Route 02

2

27721,4576

Route 03

2

27750,0666

Route 04

2

33024,4890

Route 05

2

4994,72182

Route 06

4

5205,45214

Route 07

4

12816,7704

Route 08

2

20901,0351

Route 09

2

12319,8506

Route 10

4

11385,4888

Route 11

4

12598,2585

Route 12

2

25374,1759

Minimum

0

4994,72182

Cr

Table 1 : Modeling results

M: Number of Transshipment; C1 : Transportation Cost; C3 : Transshipment Cost; T: Shipping Time; Ig : Air pollution; B: Noise Pollution; Cr : Risk Assessment. With a comparison of the different cost and impacts of each route as shown above, for the dangerous and perishable product, path 6, 7, 10, 11 are excluded of the choice of intermodal transportation modes because the transshipments times are over 2. For the usual products, it is shown in the

© Association of European Transport and contributors 2007

9

table that path 5 is the best choice when we consider the criteria as transportation costs, air pollution and accident risks; path 9 is the first choice when the time saving comes firstly in the clients’ requests; and path 6 is the one with the least noise pollutions. Criteria Transportation cost Transship cost Time Air pollution Noise annoyance Risk

Route 05 01 09 05 06 05

Intermodal choice Truck-train-truck Truck Truck-airplane-truck Truck-train-truck Truck-train-ship-truck Truck-train-truck

Minimum value 4994,72 0 9,27 53,18 1655 0.165

Table 1 : Intermodal choisses according to criteria

5. CONCLUSION In this paper, we have developed a mathematic model integrated with the transportation function of a supply chain and its environmental impacts. With the model we proposed here, a supplier can make the most adapted routing decision according to his requirement: minimization of shipment costs, minimization of transshipment damage, minimization of shipping time, or minimization of environmental impacts. The decision response of each criteria is taken for transporter basing on the mathematic model proposed here. The model would be more practical with the development of different constraints, like the practice of each transportation mode, the discount of goods shipment, and time constraints. We have also adapted multi-criteria methods AHP and ELECTRE to our model: AHP is efficient to exclude the incompatibility and indifference between alternatives but its performance is not consistent for decision judgment; while ELECTRE escapes the procedure of determination of intrinsic parameters but it accepts the incompatibility and indifference between alternatives. In the future, we would build up another multi-criteria method, which are more adaptable and efficient for this problem and study the sensibility of parameters, both of the mathematical model but also of the multi-criteria method. References Anciaux D., Roy D. et Mirdamadi S. (2005) Un Modèle de Simulation d’une Chaîne Logistique avec prise en Compte des Moyens de Transports Intermodaux, Proceedings of 5ème Conférence Internationale sur la Conception et Production Intégrées, Casablanca, Maroc. Barnhart C., Donald Ratliff H. (1993) Modeling Intermodal Routing, Journal of Bussiness Logistics 14 (1) 205-223. Beamon B.M, (1999) Designing the Green Supply Chain, Logistics Information Management 12 (4) 332-342.

© Association of European Transport and contributors 2007

10

Bontekoning, Y.M., Macharis, C. and Trip, J.J. (2004) Is a new applied transportation research field emerging? A review of intermodal rail-truck freight transport literature, Transportation Research A (39) 1-34. Boussedjra M., Bloch C. and El Moudni A. (2003) Solution optimale pour la recherche du meilleur chemin intermodal, Proceedings of 4th Conférence Francophone de MOdélisation et SIMulation, Toulouse, France. Bürckert H-J., Funk P. and Vierke G. (2000) An intercompany dispatch support system for intermodal transport chains, Proceedings of the 33rd Hawaii, International Conference on System Sciences, Hawaii, USA. Dullaet W., Maes B., Cernimmen B., Witlox F. (2005) An evolutionary algorithm for order splitting with multiple transport alternatives, Expert Systems with Applications 2005 (28) 201-208. Erenguc S. S., Simpson N. C. and Vakharia A. J. (1999) Integrated production/distribution planning in supply chains: An invited review, European Journal of Operational Research 115 (2) 219-236. European Commission (2001) Real Cost Reduction of Door-to-door Intermodal Transport – RECORDIT, Proceedings of European Commissions, Directorate General DG VII, RTD 5th Framework Programme, Brussels, Belgium. Hu Qinghe, Arun Kumar and Zhang S. (2001) A bidding decision model in multi-agent supply chain planning, International Journal of Production Research 39 (15) 3291-3301. Janic M. (2007) Modeling the full costs of an intermodal and road freight transport network, Transportation Research Part D (12) 33-44. Knorr W. (2005) EcoTransIT: Environmental Methodology and Data, Institut für Energie- und Umweltforschung Heidelberg GmbH. Tsamboulas, D. and Mikroudis, G. (2000) EFECT - evaluation framework of environmental impacts and costs of transport initiatives, Transportation Research Part D (5) 283-303.

© Association of European Transport and contributors 2007

11