Decision making of oil spill contingency options with ... · Decision making of oil spill contingency options with fuzzy comprehensive evaluation ... during oil spill response management

Water Resour Manage (2007) 21:663–676

DOI 10.1007/s11269-006-9031-5

ORIGINAL ART ICLE

Decision making of oil spill contingency optionswith fuzzy comprehensive evaluation

X. Liu · K. W. Wirtz

Received: 2 August 2005 / Accepted: 8 May 2006C© Springer Science + Business Media B.V. 2006

Abstract Accidental oil spills are one of major sources to affect ecologically and econom-

ically sensitive marine areas and shorelines. The aim of decision making during oil spill

response management is to minimize pollution effects in coastal areas, once spills occur.

However, not all coastal areas at risk can be saved due to a limitation of equipments or

options. Thus, often preferences between different coastal areas or uses, respectively, have to

be made in an operational way. Such a management issue is here taken as a multi-group multi-

criteria decision making problem involving a variety of stakeholders and natural dynamic

environments. For solving such a complex problem, this paper targets efforts to integrate

a second order fuzzy comprehensive evaluation (FCE) method and consensus facilitating

techniques into computerized group decision support system. Such a DSS takes into account

the influence of multiple criteria and the knowledge of different interested groups and sim-

ulates a consensus based decision process. Through a case study of the Prestige accident

off the Spanish coast in 2002, it is demonstrated that the model provides a simple, effec-

tive and adaptable method to solve operational management problems related to complex

human nature interactions as realized during oil spill management. Moreover, a series of

analyses explore potentials and limitations of the FCE for further applications in the field of

multi-group multi-criteria decision-making.

Keywords Decision making . Oil spill . Fuzzy comprehensive evaluation . Multi-criteria .

Group consensus

1. Introduction

An ongoing concern of an integrated coastal zone management (ICZM) in many areas around

the globe is related to the preparedness against accidental oil spills. It is estimated that tankers

X. Liu (�)ICBM, Carl von Ossietzky University, 26111 Oldenburg, Germanye-mail: [email protected]

K. W. WirtzInstitute for Coastal Research, GKSS, 21501 Geesthacht, Germany

Springer

664 Water Resour Manage (2007) 21:663–676

release up to 420,000 tons of oil in 2003 throughout the world (ITOPF) and the major oil

spills still occur at irregular intervals (European Environmental Agency, 2001), although

counter acting environmental programs and policies have been strengthened. Among others,

their effectiveness is constrained by, firstly, the dramatic increase of oil-related production

and transportation; secondly, some prevention measures only partially stop oil spills. For

example, both ship owners and environmentalists argue that yet almost invariably spills are

caused not by collisions but by running aground so that a rush to decommission old hulls

alone is not sufficient as evident from the Exxon Valdez and the Erika tanker accidents

(Rowe, 2004). Thus, responding to an emergency related to spilled oil or chemicals in an

effective way turns out to be a more critical concern in the domain of integrated coastal zone

management than ever. An oil and chemical spill and its long lived consequences can pose a

major impact on sensible coastal areas. A golden rule of oil spill contingency management

is, therefore, to remove as much oil as possible from the sea surface in order to minimize

the onshore impact. Traditional responses consist of mechanical (e.g., booms and skimmer)

and chemical methods (e.g., dispersants). However, both the relative impact and feasibility

of these response methods depend on the circumstance of the oil spill, the weather condition

and the time window. Except of these methods, an effective alternative is to tow the spilled

vessel towards the open sea or to an isolated harbour. Since the generally limited applicability

of counter-measures necessitates the set-up of operational preferences as it is not feasible

to save all areas under risk with the same reliability. This alternative aims to redistribute

less oil into sensitive habitats and can be carried out alone or simultaneously with other

response methods. A highly debated and recent example is the Prestige accident off the coast

of Spain in November, 2002. This accident caused a serious public anger due to the fact that

the break-up of spill cargo resulted from an abrupt change in towing direction as made by

the Spanish government. After the Prestige emitted its first emergency call, a right selection

among several options, such as towing it towards the open sea and to harbours should have

been done immediately to minimize oil impacts on the coastal zone.

1.1. Multi-group multi-criteria decision making problem

The decision making, such as a ruling destination of a spilling ship, is a typical example of a

complex problem involving a variety of stakeholder interests and complex dynamic environ-

ments. Therefore, an oil spill contingency management can be interpreted as a multi-group

multi-criteria decision making problem. In response to a priori existing legislation and local

interests, a sound decision should not only be based on the viewpoint of decision makers,

but also reflect opinions of groups such as fishermen and environmentalists emphasizing the

conservation of already protected marine or coastal environments. The situation becomes

more complicated since the diverse ecological or economic functioning of areas under risk

and several incomplete information layers exist. Thus, the decision making process should

incorporate two main integration aspects: (1) integration between human and the natural en-

vironments (vertical integration), that is, putting human knowledge into the natural resource

management, and (2) integration across interested stakeholders (horizontal integration). The

vertical integration focuses on the relationship between human resource use and natural envi-

ronments. To realize the vertical integration, Wirtz et al. (2004) have used a straightforward

weighted sum model (WSM) for the oil spill contingency problem, which aids individuals

to find their own preferred solutions. It also ensures that individuals have confidence in the

provided solutions by carrying out an uncertainty analysis. But there exist two weaknesses in

WSM with regard to the vertical integration. One is related to its capacity to deal with impre-

cise data. Normally the collected data contains both robust information and uncertainties. A

Springer

Water Resour Manage (2007) 21:663–676 665

satisfactory outcome from WSM highly depends on a pretty good quality of the collected data

in which uncertainties are low. The second weakness derives from its incapacity to deal with

lexical data, for example, when stakeholders express their opinion about the importance of

each criterion in a qualitative way. In this paper, we hence adopted a method based on fuzzy

logic theory. Many experts (e.g., Carlsson, 1999; Triantaphyllou, 2000) argued that fuzzy

logic techniques provide a powerful mathematical and methodological basis for dealing with

the representation of complex physical world problem (e.g., an environmental impact of a

human action) and lexically imprecise and incomplete knowledge. Based on such benefits, a

two step fuzzy multi-criteria decision making (MCDM) method, called a second order fuzzy

comprehensive evaluation model (FCE), is expected to provide a high level of confidence

for the selection of the optimal towing direction, particularly when data are imprecise and

stakeholders’ opinion are described qualitatively. Many successful examples verify the appli-

cation of the fuzzy MCDM method into the field of coastal and environmental management.

William et al. (2004) used multi-attribute analysis with fuzzy set theory to evaluate the coastal

landscapes. Yu et al. (2004) used the fuzzy decision making model to analyse alternatives for

the reservoir flood operation. In making a sediment management plan, Stansbury et al. (1999)

found an optimal option by using fuzzy method. Gurocak and Whittlesey (1998) developed a

fuzzy method for fishery management. An evaluation of alternative wastewater systems with

fuzzy technique proposed by van Moeffaert (2002) has been used in Surahammar, Sweden.

The horizontal integration pays more attention to the interaction between different groups

involved. It aims to build a consensus in which a variety of stakeholders are involved to

settle multiparty disputes. A number of methods including voting, negotiation, mediation

and arbitration can, in principle, be used to develop a mutually acceptable solution. In this

paper, stakeholders with different interests are simulated, which participate in reaching a

consensus through a voting system. This additional feature of participation consensus building

was missing in the previous work done by Wirtz et al. (2006) and here is developed to

minimize potential political conflicts related to operational responses in a pre-emptive mode.

Furthermore, it helps to identify appropriate strategies in compliance with an ICZM process

involving local stakeholders before the eventual onset of a crisis.

1.2. Outline of the methodology

The FCE method used in this paper can be seen as an integral part of a computerized group de-

cision support system (CGDSS) to aid users to make a structured decision. This study focuses

on the multi-criteria evaluation with an involvement of fuzzy knowledge and consensus

making mechanisms employed, and how the combined method applied. A brief methodol-

ogy scheme is outlined in Figure 1. Response alternatives and impacts of oil pollution are

retrieved from an environmental simulation model. To assess the performance of each alter-

native option, a second order fuzzy comprehensive evaluation (FCE) is proposed. Through

such an evaluation, a ranking by each interested group is taken into account. To reach an

agreement that represents a majority view, consensus building mechanism and stakeholders

are involved. Finally, a comparison with an application of a WSM to the same problem is

made and rankings resulting from different weighting schemes are statistically analysed.

2. Some notes on FCE and consensus building mechanism

Like previous studies using different WSM evaluation methods by Wirtz et al. (2006) also

Wirtz et al. (2004), this paper sets the focus on the accident of Prestige, which carried 77,000

tons of oil split in half off the northwest coast of Spain. At the initial Prestige spill site

Springer


Fig. 1 Main components of a computerized group decision support system

we define five different contingency alternatives of towing the spilling vessel: direction NE,

NW, W, SW and E (Wirtz et al., 2006). As explained in more detail by Wirtz et al. (2006),

towing was the only feasible response to the imminent break-up of the tanker. Among these

five alternatives, option NW, W and SW denote a strategy where the vessel is transported

offshore. Option NE and E, on the other hand, stand for towing the ship into one of the

local harbours (La Coruna or Fisterra). For each of the contingency options, a simulation

model called OSCAR (Oil Spill Contingency and Response) developed by SINTEF Norway

(Aamo et al., 1997; Reed et al., 1996), is applied to calculate and record the distribution

of the oil pollution reaching the coastal regions and the residual oil in the open sea. As

simulation results depended on incomplete information associated to hydrodynamic and other

factors, forecasts bear general uncertainties which, to some extent, result in imprecise data.

A detailed description of the simulation set-up can be found in Wirtz et al. (2004, 2006) and

Wirtz et al. (2004). Simulation results (see Appendix 1) show that there exist conflicts in the

selection of alternatives between hypothetical stakeholders. For example, option E, the route

to the harbour of Fisterra, is preferred according to the interest of environmentalists, since it

redistributes a less amount of oil entering into the local protected area, while option NW is

the best choice for the local fishery and tourism. To choose the best one among a discrete set

of alternatives, multi-criteria decision making (MCDM), a widely used technique, provides

decision makers a practical solution. In the field of MCDM, a fuzzy MCDM is often verified

to be capable of dealing with imprecise data and lexical information. Thus, a second order

FCE proposed by Ji et al. (2000a, b) is applied to evaluate the performance of each alternative.

The second order FCE proceeds along three main steps which are summarised as follows, (1)

defining a fuzzy set; (2) the 1st order fuzzy evaluation and (3) the 2nd order fuzzy evaluation.

2.1. Defining a fuzzy set

The five alternatives are assessed with respect to a set of common criteria. These can be

regarded as representative for many coastal regions around the world with their specific

economic uses (mostly fishery and traffic harbours) and ecological value. They reflect existing

interests as well as existing background information at the Galician coast. With special regard

paid to oil pollution, the following criteria are defined: the fishery (F), the tourism (To), the

transportation (Tr), the mariculture (M), the residual risk (RR), the reproduction (RA), the

Springer


Table 1 Overview of decision criteria, their descriptions and fuzzy grade divisions. NFG: the number offuzzy grades; VSD: very seriously destroyed; SD: seriously destroyed; MSD: middle-scale destroy; SLD:slightly destroyed; VSLD: very slightly destroyed

Criteria Fuzzy grade NFG/from . . . to . . . Description (summed amount of oil)

F u1 4/from SLD to VSD In the four principal fishery areas offshore the North

Spanish coast

To u2 5/from VSLD to VSD In the main recreation areas along the coastline of

North Spain

Tr u3 4/from SLD to VSD In the port of Coruna and Fisterra

M u4 5/from VSLD to VSD In the ten most important areas for mariculture

along the coastline of North Spain

RR u5 3/from SLD to SD In the open sea

RA u6 4/from SLD to VSD In Ramsar areas with special importance as

spawning and breeding areas

PeA u7 3/from SLD to SD In areas which are highly vulnerable because of

their coastal morphology

PrA u8 5/from VSLD to VSD In national and international protected natural areas.

The value of oil is weighted with a factor of 1, 2 or

3 linked to the number of protection extraditions

persistence (PeA) and the protection (PrA). The vector of the selected criteria is,

u = {u1, u2, u3, u4, u5, u6, u7, u8} = {F, T o, T r, M, R R, R A, PeA, Pr A} (1)

Using the criteria vector and records of simulated towing scenarios from OSCAR, we con-

struct a performance matrix in which each element represents an amount of oil pollution in

tons for each effective criterion against each of the alternatives (see Appendix 1). To evaluate

the performance matrix by a FCE, firstly different pollution grades are set with regard to each

criterion. These pollution grades are described lexically and qualitatively. An overview of

main effective criteria, their descriptions and fuzzy grades is given in Table 1. Secondly, we

link the continuous amount of oil pollution to the fuzzy grades by using a fuzzy membership

function. This function transforms the model generated sum of oil into a degree vector (Ai )

corresponding to the fuzzy grades predefined for each criterion.

Ai = (μi1, μi2, . . . , μini ) μi j ∈ [0, 1] (2)

where i = 1, 2, . . . , 8; j = 1, 2, . . . , ni and μi j is the fuzzy membership of the jth grade

of the ith criterion.

μi j = max

(min

(xi − e1

i j

e2i j

− e1i j

, 1,e4

i j− xi

e4i j

− e3i j

), 0

)(3)

where e1,...,4

i jare four scalar parameters for the jth fuzzy grade of the ith criterion. Details and

justification of the membership function can be see in Liu and Wirtz (2005).

Springer


2.2. First-order fuzzy evaluation

Coastal areas are not damaged in a uniform way, as oil strands onshore. To aid decision

makers to identify the damage degree, equally spaced damage levels ranging from 0 to 1 are

predefined, which imposes a numeric measure on the performance of alternatives.

ς = {ς1, ς2, . . . , ς11} = {0, 0.1, . . . , 0.9, 1.0} (4)

where 0 represents no damage, while 1.0 denotes a completely destroyed coastal area. For

any criterion i, a first order fuzzy degree assignment matrix (Ri ) is constructed, in which each

element represents a fuzzy degree of each kind of damage grade against each of these 11

coastal damage levels. Their values are somewhat empirical and can be modified for a specific

application. An intuitive example can be seen in the Appendix 2, in which the predefined

first order fuzzy degree assignment matrix for fishery (R1) is shown.

After combining the pre-defined first order fuzzy degree assignment matrix (Ri ) and the

fuzzy degree vector (Ai ), the first-order FCE set for criterion i can be obtained through the

following formula:

Bi = Ai × Ri = (bi1, bi2, . . . , bind) (5)

where nd = 11, i ∈ [1, 8], Bi represents a fuzzy coastal impact performance with respect to

the ith criterion.

2.3. Second-order fuzzy evaluation

In practice, not all the criteria (i.e. F, To, Tr, M, RR, RA, PeA and PrA) are equally important.

Hence, a weighting vector W is used to adjust the importance of the criteria,

W = (w1, w2, . . . , wm) (6)

where W denotes the degree of importance of each criterion. W can be expressed quantitatively

or qualitatively by representatives of different interested groups such as policy makers and

fishermen. It is difficult to ask stakeholders from different backgrounds to assign a numeric

value for each criterion. Thus, in this study stakeholders are supposed to present their opinions

for importance of each criterion lexically. The following equation is used to transfer the lexical

information to numeric weight value:

W = C × M (7)

Where C = [0, 0.1, 1] and M is a weighting matrix (see Appendix 3). Through this calcula-

tion, the qualitative description of importance for each criterion is transformed to numerical

numbers of weight values. An example of calculating the weight value is given in Appendix 3.

Then, by multiplying W by B, one gets the second order FCE set (K),

K = W × B = (k1, k2, . . . , kp) (8)

Springer


Finally, by combining Equation (4) and (8), then by dividing by the sum of K, the overall

coastal impact performance (CIP) can be determined:

CIP =nd∑

p=1

kpςp

/nd∑

p=1

kp (9)

2.4. Making a consensus

It is evident that building a consensus constitutes a central process within an integrated

coastal management (Poitras et al., 2003; Vasseur et al., 1997). However, time scale of oil

spill requires a rapid decision and limits the use of a participation process for stakeholders.

One suggestion is to survey the preferences of stakeholders in a priori. Stated preferences

are then tested in a virtual case. Due to the simple structure of a voting system and its fair,

i.e., democratic nature, we carry out the group choice mechanism of voting to build a model

consensus among different interested groups. In addition, within a voting system there are

many algorithms used to aggregate group members’ preferences into a group consensus.

There exist three approaches: (a) Borda-Kendall method (B-K), where ranks are averaged;

(b) approval method where deriving a group consensus is to define the alternative with the

highest number of the top two as a winner; and (c) Cook and Seiford distance method, where

the smallest distance is examined which an ordering can offer with respect to any ordering

of the alternatives (Massam, 1993). In order to produce a widely accepted consensus and

minimize uncertainties introduced by the consensus algorithms, these three approaches are

carried out in parallel and correlations between their results are examined as well.

3. Results and discussions

3.1. Comparison with non-fuzzy method

The three aggregation methods produce different group consensuses. According to correlation

analysis, it is observed that the B-K method yields similar outcomes by comparison with the

other two methods, although the consensus results produced by Approval and Cook-Seiford

distance slightly differ from each other. The high positive correlation between B-K method

and others allowed us to produce a group consensus without introducing much uncertainty.

Compared with the weighted sum model (WSM) used previously by Wirtz et al. (2004),

FCE give a consistently result. Figure 2 focuses on the comparison of ranking results. It is

obvious that both methods produce consistent results, namely the top two preferred alterna-

tives are E and NW, then followed by NE and W and the worst case is SW, if we compare

the mean rank of five alternatives. Although the alternative E outranks NW with respect to

the mean rank value, the overlaps between them suggest they are both evaluated similarly.

3.2. Classification of alternatives

To make the best choice out of a given set of alternatives, not only the rank average but also

its standard deviations should be considered. However, in practise not always exists an option

that performs best on both so that it is helpful to further classify options into an optimal and

suboptimal set. Given that an overall optimal solution is not feasible, manager may easily

Springer


Fig. 2 Ranking results derived from FCE and WSM by Wirtz et al. (2006). For each option mean rank isrepresented by histograms and again uncertainty is indicated by the standard deviation of ranks associatedwith each mean rank value

find a substitute in its original group. If we treat mean ranks and standard deviations equally

important an Euclidean metric can be employed to calculate the difference between options

oi and o j ,

d(oi , o j ) =(

2∑m=1

(o

m

i− o

m

j

)2

) 12

(10)

where o1i refers to the mean rank and o2

i to the standard deviation of option oi . Options can

be classified into one group, if their distance is minimal in a set of distances between them

and other options, such that groups with one, two or more members will emerge as shown in

Figure 3. There, the classification of towing alternatives in the Prestige decision problem was

made by two different models, FCE and WSM. The algorithm leads to three groups in both

cases. Group one (best performing group) is characterized by a low mean rank and standard

deviation, group two (intermediate performing group) is characterized by intermediate mean

ranks and relatively high standard deviation, while group three (worse performing group)

performs worse on both issues. In contrast to the classification based on FCE ranking results

which strictly satisfies these grouping features (Figure 3a), group three for the WSM based

ranks has a low standard deviation value (Figure 3b).

3.3. Statistical tests

Generally, the rank of an alternative is affected by weight profiles which, in turn, are de-

fined by the relative influence associated to all interest groups involved. In this paper,

Springer


Fig. 3 Classification of alternatives, (a) is based on the result from FCE. (b) is based on the resultfrom WSM. BG: best performing group; IG: intermediate performing group; WG: worse performinggroup

the importance level of each criterion can be described in three levels: important, rele-

vant and unimportant. If the importance level of one criterion is fixed, there are 2187

(3∧7) possible combinations of the remaining seven criteria. In our experiments, we cal-

culated the mean rank of 2187 scenarios for each alternative and for each importance

level of the selected criterion. During the statistical tests, the following definitions are

used:

(1) Positive criterion/Negative criterion (PC/NC): if a selected criterion whose importance

level ranging from unimportant to important may promote/degrade the mean rank of an

alternative, such a criterion is defined as a positive/negative criterion for this alternative.

(2) Null hypothesis (Ho): for a certain alternative, the importance level of one selected

criterion (ui ) ranging from important to unimportant will not lead to a change of its mean

rank (e.g. there is no ranking effect due to prevalence changes).

Figure 4 maps the sensitivity of rankings with respect to a change in importance levels

for all criteria. For example, the mean rank of alternative NE promotes from 3.25 to 2.9 as

the importance of criterion fishery ranges from level “important” to “unimportant”.

For each alternative, the null hypothesis is tested repeatedly with respect to each criterion.

Only two cases accept the null hypothesis, i.e. for alternative NE the criterion PrA and for

Springer


Fig. 4 Sensitivity test for each alternative in terms of each criterion. The importance of each criterion isdefined in three different levels. Their changes contribute to the variation of mean rank of each option. ∗∗significant at α = 0.1. 5) ∗accept the null hypothesis (see Table 2 for details)

Table 2 Overview of the statistical tests against the null hypothesis (for each alternative, the most criticalcriterion is given in bold numbers)

Alternative

NE NW W SW E

Abs. Abs. Abs. Abs. Abs.

Criteria (t) PC/NC (t) PC/NC (t) PC/NC (t) PC/NC (t) PC/NC

F 16.41 NC 16.22 PC 8.58 PC 6.34 NC 5.11 PC

To 1.88∗∗ NC 5.99 PC 19.51 PC 21.21 NC 0.80∗ PC

Tr 40.91 NC 9.87 NC 8.66 PC 22.78 PC 31.86 PC

M 9.87 NC 4.65 PC 5.21 PC 7.42 NC 11.73 PC

RR 22.73 PC 44.74 NC 31.15 NC 8.30 PC 29.64 PC

RA 30.25 PC 21.05 PC 12.59 NC 20.81 NC 29.25 NC

PeA 22.29 PC 7.10 PC 14.19 NC 17.41 NC 8.33 NC

PrA 1.06∗ NC 7.46 NC 9.99 PC 13.86 NC 14.28 PC

Notes. (1) Abs. (t): absolute value of student t. (2) PC/NC: positive criteria or negative criteria. (3) Numberwithout any mark, significant at α = 0.01. (4) ∗∗significant at α = 0.1.5) ∗accept the null hypothesis

Springer


alternative E the criterion To (Table 2). All other cases indicate that a change relevance of

a specific criterion has a significant effect on the mean rank at a 99% or 90% confidence

level. For alternative NE, SW and E, the most critical criterion turns out to be transportation

(Tr) as might be expected since these routes infer the highest risks for Galician harbors.

Alternatives NW and W are most sensitive with respect to the residual risk (RR) since a

higher amount of the oil is simulated to remain in the open sea. Table 2 summarizes all

sensitivities of the rank ordering together with the sign of change due to altered relevance.

For alternative E six criteria have a positive effect, while for alternative SW six criteria

have an opposite negative on the ranking. To some extent, the number of positively sen-

sitive criteria determines the performance of an alternative. We hence conclude that alter-

native E is likely to outrank SW even independently from calculating their absolute rank

values.

4. Conclusions

Although the FCE based methodology outlined here is applied to a single case study only we

tempt to generalize some of the results, in particular regarding an effective decision making

during oil spill response management. The comparison of the virtual consensus making here

and the actual history of the Prestige disaster with all its political consequences motivate for

a number of recommendations, which are more far reaching than a hindcast of an optimal

towing route. A framework in which simulation and environmental data are combined with

evaluation techniques and a voting mechanism like presented in this paper can be a first step

as it helps to structure the problem. For an oil spill abatement at the Spanish coast, eight

environmental and economic related criteria were chosen which to some extent should also

be relevant for other resource use conflicts in the coastal zone. With a simulated participa-

tion of interested groups, the fuzzy methodology demonstrated its capability of dealing with

linguistic information (e.g. the interested group’s qualitative description of relative impor-

tance for each decision criteria) in a comprehensive and stable way. In addition, like most

other methodologies the FCE approach requires a technical understanding of internal setups

such as the selection of the membership functions, the number of fuzzy grades for a specific

criterion or the number of the damage levels and. It is therefore important that the DSS

structure is tested against internal degree of freedom as well (Liu and Wirtz, 2005), while

regarding the real world uncertainty it should also produce a coarse, “fuzzified” evaluation

output. Classifying alternatives into different groups such as the optimal and the suboptimal

solutions group helps manager to identify possible substitutes when an optimal option has to

be rejected for reasons not covered by the DSS. Statistical tests and related analyses showed

the potentials of the FCE model for solving multi-agent multi-criteria decision making prob-

lem. Based on the comparisons with the non-fuzzy method presented by Wirtz et al. (2006),

the FCE model yields similar results in the case study of the Prestige accident which are not

matched by the decision actually taken by the authorities. This should motivate for a wider

implementation of DSS along coastal areas under risk or pressure in order to strengthen a

more rationale, transparent and, this way, also socially sustainable decision making. And of

course, decision makers should not completely rely on the computer-based results by con-

straining the final decision to the DSS based ranking of alternatives (Lahdelma et al., 2000).

They always should and will continue to maintain the freedom to deviate from a prescribed

solution and may inspire suggestions for new alternatives (Ozernoy, 1984; Lahdelma et al.,2000).

Springer


Appendices

(1) A performance matrix, simulation results from OSCAR, is weighted by a set of specific

probabilities of ship sinking (PSS). PSSNE = 0.004; PSSNW = PSSW = PSSSW = 0.009;

PSSE = 0.002.

NE NW W SW E

F 34.5 18.5 29.5 74.7 19.1

To 27.4 15.3 24.9 60.1 15.8

Tr 17.8 5.2 9.2 3.5 1.4

M 41.5 26.6 38.5 78.0 24.1

RR 55.3 178.5 168.2 114.3 20.3

RA 0.3 0.3 0.4 4.4 0.7

PeA 0.5 0.6 1.3 5.9 0.8

PrA 30.1 24.0 31.0 72.9 18.7

(2) The example of the first order fuzzy assignment matrix for fishery (R1) is defined as

follows,

R1 =

⎡⎢⎢⎣0.4, 0.6, 0.8, 1.0, 0.8, 0.6, 0.4, 0, 0, 0, 0

0, 0, 0.4, 0.6, 0.8, 1.0, 0.8, 0.6, 0.4, 0, 0

0, 0, 0, 0, 0, 0.4, 0.6, 0.8, 1.0, 0.8, 0.6

0, 0, 0, 0, 0, 0, 0, 0.4, 0.6, 0.8, 1.0

⎤⎥⎥⎦(3) The construction of M depends on: (1) the description of importance of each criterion by

an interested group and (2) a reference weighting matrix shown as follows,

Level of

importance I1 I2 I3

0 0 0.05 0.95

0.10 0.05 0.90 0.05

1.0 0.95 0.05 0

An example of qualitative description of relative importance for each criterion is given as,

Criterion Importance

u1 F I1 Important

u2 To I1 Important

u3 Tr I1 Important

u4 M I2 Relevant

u5 RR I3 Unimportant

u6 RA I1 Important

u7 PeA I2 Relevant

u8 PrA I3 Unimportant

Springer


Then, according to the reference weighting matrix, the derived weighting matrix M is as

follows,

M =⎡⎣ 0 0 0 0 0 0 0 0

0.05 0.05 0.05 0.90 0.05 0.05 0.90 0.05

0.95 0.95 0.95 0.95 0.95 0 0.95 0

⎤⎦Using Equation (7), it follows that,

W = (w1, w2, w3, w4, w5, w6, w7, w8)

= (0.955, 0.955, 0.955, 0.14, 0.05, 0.955, 0.14, 0.05)

Practitioners may normalize this vector to let its sum equal to 1.

Acknowledgements We thank SINTEF Marine Environmental Technology for the permission to use theOSCAR software. This work is supported by the German Federal Ministry of Education and Research (BMBF)and the Land Niedersachsen. The authors would like to thank Susanne Adam for OSCAR simulations andanonymous reviewers for their constructive suggestions.

References

Aamo OM, Reed M, Lewis A (1997) Regional contingency planning using the OSCAR oil spill contingencyand response model. In: Proceedings of the 20th Arctic and Marine Oil Spill Program. AMOP TechnicalSeminar, Vancouver, Canada

Carlsson C (1999) Soft computing: Modelling technologies and intelligent systems nimitrack. In: Proceedingsof the 32nd Hawaii International Conference on System Sciences

Gurocak ER, Whittlesey NK (1998) Multiple criteria decision-making: a case study of the Columbia Riversalmon recovery plan. Environmental and Resource Economics 12:479–495

ITOPF: International Tanker Owners Pollution Federation Limited. http://www.itopf.com/stats.htmlJi S, Li X, Du R (2000a) Tolerance synthesis using second-order fuzzy comprehensive evaluation and genetic

algorithm. International Journal of Production Research 38:3471–3483Ji S, Li X, Ma Y (2000b) Optimal tolerance allocation based on fuzzy comprehensive evaluation and genetic

algorithm. International Journal of Advanced Manufacturing Technology 16:451–468Lahdelma R, Salminen P, Hokkanen J (2000) Using multicriteria methods in environmental planning and

management. Environmental Management 26:595–605Liu X, Wirtz KW (2006) Consensus oriented, fuzzified decision support for oil spill contingency management.

Journal of Hazardous Materials 134(1–3):27–35Lootsma FA, Boonekamp PGM, Cook RM, van Oostvoorn F (1990) Choice of a long-term strategy for

the national electricity supply via scenario analysis and multi-criteria analysis. European Journal ofOperational Research 48:189–203

Massam BH (1993) The right place: shared responsibility and the location of public facilities. Longman Group,Uk Limited

Ozernoy V (1984) Generating alternatives in multiple criteria decision making problems: A survey. In: HaimesY, Chankong V (eds) Decision making with multiple objectives. Springer-Verlag, NY

Poitras J, Bowen R, Wiggin J (2003) Challenges to the use of Consensus Building in Integrated CoastalManagement. Ocean and Coastal Management 46:323–333

Reed M, Aamo OM, Dowing K (1996) Calibration and eesting of IKU’s oil spill contingency and response(OSCAR) model system. Proceedings of the 1996 Arctic and Marine Oil Spill Program (AMOP) TechnicalSeminar, pp 689–726

Rowe DG (2004) Breaking up is Hard to do. Nature 429:800–802Stansbury J, Member PE, Bogardi I, Stakhiv EZ (1999) Risk-Cost Optimization Under Uncertainty for Dredged

Material Disposal. Journal of Water Resource Planning and Management 125:342–351Triantaphyllou E (2000) Multi-criteria decision making methods: A Comparative Study. Kuwer Academic

Publishers, Dordrechts, Boston, London.

Springer


van Moeffaert D (2002) Multi-criteria decision aid in sustainable urban water management. Master Thesis.Royal Institute of Technology, Stockholm, Sweden

Vasseur L, Lafrance L, Ansseau C, Renaud D, Morin D, Audet T (1997) Advisory committee: a powerful toolfor helping decision makers in environmental issues. Environmental Management 21:359–365

William AT, Ergin A, Micallef A, Phillips MR (2004) South Wales Coastal Scenery: An Innovative Methodof Evaluation. In: Proceeding of the Littoral 2004, A Joined EUCC and EUUROCOAST Conference,Aberdeen, UK.

Wirtz KW, Adam S, Liu X, Baumburger N (2004) Robustness Against Different Implementation of Uncertaintyin a Contingency DSS: the Prestige oil spill accident revised. In: Pahl-Wostl C (Hrsg.) Proceeding of theInt. Environmental Modelling and Software Society Conference 2004. Osnabruek

Wirtz KW, Baumberger N, Adam S, Liu X (2006) Oil spill impact minimization under uncertainty: EvaluatingContingency Simulations of the Prestige Accident. Ecological Economics (in press).

Yu Y, Wang B, Wang G, Li W (2004) Multi-person multiobjective fuzzy decision-making model for reservoirflood control operation. Water Resources Management 18:111–124

Springer

Documents

Decision making of oil spill contingency options with ... · Decision making of oil spill contingency options with fuzzy comprehensive evaluation ... during oil spill response management