Better decision making to improve robustness of OCV designs Ali Ebrahimi1, Per Olaf Brett1,2, Jose J. Garcia3, Henrique M. Gaspar3, Øyvind Kamsvåg1

ABSTRACT

This paper discusses case examples of how the proper integration of Multi-Criteria based Decision Analysis

“MCDA” and Multi-Objective design balancing techniques are useful to improve Offshore Construction

Vessel (OCV) design solutions and make the vessel design process more effective. Our proposition is that

better decision-making in vessel design of OCVs is ensured by applying state-of-the-art decision-making

methodology including evolutionary MCDA and multi object Pareto-front design methods as an integrated

approach. In this way, full integration and investigation of most relevant critical OCV design aspects and

design objectives are included. This paper discusses also how systems thinking in vessel design can provide

a better understanding of potentially conflicting design objectives and make improvements of our capabilities

to determine different performances of possible design scenarios. Furthermore, utilizing a multiple criteria

decision-making model leads to the identification of the better-compromised set of solutions among relevant

benchmarks and pre-selected potential design criteria. The multi-criteria selection among a set of non-

dominating solutions, in which no single objective can be improved without degrading the achievement of at

least one other objective, is discussed in the paper and practical case solutions are shown to support a new

and improved vessel design approach. Multivariate data analyses of real vessel fleets are utilized to improve

accuracy and calibrate design solutions based on new sets of fleet performance indicators. Multi-criteria-

based Pareto fronts, which are specifically defined for different segments of OCVs are developed and applied

on specific real new building OCV design cases. Design solutions are selected from Pareto fronts of virtually

explored design spaces, minimizing light weight and engine power, while maximizing pay load capacity and

sea-keeping behavior. Final design solutions are selected among promising design solution candidates. This

selection is based on market performance yield benchmarks applying a statistical based decision-making

approach to achieve Smarter, Safer and Greener vessel design solutions.

KEY WORDS Offshore Construction Vessel Design; Decision-making in ship design; Multi-object design; Pareto–fronts; Design methods;

INTRODUCTION- DECISION-MAKING IN EARLY DESIGN OSV DESIGN PROCESS The notion of a superior vessel cannot be connected solely to the last decades performance indicators, such as speed and capacity, but should also consider balancing the design in terms of more abstract arguments, such as smarter, safer and environmental friendliness (greener design) or flexibility, robustness and agility within technical, operational and commercial aspects (Hagen and Grimstad, 2010; Gaspar et al., 2012). In Ulstein Group it is generally accepted that these requirements may change the way we think about ship design in general, while the challenge is significant and often a very difficult process to integrate performance capability, operability and profitability of designed vessels during the early design stages of a vessel. This creates a need for innovative and efficient designs and complementary work process, incorporating new aspects while not compromising the economic performance and competitiveness of each vessel concept. Such a complex design environment needs a better understanding of potentially conflicting objectives, while proposing improvements to determine a good early assessment of performances of designs under several scenarios. Ulstein and Brett (2012) present the Ulstein ABD (Accelerated Business Development) process, which specifies the performance expectations from internal and external stakeholders and how they can be met with an effective design and decision-making process. This approach handles stakeholder’s expectations and preferences, which are combined to provide

1 Ulstein International AS, Norway 2 Norwegian University of Science and Technology, Marine Systems Group, Norway 3 Aalesund University College, Faculty of Maritime Technology and Operations, Norway

both targets and boundaries for the solution space development. Thus, specific solutions can be selected and developed in line with the expectations and vary in terms of overall performances, to be benchmarked for what is the most preferable solutions among peer-solutions The capacities, functionalities and ship systems selection of Offshore Support Vessel (OSVs) design in general and Offshore Construction Vessels (OCVs) in particular, are much influenced by the special demand of the offshore oil and gas market. It is also important to ensure their short, medium and long term capabilities and performance yield based on a complex, likely solution space domain. Figure 1 indicates all the facets of such a design balancing exercise. Following the Ulstein ABD process in its fundamental thinking, it is argued here that such approach should be complimented by a proper decision making model. We intend to focus on how to select among possible solutions and differentiate/select better among different perspectives and their influence on design decision-making. As it is depicted in Figures 2 and 3, both perspectives A (Technical, Operational and Commercial) and B (Smarter, Safer and Environmental) should be considered in a same time frame at initial phase. Giving more priority to each of these design perspectives will lead to alternative design solutions with emphasis on different particulars and attributes. It is essential that such overall priorities are well explained and understood within its conceptual design solution space. Figure 3 shows how each of the perspectives (A or B) can be measured and metrically monitored as the design solutions are changed and compromised to meet the expectations of the stakeholders involved at the decision-making process of a new vessel design project. Each aspect of the design perspective are allocated to each corner of the controlling triangle, thus defining the boundary limits for each of these variables. Later on in the paper, it will be discussed how to deal with these aspects in a real design decision making process and how should design variables be measured, calibrated and compared with each other to improve output and outcome of the vessel design process. From a technical standpoint, ship designers should consider the influence of major technical parameters in a holistic point of view (Vassalos 2009), within a multi objective design environment, evaluating decisions based on a multi-criteria parametric based ranking. Furthermore, utilizing a multiple criteria decision-making model leads to the identification of finding the better compromised set of solutions among relevant benchmarks/preferences and pre-selected potential design solutions. Typically, in an OCV design process, performance indicators such as cost, construction quality, operability, safety, fuel economy, maneuvering, sea keeping behavior, payload capacity/cargo areas, site operation and transit mode capability, are some of the main criteria, which are considered critical in an early design stage. Hence, designers should be able to make an early process trade-off among these capabilities and capacities.

Figure 1 – Diverse ship design facets (based on Ulstein ABD process, Ulstein and Brett 2012)

In this context, it is important that we can analyze in more detail the meaning of a better solution by some arguments taken from the limited rationality theory (March, 1994). Let’s say that the primary objective of the ship design process is to generate the information needed to build a ship within customer and regulatory requirements. Rational decision-making assumes that a design will be selected among other alternatives by considering the consequences of every choice and selecting the alternative with the highest expected return. However, the optimal maximization of such return is practically impossible, given the large number of variables (i.e. the level of complexity) involved with the ship design process. In real life situations, such decision-making ends up satisficing, that is, choosing the alternative that exceeds some of the criterion or targets set, but not all of them. Hence, many vessel design objective setting processes are partly defunct or sometimes quite misleading. The perception of a better vessel relies then in a middle term perspective, between the pure satisficing and maximizing the goodness of fit of all stakeholders expectations. On one hand, we would like to create and analyze all possible alternatives, and choose the best. On the other hand, we, as human beings, are only able to compare and contrast a very limited set of variables and alternatives when we try to find the good enough combination of variables and solutions. Our proposal is that multi-criteria-thinking during early stages can assist designers and relevant stakeholders like shipowners, customers´ customer, flagstate, class, and finance among them, to improve the decision-making process and design development. To achieve better vessel design solutions eventually, either by maximizing the search and solution space, or by more effectively comparing an existing set of designs with high complexity is a must. Such decision-making requires appropriate analysis tools to handle the complexity (Gaspar et al., 2012).

Figure 2 – Different design perspectives in conceptual phase

Figure 3 – Generic model for ship design decision making

MULTIPLE-CRITERIA DECISION ANALYSIS (MCDA) Multiple-criteria decision-making or multiple-criteria decision analysis (MCDA) is a set of tools for supporting decision making when multiple objectives have to be pursued and multiple criteria evaluated, even if conflicting with each other. Most ship design problems involve multiple conflicting criteria for selecting the better design. There is no unique right methodology to be applied in ship design problems and each case will require a customized approach to understand the compromise between different solutions. In general, MCDA problems can be divided in two distinctive groups due to the different problems settings whether the solutions are explicitly or implicitly defined. Multiple-criteria evaluation problems consist of a finite number of alternative solutions explicitly known in the beginning of the solution process. Each alternative is represented by its multiple criteria performance indicator. The problem may be defined as finding the good solution by satisfying requirements given a set of available alternatives. Some useful techniques to solve multi criteria evaluation problems are (based on Triantaphyllou 2000):

i) Analytic hierarchy process (AHP) ii) Elimination and Choice Expressing Reality (out ranking method) iii) Superiority and inferiority ranking method (SIR method) iv) Weighted product model (WPM) v) Technique for the Order of Prioritization by Similarity to Ideal Solution (TOPSIS)

Multiple-criteria design problems (multiple objective mathematical programming problems) contain an infinite number of solutions. When related to vessel design, an attribute may take any value in a range instead of discrete values. Therefore, the potential alternative solutions could be infinite. The problem may be defined as maximizing the solution given an infinite possibility of create alternatives. The “maximizing” problem is transformed into a satisfying mode, the "problem solving" is interpreted by narrowing the design space and choosing a small set of good alternatives, or grouping alternatives into different preference sets. The mathematical formulation of a general multi-objective optimization problem can be written as follows (Miettinen 1999):

Subject to the constraints:

K is the multiple optimization criteria f1(x) through fK(x) are each dependent upon the N unknown design parameters in the vector x. In general, this problem has no single solution due to the conflicts that typically exist among the K optimization criteria. An extreme interpretation could be to find all "efficient" or "non-dominated" solutions in such a set of data. Non-dominated solutions are specified solutions that are not possible to move away from them to any other solutions without

sacrificing in at least one criterion these solutions called Pareto fronts (Statnikov 1999)., exemplified in Figure 4 (Ross and Hastings, 2005; Ross et al., 2004). As the set of Pareto fronts solutions is too large to be evaluated for final decision making, we can merge utility functions (normally in a single cost indicator) or utilize some heuristic method to filter the large number of tradeoffs until key criterial are satisfied (goal programming).

A demonstration of the use of the Pareto front in MCDA is presented later in this paper. We attempt vessel design solutions to integrate both evolutionary and mathematical programming methods in different steps of a decision-making process to generate non-dominated design solution with highest fitness to client requirements. To achieve this multi objective design (MOD), performance-indicator-based-outranking and hierarchical-design-scoring models are aggregated into the Ulstein OSV-design-balancing and decision making-process (Figure 5). The objective of such a process is to come up with more reliable and robust solutions in the final stage

Figure 4 - Different type of trades within the design space (Ross and Hasting 2005; Ross et al., 2004)

Figure 5 - Ulstein design balancing and benchmarking methodology process flowchart

MULTI- CRITERIA BASED DECISION ANALYSIS TOWARDS A VALUE ROBUST OSV DESIGN The multi-object design procedure used here investigates tradeoffs among different objectives in the design of OSVs. A better

vessel solution is achieved by balancing solutions with lowest possible operational and capital cost (OPEX and CAPEX) and highest possible performance capabilities among all possible solutions in an explored design space. The concept of right vessel

for the right mission (Gaspar et al., 2015) is used to improve the better designs in contrast to competitors of the market for a specific customized design. There is always trade off among required missions that should be decided in multi-criteria decision making procedure to make more understandable which attributes have higher priority for different clients and particular designs, that all may influence the design parameters and main particulars of vessels in the broader pictures. These kinds of variant functionality expectations challenges the investigation of value robust designs that perform as expected over time and are partly insensitive (or profitable responsive) to changes in operational, environmental or regulative factors. Practical design experiences shows that inside OSV fleets this robustness is connected to different design parameters, functionalities and vessel capacity/capabilities given the diverse OSV segments. For instance while winch pull and anchor handling winch power has higher importance in anchor handling and towing supply (AHTS) design; in the diving support vessel segment (DSV), dynamic positioning (DP) capability comes more into consideration since in cross-over vessels (PSVs

Figure 8 - ranking based vessel design procedure

with added functionalities) higher transport payload capacity and better transit mode operation are more important design criteria’s. Figure 6 illustrates a simplified model of the importance level of different design elements and vessel particulars in the design of OSVs, which are directly influencing design solution value robustness. A detailed dependency model of PSV’s capabilities is depicted in Figure 7, which shows that these functions are internally related and how influence technical, commercial and operational aspects of the decision-making procedure (Gaspar et al., 2014).

The methodology focuses more on the understanding of design factors inside each OSV segments by introducing relevant performance indicators, enabling the designer to measure the performance of vessel and compare different designs to each other and competitor’s designs with initially accepted performance indicators. The same comparing method is applied to the set of Pareto front designs that are driven out from the multi objective process at the primary stage of the design. Non-dominated sorting-crowding-distance algorithms give opportunity to select some designs from the Pareto set (Silvano et al., 2011). The distance computed for the unique fitness is, the distances assigned to individuals with the corresponding goodness of fit. However, coming up to the final solution needs, another decision-making strategy to clarify differences among alternatives could be applied. As the number of solutions are finite at this stage, following multi criteria evolution problem solving methods (MCDA) enables better decision-making and design balancing processes.

Ranking based decision-making model Ranking market-benchmarks and different design alternatives based on technical, operational and commercial aspects separations are conducted in this analysis according to the procedure listed in figure 8. This method is applied in three main steps, involving indices development based on vessel missions, scoring by indices and ranking by statistics.

Figure 6 - OSV segment function importance

table Figure 7 - PSV segment function importance and their

relation with technical commercial and operational

aspects

Figure 9 - Index based ranking example

A ranking-based-model, that is, an approach differentiating a performance indicator developed for each specific segment according to required missions of the vessel segment is needed. The idea behind this ranking-base-method is to define some relevant indexes to measure different vessels performance yield based on Technical, Commercial and Operational aspects.

Indices for different OSV segments are developed based on the generic Fraction of ��������������, which is an indicator of

efficiency of a specific factorial relationship. Indices are developed based upon available data of different OSV segments in ship data bases such IHS Fairplay or MarineBase and outline specifications of different vessels existing in real life. The concept of developing vessel performance yield indicators is following the similar idea as that of the EEDI index (IMO 2011; Gaspar and Erikstad, 2009), which measures environmental friendliness of vessel designs based on available parametric and capacity data avoiding weighting factors. Figure 9 shows the results of the UGPI (Ulstein General Performance Indicator™) for some single OSV design solutions with market competitors included. The frequency distribution plot is the way results are visualized. UGPI is a combination of technical, operational and commercial indices as presented in Figure 10. Each specific index is a combination of different design factors and vessel particulars, which are gathered from outline specification of different existing designs/vessels or calculated based on classic naval architecture analytical methods for newly developed designs. For instance, the Power Utilization Index™ (PUI) is demonstrating how total installed power of vessel is utilized to fulfill requirement of different major consumers such as DP level, crane capacity, operational area, sea state, accommodation size among other factors, to differentiate between different designs from an installed power utilization aspect. Equation 1 and 2 depicts two different INDEX equations generated for the UGP index for AHTSs and PSVs respectively. Equations according to the set of expected functionality of the vessel design solutions are developed and they have been adjusted as a base for a purpose built outranking and benchmarking method for OSVs. Each individual number is normalized to the average of the total fleet of relevant segments of vessels, to encompass and mitigate the impact of scale differences among different particulars. However, it is not the aim of this paper to present the methodology of such an indexing procedure in full but discuss how Ulstein is now benchmarking and measuring performance yield of own and competitor’s vessel designs. The procedure has already been applied and robustness tested in Ulstein to study the true differences among design solutions, and which means a better vessel solution.

Figure 10 - UGPI™ decomposition for different OSV segment

� � =���������ℎ�4090 � × � �� ����6 # × � $���12,7 �

��)��*5807 � PUI =�0)11�*� �21174 # × �3�45ℎ 6�4� �211143 # × � �� ����5,4 # × � $���12,2 �

��)��*4177 �

Equation 1: PUI, PSV segment Equation 2: PUI, AHTS segment

Hierarchical multivariate based vessel/design rating according to smarter, safer and greener aspects: The Analytic Hierarchy Process (AHP) is a decision-making procedure used to offer solutions to decision problems in multivariate environments, in which several alternatives for obtaining given objectives are compared under different criteria (Saaty, 1987). The AHP establishes decision weights for alternatives by organizing objectives, criteria and subcriteria in a hierarchic structure. These structures are created by having sets of alternate attributes existing between a set of alternate designs and the overall problem of which design to select. These alternate attributes may relate to numerous levels associated with the overall problem, for example they may relate to the sub-attribute of design, emerging properties of a design and the relative preferences of any stakeholders involved in the overall decision process. Figure11 shows the structure of the methodology. To enable the utility of AHP in OSV design ranking, a combination of expert judgment and historical data analysis is used to develop hierarchical model and pairwise comparison among sub attributes. Following steps are demonstrating the way we developed AHP model to compare different selected design from a Pareto set among each other and with relatively selected benchmarks to show the robustness of generated design as a design quality assurance.

i) Hierarchical factor categorization ii) Metric attribution of design factor causal map matrices iii) Hierarchical comparative based ranking

A CASE STUDY OF AN IMR VESSEL DESIGN ACCORDING TO THE PROPOSED ULSTEIN APPROACH According to reviewed methodology, a case study of a medium size globally operable, cross over inspection, maintenance and repair (IMR) vessel is considered. Design range constraint are defined in Table 1. Initially it is required to develop a design solution space according to introduced multi-objective design methodology within defined range of constraints. What we propose in this model compared to available multi-objective ship design models, is using parametric design equations developed at Ulstein (Ebrahimi et al., 2015). Stakeholder’s expectations are initially identified and metricized into objective functions when solving the multi-objective design problem and generating on Pareto front sets. DWT = f & ( L , B , T ) Deck Area = f & ( L, B ) Deck Load = f & (L, B, D) Power = f & ( L, B, T , Speed) Light weight= f & ( L , B , T ) Seakeeping performance = f & (L,B,T,D,Cb,Cp,Lcb,GM,Cwp) A developed MOD tool (Figure 13) uses DWT as target function. It is set on 4400tonnes since this number is modified later based on defined standard moonpool size, crane size and other installed deck equipment. Deck area, deck load and seakeeping performance of solutions are maximized and light weight and installed nominal power are minimized. Conceptual 3-Dimensional Pareto fronts are generated (Figure 14) for the virtual solution space.

Figure 11 - Example of AHP decision hierarchy

Table 1 - Design constraint defined for required IMR

vessel Objective Functions (from stakeholder’s expectations)

Fig 12 - AHP Analysis steps

After 20 iterations six final solutions suggested by the tool, which are selected based on crowding distance theory from Pareto front are: See Table 2. Final solution consists of six different solutions, having different functionalities since all are non-dominated solutions from the Pareto front. It is shown that there is around 130 m2 discrepancies in net deck area provided by solution 4 and higher ability for lifting and provided accommodation because of higher beam compared to solution 2, which means higher revenue earning capability of solution 4. This solution has around 450tonnes more light weight and requires 430 kW more installed power. The conflicting objectives comes into decision making process and it should be considered what is more important for customer, pay more as capital cost and earn more during operation or lower investment with reasonable revenue earning capability certainly this decision on your bet with the market. To proceed means to select one of the solutions more fitted to market needs, based on previously defined perspectives A and B.

We start with the performance indicator ranking based on these solutions. Commercial indicators of all the solutions are equal, since, all are internal designs and our assumption is the same brand of equipment similar building country and similar year of built there is part of judgment. However, in contrast to the global OCV fleet all these commercial items are coming into consideration. In terms of technical and operational indices, we can observe differentiation among solutions, which makes decision-making process much easier. Table 3 presents the results of different indices calculated for these solutions.

Figure 13 - Ulstein MOD design criteria set-up PSV

segment

Figure 14 - 3D Pareto front and set

selection

Table 2 - Six final solutions introduced by the model

Table 3 - Calculated performance indicators for six solutions

TPLCI (50) PUI (50) GSCI (50) SOCI (50) CMPI (1-50) TOPI ( 1-50) UGPI (1-100) Price (USD) UGPI / Price

Sol1 7,8 14,1 16,5 4,9 25 21 46 63 825 435 26,9

Sol2 8,3 13,8 8,6 4,1 25 16,5 41,5 57 824 048 26,8

Sol3 7,8 13,6 7,3 4,1 25 15,4 40,4 60 839 198 24,8

Sol4 8,1 13,8 16,9 5,4 25 21,6 46,6 63 752 618 27,2

Sol5 8 13,7 17,2 4,9 25 21,3 46,3 62 417 296 26,8

Sol6 8,1 13,7 8 4,5 25 16,2 41,2 62 456 765 24,6

In terms of payload capacity (combination of DWT, deck area, deck load, tank capacities and accommodation), some solutions indicate better utilization of size in terms of ability to transfer more cargo although all solutions are almost in a similar range. While, the General Service Capability Indicator™ (GSCI), is showing higher speed and seakeeping performance, but uses more fuel to comparable vessels. In Power Utilization™ (PUI) all solutions are close to each other while we see wider range of Site Operation Capabilities™ (SOCI), which is mainly because of different crane capacity and accommodation size installed. However, a combination of these technical and operational indicators in Technical and Operational Performance Indicators™ (TOPI) shows vessels with higher beam are better solutions among the peers because of providing better seakeeping performance, which is essential for operability of these segments of vessels. Although the construction cost is higher for these solutions compared to the smaller size solutions, still they can gain higher Performance/Price score which is indicating higher revenue earning capability per each USD of investment. Figure 15 shows the position solutions of the global OCV fleet distribution. The Solution 4 has the best position considerably and better than the top 15% of the actual vessel fleet sample used as reference.

Figure 15 - Position of six solutions in the OCV fleet TOPI distribution

Figure 16 - Hierarchical decomposition smarter, safer and greener OCV segment

Table4 - Calculated score of different aspects for six solutions

After analyzing solutions from the perspective A, we perform a comparison among solutions from a perspective B. To develop such analysis we have applied hierarchical analytical process to rate different solutions. Internal expert judgment processes involving a team of experienced Naval Architectures and Engineers enabled us to drive a hierarchical tree based on smarter, safer and greener for OCV segment, as it is depicted in Figure 16. Pair-wise comparison of subcriteria is the result of expert judgment and trend analysis of historical data for different criteria. Multiple regression model and β values have been considered as an importance factor in the study to evaluate and validate the expert judgment. The calculations contain proprietary data and are not indicated in this study. Figure 17 and Table 4 reflect the result of perspective B (Smarter, Safer and Greener vessels) based on an AHP model. Overall score is calculated based on sum-product of the score of each individual aspect to overall importance ratio (Equation 3). It should be considered that overall importance ratios are the result of lower level sub criteria pairwise comparisons which has been calculated initially.

Overall score = ∑ 9:;<= ;> =?:@ ?9A=:B ∗ DEA;<B?>:= <?BD;>DFG

It can be observed from the results presented in Table 4 and Figure 15, that despite Solution 4 is substantially smarter than Solution 2, in terms of being greener this design solution is considered an inferior solution. Comparing results achieved for both perspectives (A) and (B) enables us to narrow our solution space. As it was demonstrated for Solution 4 among 6 solutions, it has some strengths and weaknesses, since we aggregate them in an overall triangulation-based decision-making model, we observe that this solution is a smarter solution with higher revenue earning capability per 1 USD of investment, which is typically, more highly prioritized among shipowners as the ultimate differentiator in a real decision-making situation.

CONCLUDING REMARKS The main purpose of this paper was to present an approach which can actually handle the complex decision-making problems in conceptual OSV design in a meaningful and practical way. Any ship design process should provide the designer with a structured method for generating, analyzing, evaluating designs, to achieve at the end of the process a valid (set of) design solution (Erikstad, 1996). The iterative nature of this process requires from OSV designers to come up with acceptable solutions as fast as possible during the conceptual phase. With this intend, we have presented and reviewed in this work a method and procedure to improve early concept design solution decision-making for OSVs, based on a Pareto ranking/Multivariate oriented Benchmarking developed and tested by Ulstein Group. The procedure reduces the time and effort necessary to balance a vessel design solution at the conceptual design stage. It also increases the robustness of early decision-making in vessel design projects. The methodology includes some of the more recent developed decision-making methods and complimentary mathematical tools. The procedure follows recent advances in critical systems thinking, decision-making logic and the full and integrated use of public as well as proprietary data in the maritime field. Specific real vessel design case results are presented and discussed. The result shows that such methodology and procedures can easily differentiate and establish on factual grounds, what is a better ship compared to individual and peer fleet of vessels. The method can handle “a moving target” reference basis, like the EEDI approach, because the method is based on sample data, which is expanded upon for every new vessel design being delivered, However, since the procedure is focusing the relative effects of benchmarking and not absolute vessel

Smarter Safer Greener Overall

Importance ratio 60 % 23 % 17 %

Scores of each aspect over 100%

Sol1 75 % 52 % 70 % 69 %

Sol2 66 % 47 % 60 % 61 %

Sol3 62 % 45 % 59 % 58 %

Sol4 80 % 52 % 55 % 69 %

Sol5 78 % 51 % 52 % 67 %

Sol6 65 % 49 % 50 % 59 %

Figure 17 - AHP study results

Equation 3 - AHP overall score calculation

performance yield measurements, it is less sensitive to sample fleet changes. In most cases, the public data repository provides large enough segment samples to handle applied statistical methods within acceptable recommended practices. There are problems with using empirical data to tell whether (or when) decision makers maximize or satisfice. The usual difficulties of linking empirical observation (our data/clusters) to theoretical statements (this design is better than the other) are compounded by the ease with which either vision can be made tautologically true. True believers in maximization can easily use circular definitions of preferences to account for many apparent deviations from maximizing. True believers in satisficing can easily use circular definitions of targets to account for many apparent deviations from satisficing. However, in this paper we tried to show that multi-objective optimization models separately will not respond to all the required information for effective decision-making but with a much broader uptake of perspectives and critical variables than in vessel design procedures of the past. The case studies show that solutions selected from a Pareto set of data are all non-dominated solutions among thousands of virtually generated peers and still have some major differences which is significantly important from the OSV design aspect. The paper shows how utilization of appropriate multi-criteria decision-making model will reduce the complexity of decision-making procedure to achieve better fitted (goodness of fit to expectations) design to customer requirement.

REFERENCES EBRAHIMI, A., BRETT, P.O., KAMSVÅG, Ø., GASPAR, H.M. and GARCIA, J.J., “Parametric OSV Design Studies – precision and quality assurance via updated statistics”, (accepted) 12th IMDC, Tokyo, 2015

ERIKSTAD, S. O. “A decision support model for preliminary ship design”. PhD thesis, NTNU (1996).

GASPAR, H. M., ERIKSTAD S. O. “Extending the Energy Efficiency Index to Handle Non-Transport Vessels”, COMPIT’09, Budapest, Hungary, 2009.

GASPAR, H. M.; RHODES, D. H.; ROSS, A. M.; ERIKSTAD, S.O. “Addressing Complexity Aspects in Conceptual Ship Design: A Systems Engineering Approach”, Journal of Ship Production and Design, DOI: 10.5957/JSPD.28.4.120015, Volume 28, Number 4, pp. 145-159(15), November 2012

GASPAR, H. M., BRETT, P. O., EBRAHIM, A., KEANE, A. “Data-Driven Documents (D3) applied to Conceptual Ship Design Knowledge”, COMPIT’14, Newcastle, UK, 2014.

GASPAR, H. M., BRETT, P. O., ERIKSTAD, S.O., ROSS, A. M. “Quantifying robustness of OSV designs taking into consideration medium to long term stakeholders’ expectations” (accepted) 12th IMDC Tokyo, 2015

HAGEN, A. AND GRIMSTAD, A., “The Extension of System Boundaries in Ship Design”, Transactions of RINA, vol. 152, 2010.

IMO, “Marine Environment Protection Committee (MEPC)” 62th session, 2011.

MIETTINEN, K. M., “Nonlinear Multiobjective Optimization”, 1999

MARCH, J. G. “A primer on decision-making: How decisions happen” The Free Press, New York (1994).

ROSS, A.M. and HASTINGS, D.E., “The Tradespace Exploration Paradigm”, SEARI - Massachusetts Institute of Technology, 2005

ROSS, A.M., HASTINGS, D.E, WARMKESSEL, J. M. and DILLER, N. P., “Multi-Attribute Tradespace Exploration as Front End for Effective Space System Design”, Journal of spacecraft and rockets, No.1 Jan-Feb 2004

SAATY, R. W., “The analytic hierarchy process – what it is and how it is used”. Mathematical Modelling Volume 9, 1987

SILVANO, C., FORNACIARI, W. and VILLAR, E., “Multi-Objective Design Space Exploration of Multiprocessor SoC Architectures”, 2011

STATNIKOV Roman Multicriteria Design: Optimization and Identification / Edition 1 1999 ISBN:0792355601

TRIANTAPHYLLOU, E., “Multi-Criteria Decision Making - A comparative study”, 2000

ULSTEIN, T., AND BRETT, P. O. “Critical systems thinking in ship design approaches” 11th IMDC, Glasgow, UK, 2012.

VASSALOS, D., “Design for safety” in “State of the art report on design for X”, 10th IMDC, Trondheim, 2009.