Design Theory and Aspects of Uncertainty in Design

DESIGN THEORY AND ASPECTS OF UNCERTAINTY IN DESIGN Trondheim, June 2015

Abstract In this final paper for the MR8100 - Theory of Design class at NTNU, aspects of design theory covered in class, such as design as a mapping process, design axioms, design methods and design complexity, are presented and discussed together with the author’s PhD topic, which involves handling uncertainty in marine systems design.

Carl Fredrik Rehn [email protected]

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Carl Fredrik Rehn

TABLE OF CONTENTS Structure of the Report ........................................................................................................................................... 1 Design Theory and Design as a Science ................................................................................................................... 2 Design Theory and Design as a Science ............................................................................................................... 2 Design as a Mapping Process .............................................................................................................................. 2 Design Axioms ..................................................................................................................................................... 3 Complexity in Design ........................................................................................................................................... 3 Knowledge-Based Design .................................................................................................................................... 4 Design Process Methodologies ............................................................................................................................... 5 Traditional Methodologies .................................................................................................................................. 5 Systems Critique.................................................................................................................................................. 6 PhD Topic - Handling Uncertainty in Design of Marine Systems ............................................................................. 7 Discussion ................................................................................................................................................................ 7 Design Theory and Aspects of Uncertainty ......................................................................................................... 7 Design Axioms/Complexity and aspects of Uncertainty ..................................................................................... 9 Design Methodologies and Aspects of Uncertainty .......................................................................................... 10 Satisficing vs. optimizing ............................................................................................................................... 11 Critics ............................................................................................................................................................. 12 Conclusion ............................................................................................................................................................. 12 References ............................................................................................................................................................ 12

STRUCTURE OF THE REPORT The first two chapters discuss topics that have been covered in class: -

Design theory and science. Design process methodologies.

Thereafter, the authors PhD topic on handling uncertainty in design is introduced, before it discussed in light of the earlier presented material on theory and methodology, in roughly the same order. If the design theory curriculum is known for the reader, the added material in this report are the following chapters: “PhD topic”, “Discussion” and “Conclusion”.

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DESIGN THEORY AND DESIGN AS A SCIENCE DESIGN THEORY AND DESIGN AS A SCIENCE Discussions on design as a science were introduced in the 1960s, when R. Buckminster Fuller (Fuller et al., 1965) defined design as a systematic form of designing and S. A. Gregory (Gregory, 1966) drew the distinction between design and the scientific study of design. In 1969, Nobel laureate Herbert A. Simon (Simon, 1996) introduced a distinction between science in the natural and artificial world, where artificial means man-made as opposed to natural. Natural science is knowledge about natural objects and phenomena, and the underlying goal often is related to increasing knowledge and to better understand nature. In the sciences of the artificial, on the other hand, it is important to understand the purpose of a design. The object or system can for example be a ship, which has been designed with a purpose and has a function that it is supposed to satisfy. The goal in the sciences of artificial objects is related to achieving a better understanding, in order to Figure 1: Human interaction with classical science and the sciences of the artificial. improve the system or object performance.

DESIGN AS A MAPPING PROCESS On a general basis, design can be considered as a creation of a plan for the construction of an object or system, and has been central in many disciplines for a long time. In a more specific description, design can be considered as a mapping process from a performance space, to a descriptive space (Coyne et al., 1990, Suh, 1990), as illustrated in Figure 2. “Design may be formally defined as the creation of synthesized solutions in the form of products, processes or systems that satisfy perceived needs through the mapping between the functional requirements (FRs) in the functional domain and the design parameters (DPs) of the physical domain, through the proper selection of DPs that satisfy FRs.” - Nam P. Suh, The Principles of Design, page 27 (Suh, 1990) A way of explaining the difference between these two spaces is by linguistic terms, where design descriptions belong to the syntactic space and functional performances belong to the semantic space. The syntactic space is based on syntax, which describes how sentences are formed by words. This can be interpreted as how e.g. a ship is formed by its design variables, such as length, beam and draught (L,B,D). The semantic space is related to meaning, and illustrates the purpose of a design. This can for example a ship that is designed for efficient transportation purposes.

Figure 2: Descriptive space and performance space.

̅̅̅̅̅) and The performance space is typically characterized by requirements, e.g. the initial metacentric height (𝐺𝑀 freeboard (F) for a ship. As illustrated in Figure 2, these can be found as functions of parameters from the description space (L,B,D), a process that can be referred to as analysis. Designing the ship would then be by finding the best set of parameters to satisfy the requirements. Although ̅̅̅̅̅ 𝐺𝑀 and F can be calculated from (L,B,D), 2

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this function does not work the other way directly. In some sense however, one can say that it does, by means of regressive functions on historical data (Levander, 2009). Mapping from the descriptive space to the performance space can be considered as analysis. As opposed to synthesis, analysis is related to the separation of a topic or substance into its constituent elements. Since the traditional iterative design process (such as the design spiral, see Figure 8) includes analysis, these two processes are not as different as they might appear, and one can say that they complement each other.

DESIGN AXIOMS “How do we make good design decisions and why is a design a good design?” – These are questions that typically can be asked in a design process. In order to answer these fundamental design questions and to provide aid in the creative design process, in 1990 Nam Suh proposed two design axioms, or design “postulates” (Suh, 1990). The axioms govern all design decisions, whether they are for products, processes, systems, software or organizations, and the basic goal is to establish a scientific foundation for the design field. Axiom 1 Axiom 2

The Independence Axiom: Maintain the independence of functional requirements. The Information Axiom: Minimize the information content of the design.

The Independence Axiom deals with the relationship between functions and physical requirements, and states that during the design process, the mapping from functional requirements (FR) to the design parameters (DP) must be such that a perturbation in a particular DP must affect only its referent FR. Therefore, an optimal design always maintains the independence of the FRs. The Information Axiom deals with a minimization of the information content of the design, which can be done by minimizing FRs, standardization and the use of symmetry in design. Both axioms favor a reduction of design complexity. Based on this way of thinking, the best design is a functionally uncoupled design that has the minimum information content. Furthermore, Suh introduces seven corollaries as propositions that follow from the axioms or other propositions that have been proven. These involve for example decoupling of coupled designs, minimization of FRs, and use of integration and standardization.

COMPLEXITY IN DESIGN In his book on the Sciences of the Artificial (Simon, 1996), Herbert A. Simon also discusses the architecture of complexity in the light of hierarchic systems. Complexity is used to characterize something that is made is made up of parts with many interactions, for example a complex system. In complex systems, the whole represents more than the sum of the parts. A hierarchy is an arrangement of items (such as objects, names, categories etc.), where the items are represented on levels relative to each other (Figure 3). Hierarchic systems are composed of interrelated subsystems, each of the latter being in turn hierarchic in structure until we reach some lowest level of elementary subsystem. Central in Simon’s discussion is that complexity frequently takes the form of a hierarchy, and that hierarchic systems have some common properties independent of their specific content. Hierarchy is one of the central structural schemes that the architect of complexity uses.

Figure 3: Hierarchy example.

Complexity and hierarchy can be seen in exemplified by biology and aspects of the evolution. The cell is for example a building block for tissue, and we find tissue organized into organs and organs again into complete systems. In turn, within the cell there are several stable subsystems, such as the nucleus, cell membrane and 3

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mitochondria. Simon argues that since complex systems consist of several levels of stable subsystems, their evolution and existence becomes easier, or perhaps even possible, at all. Furthermore, Simon argues that hierarchies have the property of near decomposability. This means that the “high-frequency dynamics of a hierarchy” – involving the internal structure of the components – can be separated from the “low frequency dynamics” – involving the interaction among components. This is because intracomponent linkages are generally stronger than intercomponent linkages. Additionally, how complex or simple a structure is also depends upon the way in which we describe it. Most of the complex structures found in the world are redundant, and we can use redundancy to simplify their descriptions.

KNOWLEDGE-BASED DESIGN In their book on knowledge-based design systems, Coyne, Gero et al. present a knowledge-based model of the design process (Coyne et al., 1990) . Although a main purpose of the model is to enable computers to assist in the design process, it presents interesting points considering the theory of design and the sciences of the artificial. In order to present a complete framework, the authors start by discussing means of describing designs. Facts are statements about relationships between objects. Objects, in turn, are simple units of information. Knowledge can be defined as relations between facts, and is central when it comes to reasoning processes (Figure 4). That is, for example, how new facts can be described from known ones, such as “A is true if B is true”. In particular, knowledge is relevant in deductive reasoning, which is a logical process of drawing certain conclusions from statements (premises). Additionally there are two other reasoning processes called induction and abduction. Induction involves the acquisition of knowledge, generally given several examples of premises that produce similar conclusions. Abduction involves reasoning to premises given the knowledge Figure 4: Reasoning and knowledge in design. and conclusions. The authors argue that abduction most accurately characterizes the design reasoning, where we know what we want – the design must exhibit certain performance characteristics, but we do not have the physical design descriptions that meets the requirements. The authors define two categories of design knowledge, those concerned with interpretation and those concerned with design syntax (Figure 5). Interpretive knowledge is what enables design behaviors Figure 5: Deductive reasoning for interpretation and syntax (Coyne et al., and attributes to be inferred from the design 1990). description. Syntactic knowledge, or generic knowledge, can be expressed as grammatical rules or actions, such as mappings between facts. In terms of linguistics, design shares with language a concern with interpretation and also with composition. That is, how things go together: in the case of language – compositions of words, in the case of design – compositions of design elements. Just like words compose sentences, designs can be represented as strings of Figure 6: Barge representation. symbols in computer scripts. Interpretation is a concern both for design and for language. Consider for example a shipyard, with tons of steel and construction materials. In a typical 4

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building process, a design is assembled from a vocabulary of parts, which fit together according to the syntax of composition. The resulting design can be interpreted as for example an oil tanker, a FPSO or a barge (Figure 6). Design involves both interpretation and syntax, and can, according to the authors, be defined as a search within the two categories of knowledge. This complements the more typical definition of design: as a mapping between the descriptive space and the performance space (Figure 7).

Figure 7: Design process illustration.

DESIGN PROCESS METHODOLOGIES TRADITIONAL METHODOLOGIES

Context

Since it often is difficult to understand how subsystems interact in complex systems, iterative design methods are normally used. The iterative approach can be illustrated by the classical design spiral in Figure 8, introduced by J. H. Evans in 1959 (Evans, 1959). Finding the right descriptive parameters for ships by this iterative process is what traditionally is considered as ship design. Levander discusses design in the light of how systems fulfill functional requirements, in order to satisfy the overall mission (Levander, 2009). Levander argues that, in contrast to the traditional ship design spiral method,

Design Space

Analysis

Performance Space Functional requirements

Design param. L,B,D,Cb

Design System Based Design

GT LW Displ.

Form

System identification

Figure 9: Design, analysis and Levander's approach.

Figure 8: Design spiral (Eyres, 2007).

which “locks” the designer to initial assumptions, the system based ship design process is more supportive to innovation and creativity. The system based design method (Figure 9) starts by clearly identifying the mission in the performance space, so that a functional description can be made. The functional description defines all systems that are needed for the design to perform as required. In order to reduce complexity and to make the creativity process easier, Levander argues that the requirements can be divided into “musts” and “wants”. Levander’s approach requires data from other designs, as the method in practice 5

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involves regressions for estimating the “form” from the functional requirements. The “form” estimates can for example be the volume of the engine room (m3) and the deck space area (m2). The system based design takes a broader view on the design process, neglecting details, so that the design easier becomes technically feasible and economically preferable. Another point of view on the design process is a risk-based approach, by Apostolos Papanikolaou. He discusses that methods of risk and reliability analysis, mostly related to safety, have become more and more frequent in modern design disciplines (Papanikolaou, 2009). Furthermore, he argues that by introducing risk as an objective into the design optimization process, rather than as constraints, new technical solutions can be explored, as the design solution space becomes larger. On the other hand, in their book on a systematic approach to engineering design Pahl and Beitz argue that a systematic approach to engineering design is essential for successful and efficient product development (Pahl and Beitz, 1996). The method they discuss involve careful planning of the process, as it first is broken down into phases and steps, each to be solved with different methods. For larger, more complex, engineering systems, NASA systems engineering handbook introduces fundamental concepts and techniques (Kapurch, 2010). In the other end of the scale - for smaller product development projects, you have Ulrich and Eppinger’s book on product design and development (Ulrich and Eppinger, 2012). In Figure 10 one can see these three design disciplines illustrated relative to each other.

Figure 10: Design discipline relations.

SYSTEMS CRITIQUE Why are not optimization and other systematic decision support methodologies more used in real design processes? In his paper from 1979 Russel L. Ackoff argues that operations research (OR) is “dead” and that the chance of its “rebirth” is low since there is little understanding of its demise (Ackoff, 1979). With reference to an article by John R. Hall Jr. and Sydney W. Hess, he describes that a gap has developed between the academic and nonacademic practitioners of OR and management science (MS). OR originated from military planning applications during World War II. In the decades after the war, these techniques were applied in businesses – alongside with the research in academia. The academic development of OR has resulted in a highly complex and theoretical field. Since OR development has been based on an imagined reality, it has become synonymous to mathematical models and algorithms, rather than the ability to formulate and solve management problems. Ackoff argues that industry practitioners decreasingly handled problems as they came, and increasingly sought and selected the problems so that favored techniques could be applied to them. The result has been reduced usefulness of OR. This has in turn been recognized by industry executives, who have pushed OR related work down in their organizations. The industry application of OR has therefore been on relatively simple problems that that arose as permitted by the application of the mathematical techniques. OR was originally a corporate staff function, because the executives once believed it would be useful to them. Then it was pushed down because they no longer believed that this was the case, but they still thought that it had a use. When OR could no longer be pushed down, it was pushed out! Ackoff also discusses other reasons for the gap. He states that the mathematical techniques are most easily thought and learned by people that are not so good at applying them at industry applications, and that users “practice what they preach” – removing the interdisciplinarity of OR. Additionally, there are several factors that make decision-theory difficult in practice, such as personal preferences and that problems usually are complex, and turns into “messes”. Because messes again often are systems of problems, the optimal solutions of the individual problems do not necessarily satisfy an optimal solution to the mess. He argues that OR in practice is 6

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too complicated to be easily modeled. OR problems also involves “predicting the future and preparing for it”, and it helps little to prepare for an imperfectly predicted future.

PHD TOPIC - HANDLING UNCERTAINTY IN DESIGN OF MARINE SYSTEMS Ocean engineering projects, typically related to transport services and petroleum exploration and production, are often complex and involves a high degree of uncertainty related to their future operating context. Uncertain factors, such as the state of the economy, oil prices and changing environmental regulations, are often influential for the viability of a project. These changing factors are difficult to predict and introduce risks for investors in a capital-intensive industry. When designing marine systems, it is therefore relevant to strike the right balance between designing for the more predictable short term situation, while still investing in flexibility to better adapt for the uncertain long-term future operating environment. Even though it is clear that a design will operate in an uncertain context, the traditional approach has often been to assume a fixed set of requirements to which the design must comply. The focus has often been on the most likely scenario, and then later to perform a sensitivity analysis to check if the design is robust enough to withstand changes. Instead of this robust approach, the focus in the PhD project is rather on other, hopefully smarter, solutions of handling uncertainty, such as flexibility. Designing with a focus on flexibility in order to handle uncertainty, one can move the focus towards exploiting opportunities created by uncertainty, instead of trying to avoid them. On the financial market side, the maritime industry is typically considered highly volatile. Thus far, these risks have often been handled by using established market mechanisms, such as insurance and financial derivatives, in order to assure value robustness. The focus in this PhD project is rather on operationalize the link between future uncertainty, risk factors and system design decisions, focusing on conceptual design parameters. The main goal for the PhD project is to increase the knowledge and competence base related to quantifying risk and uncertainties by using optimization, simulation and systems theory. This will include modeling of alternative future scenarios with corresponding probability distributions and conduct design space exploration studies to identify preferable system design configurations. The project will develop insight into how system-level properties, such as flexibility, will be of key importance for the next generation ocean systems.

DISCUSSION DESIGN THEORY AND ASPECTS OF UNCERTAINTY It is necessary to focus on all aspects of a design process in order to complete a design project, from the needs identification and concept evaluation, to the finite detailed design calculations. The latter part is perhaps often what many think of when they hear the word design. However, I argue that it is of significant importance to focus on the early stage design decisions, in order to ensure value robustness for the stakeholders over a project’s lifetime. Estimates show that between 60% and 80% of the total life cycle cost is determined during the preliminary part of the ship design processes (Dierolf and Richter, 1989). The preliminary design process is basically a stage of exploring strategies for solving the formulated problem - that is, satisfying the functional requirements in the performance space. This involves properly evaluating potential concepts in different scenarios, focusing on aspects of relevant contextual uncertainties, and how these can be managed. Despite of the importance of the decisions made in this early stage process, one sees that surprisingly little resources typically are spent here. One of the reasons for this may be that, over the past century, much of the focus in engineering research has been on methodologies for solving more isolated technical problems, e.g. structural and hydrodynamic, that in the light of more powerful computer methods (FEM, CFD, etc.) perhaps 7

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already have seen its prime time of attention. These methods reduce the endogenous uncertainties of how a design will behave, once it is build. On the other side, not that much research has been done at the preliminary design stage, of exploring methods for quantifying all aspects of uncertainties, in order to make better early stage design decisions. As design sub problems (e.g. hydrodynamics, structural, control etc.) in large parts have been solved, at least to a high degree of precision, I think that the era of focus in engineering design will be more and more on the preliminary design decisions - such as aspects of handling uncertainty and complexity in marine systems design. There are several types of uncertainties that can affect the design of a marine system. Exogenous uncertainties are independent of project design decisions, and can be for example the oil price and the freight rates. Endogenous uncertainties can be actively managed by decision makers in the design process, and can for example be the ability to predict a system’s performance before it is build. There are also combinations of these two, called hybrid uncertainties, which can be partly affected by decision maker. Examples are shipbuilding schedule and costs - typical aspects of project management. As introduced in my PhD topic description, functional requirements (FR) in the performance space are traditionally given from the business domain as a fixed set of requirements to which the design must comply. Designing for a fixed set of requirements through a design spiral, can be illustrated as a mapping process, which in the end reduces the endogenous uncertainties. By this I mean that during a high fidelity design process, there is a higher probability that the design will be able to fulfill the FRs, than in a low fidelity design process. Exogenous uncertainties, typically related to changes in the future operating context, can be introduced in the aforementioned design mapping process by introducing a time dependent performance space. The traditional description space is fixed, as designing for robustness has been the standard approach for handling uncertainty. One way for designs to comply with a time dependent performance space is by introducing a time dependent descriptions space (Figure 11). This can be done by for New example flexibility. Given that a design must be completed Contextual before it can start to operate, numerous decisions have to Information be made initially on how to deal with potential uncertainty throughout the lifecycle. Even though including real options that add value as it gives the opportunity to Time Time Continuous Dependent Dependent postpone decisions, providing the possibility to exercise Analysis Performance Design Space options must often be considered already in early stages of Space Functional Design the design process. In other words, one has to handle the requirements param. problem of identifying potential flexibilities, before they L,B,D,Cb Continuous may be relevant. This introduces the topic of handling Design known unknowns vs. unknown unknowns. Another problem that is relevant is to degree should flexibility be Figure 11: Time dependent design mapping process for built initially, or should it in turn be made as options to be handling uncertainty. exercised later.

Figure 11

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DESIGN AXIOMS/COMPLEXITY AND ASPECTS OF UNCERTAINTY When it comes to the two universal design axioms introduced by Suh (Suh, 1990), both “maintaining independence of functional requirements” and “reducing the information content of a design” involve seeking a reduction of complexity. A complex design may be mathematically chaotic, with an overall performance highly dependent on the contextual time dependent boundary conditions. My interpretation of this is that, since complex designs are more difficult to understand, they introduce more behavioral uncertainty - that is, if they will continue to fulfill the functional requirements throughout their life cycle. Uncertainties in this aspect can be related to how the design will perform statically, but also dynamically, with regard to the changing operating context. If the system is complex and difficult to understand, it makes value robustness more difficult to guarantee. From the two overall design axioms, several design corollaries can be derived. Corollary 2 - “Minimize the number of functional requirements (FRs) and constraints” can be derived from Axiom 2. This corollary states that as the number of FRs and design constraints increases, the complexity increases and thus raises the information content. In other words, this implies that the phrase “my design is better than yours because it can do more than was intended” in fact is rather mistaken (Suh, 1990). By following the two design axioms, a design should fulfill only its precise needs stated by its FRs, otherwise it becomes more complex and difficult to operate. One has indeed seen examples in the media recently 1 (2015) of multi-purpose, and highly complex ships, are more expensive to operate and in fact in the collapsing offshore market of 2014/2015, can be considered as multiuseless2! Some degree of complexity is though required in order to satisfy a typical set of FRs. When designing complex systems one can manage its complexity by simplification and decomposition into hierarchies. Aspects of handling complexity in conceptual ship design are discussed in Henrique Gaspar’s PhD thesis (Gaspar, 2013). An example of this in the maritime industry is modularity in shipbuilding, which frequently is used. By focusing on modularization in the operative part of a designs life cycle, and not only in shipbuilding processes, it can also be used as a methodology for handling future contextual uncertainty. It makes the design more flexible in the event of a retrofit to meet future needs. In this way, one can see that complexity and uncertainty have some connecting aspects.

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Superskipet som ble et mareritt (The super ship that became a nightmare), Accessed 17.06.2015, http://www.dn.no/nyheter/naringsliv/2015/06/14/2052/Oljeservice/superskipet-som-ble-et-mareritt 2 Notation adapted from Per Olaf Brett, vice president at Ulstein International. 9

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DESIGN METHODOLOGIES AND ASPECTS OF UNCERTAINTY Proper iterative detailed design methods, such as the Context traditional design spiral (Evans, 1959) reduce uncertainty related to how the design will actually perform once it is build - as this in fact is uncertain. All estimates are in the end only educated guesses, as physical models are simplifications of the real world. However, I will argue that the main focus in my PhD will be on handling aspects of hybrid/exogenous uncertainties - those that cannot directly, or can only partially, be managed by design decisions. Then focus then is typically not in the technical design procedure itself, as depicted in figure (Figure 12), but rather on the strategic planning of the design on the conceptual Figure 12: Design spiral in a context (Eyres, 2007). stage. Looking at conceptual design stage decisions, considering the uncertain aspects, Levander’s system based design approach (Levander, 2009) provides interesting insight. He focuses on clarifying the mission and functions, before the detailed iterative design is done (Figure 13). Even though he discusses the use of empirical data to estimate forms, the method does make it easier to generate alternative designs early and thereby opens up for easier innovation. This is in contrast to the traditional approach, where initial assumptions “locks” the design initially. In this way, one can say that Levander’s methodology is narrowing the gap between the business domain, typically in charge of the functional design requirements, and the engineering design domain. This makes it easier to focus on design solutions that can handle aspects of contextual uncertainty.

Figure 13: The ship design process (Levander, 2009).

In his book on risk based ship design (Papanikolaou, 2009, Papanikolaou discusses design by means of minimizing risk, as opposed to the traditional approach considering risk as fixed requirements in the design process. The risk in his point of view is on the safety side, and not directly on the risk of failing to provide value robustness, as I will focus on in my PhD. However, safety risk is indirectly correlated to the risk of failing to provide value robustness, as it can be viewed as a prerequisite for value robustness. If the design fails to comply to safety risk aspects, it cannot deliver value to the stakeholders. One can therefore say that perhaps focusing on wider aspects of risk in the design process will open up for new solutions, as the design space becomes larger. In their excellent state-of-the-art paper presented at the international maritime design conference (IMDC) 2015 (Andrews and Erikstad, 2015), Andrews and Erikstad give a broad overview on different design methodologies 10

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relevant for marine applications. Among many methods, they discuss “design-for-X” (DfX) developments, where “x” can be interchanged by concepts such as operability, flexibility, safety, profit or value robustness. The DfX framework is closely related to the goal-based design approach, and puts emphasis on the performance achievement of the design. The authors also discuss an extension of the DfX concept towards founding the performance on aggregated lifecycle based measures - often denoted as “design-ilities”. In a capital intensive and highly volatile marine industry, I would argue that these aspects are of particular importance. Interesting research related to this is done over the past years at SEAri at MIT 3. In terms of the methodologies discussed, the SEAri tradespace exploration and evaluation methods are of high interest in terms of handling uncertainty in marine systems design. They include multi-attribute tradespace exploration (MATE), epoch-era analysis (EEA), responsive system comparison method (RSC) and valuation approach for strategic changeability (VASC). In his PhD thesis, Gaspar focuses on these methodologies in terms of handling complexity (Gaspar, 2013). As complexity and uncertainty relate to some degree (complex behavior introduce more uncertainty) it is likely that much of the same thinking can be applied on my thesis subject. Epoch-era analysis and Monte Carlo simulation are methodologies of particular interest when it comes to generating scenarios for different design configurations, such as build-in flexibility, so that different solutions can be evaluated. Flexibility in engineering design is discussed in detail by Richard De Neufville in (De Neufville and Scholtes, 2011). When it comes to using flexibility in design of marine systems, it is important to consider whether designs should be built with initial flexibility, or only to have flexibility as an option to be called on later - with less initial capital binding. An option on flexibility could in a way also be called flexibility though, but is different and could be valued by slightly different approaches, similar to compound options in finance (options on options). SATISFICING VS. OPTIMIZING In his book in the sciences of the artificial, Simon discusses creating the artificial (chapter 5) (Simon, 1996). In light of his discussions of psychology of thinking (chapter 3) on problem solving, search strategies and memory, he elaborates on several theoretical and empirical methodologies that can be used in design. These include utility theory, optimization, dynamic programming, control theory, queuing theory and statistical decision theory. Despite all these available tools, he argues that actually finding an optimal design solution often is next to impossible for real life situations. For example, the simple traveling salesman problem (TSP) quickly becomes an extremely large problem, as the number of possible paths grows exponentially with the number of nodes. In such complex problems, we have to settle with good solutions, but probably not optimal. Simon labels these design methods as “satisficing”. In optimization we have heuristics that can find satisficing answers very quickly for many very difficult problems. Since finding the optimal solution for something that is uncertain is rather contradictory, I think it is evident to focus on “satisficing” methodologies in terms of handling uncertainty in preliminary design of marine systems. However, there are some methods that already try to take uncertainty into account. Stochastic optimization, for example, strives to find the optimum based on the expected value. The method can therefore settle on a solution that would be suboptimal given mean parameter values, but by taking different scenarios with corresponding probabilities into account, the solution space may look completely different. It is of intention to investigate these methodologies further in my PhD study. Another interesting point Simon touches upon, is searching for alternatives. Before one can build flexibility as real options into a design, one has to identify it. This can in itself be a cumbersome procedure, which is not

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Systems Engineering Advancement Research Initiative (SEAri), material on http://seari.mit.edu/ 11

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straight out of the book. When designing to handle uncertainty one also has to find realistic scenarios, which can be hard. However, it is of significant importance for the evaluation of the different designs. CRITICS Ackoff argues that operations research (OR) and mathematical decision support is “dead” on a general basis and that the chance of its “rebirth” is low since there is little understanding of its demise (Ackoff, 1979). So I ask, why are not decision support methodologies more used in marine system design, and will the methodologies I focus on in my PhD share the same fate? The maritime industry is usually considered to be conservative, capital intensive and volatile. Perhaps the uncertainty introduced by the high volatility makes the complexity of the decision-support more difficult than for other disciplines. I think that these three factors in particular make the gap between OR in academia and in the maritime industry even more dominant than for industry in general. Furthermore, the decision makers in this conservative industry are usually the owners, who typically are risk seekers and of relatively high age. This casual description is for privately held shipping companies, and not for major companies of national ownership. In terms of further research on quantifying risk and uncertainties in the maritime industry, it would be interesting to see how different stakeholders act with regard to future uncertainty. Ackoff discusses different reasons for the gap between academia and the industry. One is that OR in practice is too complicated to be easily modeled. OR problems also involves “predicting the future and preparing for it”, and it helps little to prepare for an imperfectly predicted future. In other words, optimization methodologies suddenly suffer from great value reduction as soon as there is any uncertainty in the picture. Methods that can properly quantify relevant aspects of risk and uncertainties for a problem is of great relevance, in other words. Hopefully, this can even close the so called gap between academia and the industry for decision support in design.

CONCLUSION It seems that even though design has been ever-present in the history of mankind, quite little research have been done on this topic, and particularly in the light of handling uncertainty. Many decision support models quickly lose their value in real life applications, as soon as aspects of uncertainty are introduced. This makes the research of handling uncertainty in system design of high relevance today. I hope that through my PhD I can contribute to closing the decision support gap seen between academia and the industry.

REFERENCES ACKOFF, R. L. 1979. The Future of Operational Research is Past. The Journal of the Operational Research Society, 30, 93-104. ANDREWS, D. & ERIKSTAD, S. O. 2015. State of the art report on design methodology. IMDC 2015. Tokyo, Japan. COYNE, R. D. D., ROSENMAN, M. A., RADFORD, A. D., BALACHANDRAN, M. & GERO, J. S. 1990. Knowledge-Based Design Systems, Addison-Wesley Longman Publishing Co., Inc. DE NEUFVILLE, R. & SCHOLTES, S. 2011. Flexibility in engineering design, Cambridge, Mass., Cambridge, Mass: MIT Press.

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NTNU: MR8100

Carl Fredrik Rehn

DIEROLF, D. A. & RICHTER, K. J. 1989. Computer-Aided Group Problem Solving for Unified Life Cycle Engineering (ULCE). Institute for Defence Analyses, Alexandria, Virginia, IDA Paper P-2149. EVANS, J. H. 1959. Basic design concepts. ANSE Journal. EYRES, D. J. 2007. Ship Construction (Sixth Edition), Oxford, Butterworth-Heinemann. FULLER, R. B., MCHALE, J. & INVENTORY, S. I. U. A. C. W. R. 1965. World Design Science Decade, 19651975: Five Two-year Phases of a World Retooling Design Proposed to the International Union of Architects for Adoption by World Architectural Schools, World Resources Inventory. GASPAR, H. M. 2013. Handling aspects of complexity in conceptual ship design. 2013:319, Norwegian University of Science and Technology, Faculty of Engineering Science and Technology, Department of Marine Technology. GREGORY, S. A. 1966. The Design Method, London; New York, Butterworths; Plenum Press. KAPURCH, S. J. 2010. NASA Systems Engineering Handbook, DIANE Publishing Company. LEVANDER, K. 2009. System Based Ship Design. PAHL, G. & BEITZ, W. 1996. Engineering Design: A Systematic Approach, Springer. PAPANIKOLAOU, A. 2009. Risk-Based Ship Design: Methods, Tools and Applications, Springer Berlin Heidelberg. SIMON, H. A. 1996. The Sciences of the Artificial (3rd ed.), MIT Press. SUH, N. P. 1990. The Principles of Design, New York, Oxford University Press. ULRICH, K. T. & EPPINGER, S. D. 2012. Product Design and Development, McGraw-Hill.

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