2 Design Catalogues : An Efficient Search Approach for Improved Flexibility in Engineering Systems Design Citation

This paper proposes and demonstrates design catalogues as a computationally efficient method for identifying improved designs for complex technological systems. This new process significantly speeds up the analysis of systems that will operate and will be managed under various uncertainty scenarios. It enables analysts to explore the design space more fully, taking into account a greater number of design parameters and variables. It can lead to design solutions with greatly improved lifecycle performance. The design catalogue consists of a small subset of designs that collectively perform reasonably well over a range of possible scenarios. The catalogue approach contrasts with the usual approach that optimizes designs for each scenario, and thus can only afford to examine a limited number of situations. Each design in the catalogue consists of combinations of design variables, parameters, and flexibility decision rules. The set of designs in the catalogue is determined using adaptive One-Factor-At-a-Time (aOFAT) analysis. An example demonstrates the use and how it leads to improved lifecycle performance compared to a standard benchmark design. Introduction Engineering systems are characterized by a high degree of technical complexity, social intricacy, and elaborate processes aimed at fulfilling important functions in society (ESD 2011). Given they are typically long-lived (+20 years), they face much uncertainty at strategic, tactical, and operational levels. Examples engineering systems include critical infrastructures for defense, emergency services, power generation and distribution, telecommunications, transportations, and housing. From a computational standpoint, the design of engineering systems today is a daunting task (Braha, Minai, and Bar-Yam 2006; de Weck, Roos, and Magee 2012). As summarized in Table 1, it involves modeling and optimizing basic infrastructures (e.g. plants, networks, etc.) considering a wide array of uncertainty scenarios (e.g. market demand, price, regulations) over long-term horizons. There can be many possible architectures and operating modes (e.g. number and size of plants, routing of vehicles on network, allocation of production lines to products, etc.) The system can also be evaluated based on many lifecycle performance indicators (e.g. net present value (NPV), return on investment (ROI), etc.) It is practically intractable to consider exhaustively all possible design combinations and alternatives. Table 1 Full analytical problem for designing engineering systems (Cardin 2007) Initial Design Uncertain Variables Managers Adjust Lifecycle Performance Physical infrastructure (Many possibilities) Price, demand for services (Many possibilities) Best use of existing facilities; development of additional facilities (Many possibilities) Realized net present value, rate of return, etc. (Many possibilities) A typical approach in engineering design and complex project evaluation is to simplify the full analytical problem. Instead of considering many scenarios of periodical data, designs are often optimized for the most likely projection of major uncertainty drivers (Braha, Minai, and Bar-Yam 2006; Eckert et al. 2009). Typical project evaluation approaches based on discounted cash flow (DCF) analysis, such as NPV, does not account for the fact that system operators will react periodically to enhance the system performance (Trigeorgis 1996). Also, design decisions are often based on one evaluation metric like internal rate of return (IRR), NPV, or ROI. The combination of such practices in systems design and evaluation can lead to sub-optimal choices, and even economic failure (Cardin et al. 2012). Flexibility in Engineering Systems Design: Computational Challenges There has been a great deal of effort over the last two decades to improve standard design and evaluation practice by making more explicit considerations of uncertainty. Flexibility in engineering design is one approach, which aims at designing an engineering system so it can adapt and change pro-actively in the face of uncertainty in environment, markets, regulations, and technology (de Neufville and Scholtes 2011; Nembhard and Aktan 2010). It is a different design paradigm from, for example, design for robustness, which aims at making systems function more consistent and invariant to changes in the environment, manufacturing, deterioration, and customer use patterns (Jugulum and Frey 2007). In the literature, flexibility in design is often referred to as a real option embedded “in” the system (Mikaelian et al. 2011; Wang and de Neufville 2005). Flexibility can improve expected lifecycle performance by affecting the distribution of possible outcomes. It reduces the effect from downside (like buying insurance), risky scenarios, while positioning the system to capitalize on upside, favorable opportunities (like buying a call stock option). For example, the 25 de Abril bridge connecting Lisbon to the municipality of Almada in Portugal was originally designed to carry four car lanes. Engineers accommodated the design for more car lanes if needed in the future, as well as a railway on its lower platform, should usage and demographic patterns warrant it. This flexibility in design later allowed expansion to the current six car lanes and two-railroad tracks infrastructure seen today. This strategy required a smaller initial investment than if full capacity was deployed, and deferred additional costs to the future, taking advantage of the time-value of money. It also enabled more traffic between the two cities today, contributing to a growing economy. Flexibility has shown improvements typically ranging between 10% and 30% compared to the outcome from standard design and evaluation practice in different industries: strategic phasing of airport terminals development (de Neufville and Odoni 2003; Gil 2007), offshore platforms designed for future capacity expansion (Jablonowski, Wiboonskij-Arphakul, and Neuhold 2008), strategic investments in new nuclear plant facilities (Rothwell 2006), supply chain adaptation to fluctuating exchange rates (Nembhard, Shi, and Aktan 2005), strategic investment in innovative water technologies (Zhang and Babovic 2012), etc. Examples abound. An important issue in designing engineering systems for flexibility is the complexity of the analytical problem. In addition to the many design variables and parameters to consider, designers need to account for a wide range of uncertainty scenarios, periodic managerial adjustments, and evaluation metrics. The case of Lin et al. (2009) illustrates the issue, working on the design of an oilrig with a major oil company. The company would use a highly complex model for the oil and gas infrastructure system, and optimize the design based on average historical price, and most likely original oil in place (OOIP) quantity (i.e. estimated reserves). Optimization for only one combination of oil price and OOIP scenario would require at least a day of intensive computations. If only three distinct price scenarios were considered in each year of a 20-year lifecycle (i.e. typical lifetime for such asset), the number of design configurations to investigate would be 3 ~3.5 billion. Consideration of so many scenarios is, as a practical matter, intractable, let alone with additional degrees of freedom stemming from explicit considerations of flexibility in design and management. In essence, the problem addressed in this paper is that the analysis of performance of an engineering system under uncertainty requires a special design or operating plan for each scenario. The definition of such plans and their lifecycle performance requires a lot of time and analytical resources, as exemplified above, so analysts can only look at a very few designs fully. Therefore, solutions possibly improving lifecycle performance cannot be examined due to time and analytical constraints. Proposed Solution This paper addresses this issue by devising a priori a set of operating plans or models that together deal reasonably adequately with a range of uncertainty scenarios. This speeds up the analysis for the most relevant plans, allows analysts to consider more design alternatives, and enables uncovering better design solutions with improved lifecycle performance. The proposed solution suggests a middle-ground approach standing between the simplest set of assumptions typically made in design and evaluation, and the full analytical problem summarized in Table 1. It relies on a representative range of possibilities small enough to be manageable analytically, and broad enough to enable a more informed analysis of the design problem. The aim is to provide a practical approach leading designers to rapid lifecycle performance improvements by explicit considerations of flexibility. It is also efficient in terms of the computational and analytical resources required. Design Catalogues. The method simplifies the analysis by relying on the concept of a design catalogue. The catalogue provides a limited number of scenarios and responses intended to describe relevant patterns designers might wish to anticipate. An operating plan is therefore a combination of design variables, parameters, and flexible decision rules to manage the engineering system in operations, and over its lifecycle. For instance, instead of considering explicitly all possible 3.5 billion price scenarios, only a handful representative scenarios are considered. One operating plan is created to suit each scenario, thereby creating the catalogue. 1 More case studies are available at http://ardent.mit. edu/real_options/Common_course_materials/papers.html, http://strategic.mit. edu/publications.php, and http://seari.mit. edu/publications.php. Representative uncertainty scenarios can be selected based on different criteria, and operating plan crafted to deal best with each scenario. For example, consider a scenario where prices rise steadily at first, and star