Reliability-based design optimization (RBDO) allows decision-makers to achieve target reliability in product performance under engineering uncertainties. However, existing RBDO studies assume the target reliability as a given parameter and do not explain how to determine the optimal target reliability. From the perspective of the market, designing a product with high target reliability can satisfy many customers and increase market demand, but it can generate a large cost leading to profit reduction of the company. Therefore, the target reliability should be a decision variable which needs to be found to maximize the company profit. This paper proposes a reliabilitybased design for market systems (RBDMS) framework by integrating RBDO and design for market systems (DMS) approaches to find the optimal target reliability. The proposed RBDMS framework is applied to electric vehicle (EV) design problems to validate effect of the target reliability on company profit – or market share – and engineering performances of EV. Several observations about the optimal target reliability are presented from the case study with various scenarios. From the EV design case study, it is verified that the proposed RBDMS framework is an effective way of finding the optimal target reliability that maximizes the company profit, and the optimal target reliability varies depending on the situation of market and competitors.
Manufacturers must decide when to invest and launch a new vehicle segment or how to redesign vehicles existing segment, both under market uncertainties. We present an optimization framework for redesigning or investing in future vehicles using real options to address uncertainty in gas price and regulatory standards like the US Corporate Average Fuel Economy (CAFE) standard. In a specific study involving a product of gasoline, hybrid electric, and electric vehicles, we examine the relationship between gas price and CAFE uncertainties to support decisions by manufacturers on product mix and by policy makers on proposing standards. A real options model is used for the time delay on investment, redesign, and pricing, integrated with a robust design formulation to optimize expected net present value (ENPV) and net present value (NPV) robustness. Results for nine different scenarios suggest that policy makers should consider gas price when setting CAFE standards; and manufacturers should consider the trade-off between ENPV and robust NPVs. Results also suggest that change of product mix rather than vehicle redesign better addresses CAFE standards inflation.
When designing a product, both engineering uncertainty and market heterogeneity should be considered to reduce the risk of failure in the market. Reliability-based design optimization (RBDO) approach allows decision makers to achieve target confidence in product performance under engineering uncertainty. Design for market systems (DMS) approach helps decision makers to find profit-maximized product design under market heterogeneity. This paper integrates RBDO and DMS approaches for an Electric vehicle (EV) design. Consumers’ preferences on warranted battery lifetime are heterogeneous while battery life itself is affected by various uncertainties such as battery characteristics and driving patterns. We optimized and compared four scenarios depending on whether engineering systems are deterministic or probabilistic, and whether a market is homogeneous or heterogeneous. The results provide some insight on how the optimal EV design should be altered depending on engineering uncertainty and market heterogeneity.
Governments encourage use of electric vehicles (EV) via regulation and investment to minimize greenhouse gas (GHG) emissions. Manufacturers produce vehicles to maximize profit, given available public infrastructure and government incentives. EV public adoption depends not only on price and vehicle attributes, but also on EV market size and infrastructure available for refueling, such as charging station proximity and recharging length and cost. Earlier studies have shown that government investment can create EV market growth, and that manufacturers and charging station operators must cooperate to achieve overall profitability. This article describes a framework that connects decisions by the three stakeholders (government, EV manufacturer, charging station operator) with preferences of the driving public. The goal is to develop a framework that allows the effect of government investment on the EV market to be quantified. This is illustrated in three scenarios in which we compare optimal public investment for a city in USA (Ann Arbor, Michigan) and one in China (Beijing) to minimize emissions, accounting for customer preferences elicited from surveys conducted in the two countries. Under the modeling assumptions of the framework, we find that high customer sensitivity to prices, combined with manufacturer and charging station operator profit maximization strategies, can render government investment in EV subsidies ineffective, while a collaboration among stakeholders can achieve both emission reduction and profitability. When EV and station designs improve beyond a certain threshold, government investment influence on EV adoption is attenuated apparently due to diminishing customer willingness to buy. Furthermore, our analysis suggests that a diversified government investment portfolio could be especially effective for the Chinese market, with charging costs and price cuts on license plate fees being as important as EV subsidies.
A major barrier in consumer adoption of electric vehicles (EVs) is “range anxiety,” the concern that the vehicle will run out of power at an inopportune time. Range anxiety is caused by the current relatively low electric-only operational range and sparse public charging station (CS) infrastructure. Range anxiety may be significantly mitigated if EV manufacturers and CS operators work in partnership using a cooperative business model to balance EV performance and CS coverage. This model is in contrast to a sequential decision-making model where manufacturers bring new EVs to the market first and CS operators decide on CS deployment given EV specifications and market demand. This paper proposes an integrated decision-making framework to assess profitability of a cooperative business model using a multidisciplinary optimization model that combines marketing, engineering, and operations considerations. This model is demonstrated in a case study involving battery EV design and direct current (DC) fast-CS location network in Southeast Michigan. The expected benefits can motive both government and private enterprise actions.