۱- Introduction

۲- Background

۳- Related work

۴- Research design

۵- Results

۶- Discussion

۷- Conclusion

References

abstract

Governments around the world instituted guidelines for calculating energy efficiency of vehicles not only by models, but by the whole universe of new vehicles registered. This paper compiles Multi-criteria decision-making (MCDM) studies related to automotive industry. We applied a Systematic Literature Review on MCDM studies published until 2015 to identify patterns on MCDM applications to design vehicles more fuel efficient in order to achieve full compliance with energy efficiency guidelines (e.g., InovarAuto). From 339 papers, 45 papers have been identified as describing some MCDM technique and correlation to automotive industry. We classified the most common MCDM technique and application in the automotive industry. Integrated approaches were more usual than individual ones. Application of fuzzy methods to tackle uncertainties in the data was also observed. Despite the maturity in the use of MCDM in several areas of knowledge, and intensive use in the automotive industry, none of them are directly linked to car design for energy efficiency. Analytic Hierarchy Process was identified as the common technique applied in the automotive industry.

Introduction

Decision process can be defined as a set of actions and methods dynamically organized. This process is triggered by demand for action and it ends with a specific engagement execution [1]. Corporations have to choose the best option by aggregating outcomes of different stakeholders [2]. Although the decision-making problem could be constructed as more than one hierarchy with different criteria [3] to be solved, this process is still hard due to the following:
They are non-repetitive, unprecedented and unique [2].
Criteria may conflict itself, for example, customers want quality but they also want something not expensive. Conflicting criteria make the decision task tough [4,5].
Criteria such as fuel consumption can be objectively measured, commonly named as tangible criteria. Flexibility, quality, efficiency or future income, cannot. This group is classified as intangible criteria. Intangible criteria cannot be converted into numeric or monetary values [6,7]. As proposed by law [8], energy efficiency (EE) should not be calculated by models only, but by the whole universe of new vehicles registered. In this scenario, the composition of vehicles sold in the market will have influence on profits of each automaker, since additional taxes are going to be applied for those automakers that do not achieve a specified target. Among all variables to be considered, one can highlight that analysis of manufacturing costs, customer value perception and market share, can be characterized as a multi-criteria decision-making problem. Due to the increasing competition, dynamic customer demands and regulatory laws, the scenario requires automakers to add energy efficiency items to practically the entire portfolio.