A multi-criteria decision-making framework for electric vehicle supplier selection of government agencies and public bodies in China

Abstract

Electric vehicle deployment shows promising potentials in promoting cleaner energy utilization and reducing carbon emission. Due to increasing carbon neutral pressure and market competition from transportation sector, government agencies and public bodies (GAPBs) have emphasized the significance of electric vehicle adoption through supplier selection. Consequently, GAPBs must consider a reasonable criteria system and a comprehensive supplier selection framework and rationally select the electric vehicle supplier that matches their practical needs in terms of economic, social, environmental, and technical factors. This paper provides insights into electric vehicle supplier selection (EVSS) from the perspective of GAPBs using an integrated multi-criteria decision-making (MCDM) framework based on best-worst method (BWM) and fuzzy ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Initially, 14 critical factors from economic, social, environmental, and technical dimensions are identified as the criteria by literature review and experts' opinions. Then, a comprehensive decision framework using the integrated MCDM approach is proposed. To validate the applicability and feasibility of the proposed framework, a case study is launched and analyzed. It emerges that bad environmental record, cost, quality, service, and environmental initiatives are the most important criteria in EVSS for GAPBs with the weight values of 0.1995, 0.1172, 0.1219, 0.0708, and 0.2553. The comparative analysis and the sensitivity analysis are performed for verifying the reliability of the proposed framework. The work helps to understand the electric vehicle supplier selection criteria and makes methodological decision-making support for GAPBs.

Priority Selection of Road Traffic Net-Works in Emergency Situations Based on Internet of Vehicles

“…Due to the lack of effective communication technology in the past, research in this fieldis relatively scarce. With the development of IoV, fast communication between vehicles is achieved, which has aroused the research interest of scholars [13][14]. Liu Z et al aimed to improve the driving efficiency of EVs under high road traffic density, and an automatic yielding system was designed by utilizing vehicle to vehicle communication in IoV.…”

“…However, when the number of vehicles and traffic density were equal, the decrease in C1 compared to B1 was smaller. Similarly, the actual adjustment time of vehicle C1, the total dynamic trajectory adjustment time of these two fleets, and the minimum communication distance between EV and B1 were calculated using equations ( 11), (14), and (15) in Figure 11. From Figure 11 (a), except for the density of fleet C which was +0.0333m-1, the actual adjustment time of vehicle C1 at all other densities was gradually increasing.…”

“…is calculated using the equations in the third and fourth rows of equation(13). The total time required for adjusting the actual dynamic trajectories of fleets B and C is represented by equation(14).…”

Addressing the supplier selection problem by using the analytical hierarchy process

“…The supplier selection problem considers economic, environmental, and social concerns [ 12 ]. Supplier selection is one of the most prominent issues for a sustainable business, and the adverse environmental history, cost, quality, service, and environmental initiatives are the top attributes for electric vehicle supplier selection [ 13 ]. However, different organizations have different criteria for supplier selection problems based on the characteristics of organizations.…”

A Review of Smart Contract Blockchain Based on Multi-Criteria Analysis: Challenges and Motivations

“…In addition, the preferences (criteria weight) is yet set for the unified evaluation matrix. MCDM helps the decision maker to obtain the best solution based on the critical criteria the decision maker emphasized in a set of conflicting critical criteria [5]. Furthermore, by adopting the MCDM method, decision-makers can be more accurate and faster, increasing the overall performance of a process.…”