The purpose of this study is to propose a fuzzy analytic hierarchy process to calculate the criteria weight for evaluating fashion design schemes. The criteria weight is used in the synthetic evaluation method to calculate the index value of each scheme for the selection of the best fashion design. There are four schemes and eight criteria to be evaluated. These four schemes are Academy Look (scheme 1), Country Girl (scheme 2), Ballerina (scheme 3), and Gypsy Girl (scheme 4). The eight criteria are Fashion Forecast, Theme Story, Best-seller Modification, New Idea, Product Position, User Value, Opponent Ability, and Brand Image. To test consistency, another method called the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is also proposed to calculate the index value of each scheme for the best selection of the fashion design. We find out that the TOPSIS works better than the synthetic evaluation in the selection of fashion design scheme. The TOPSIS uses the concept of the positive-ideal solution and the negative-ideal solution. This method differentiates significantly one scheme from another. This feature makes it much easier for the fashion designer in the selection of the best fashion design.

Currently, energy management control mainly focuses on single-objective optimization (SOO). Even if multi-objective optimization (MOO) problem is studied, it is often converted into an SOO problem by using the weighted sum method. Obviously, it cannot really reflect the essential strengths of MOO. In this paper, a parallel hybrid electric vehicle is taken as the research object. The fuel economy, emissions, and drivability performance are taken as optimization objectives. The parameters of energy management and driveline system are optimized. Considering the constraint conditions of the dynamic performance and charge balance, the fast non-dominated sorting differential evolution algorithm (NSDEA) is proposed to solve the multi-objective optimization problem. Then multi-group sets of Pareto solutions with good distribution and convergence are obtained. The simulation results of NSDEA show that the fuel economy is increased by 20.26% on average. The emissions evaluation index is optimized by 11.33% on average, and the maximum carbon monoxide (CO) optimization value reaches 21.9%. The average of drivability evaluation index (jerk) is up to 20.84%, and 40.32% for maximum. Obviously, the above obtained results are discrete points. They only represent some optimal solutions. Based on the above sets, the locally weighted scatter plot smoothing method is used to fit continuous curve and surfaces. Then, the multi-objective Pareto trade-off optimal control surface is established to further obtain the optimal solutions. This study can provide more reference for the optimal control strategy and lay a foundation for multi-objective energy management of the actual vehicle.