The selection of fresh product suppliers is a multi-criteria decision making (MCDM) problem with great significant and application value. This requires trade-offs between multiple criteria to prove its ambiguity and uncertainty. Therefore, a novel two-stage fuzzy integrated MCDM method to select suitable suppliers is employed. In the first stage, two collective relationship matrixes are constructed by quality function development (QFD), and relationships among customer requirements (CRs), company strategies (CSs) as well as selection criteria are considered separately in the two matrixes. Subjective criteria weights are obtained by fuzzy best-worst method (BWM) appropriately. In the second stage, the objective criteria weights are obtained using Shannon’s entropy method, and the fuzzy multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) is applied to rank suppliers. Finally, an application case is applied to prove the feasibility of the proposed method. These conclusions can help companies improve their CSs and increase their market competitiveness.

Low-carbon product design is an important way to reduce greenhouse gas emission. Customer collaborative product innovation (CCPI) has become a new worldwide product design trend. Based on this popularity, we introduced CCPI into the low-carbon product design process. An essential step for implementing low carbon CCPI is to clarify key low carbon requirements of customers. This study tested a novel method for perceiving key requirements of customer collaboration low-carbon product design based on fuzzy grey relational analysis and genetic algorithm. Firstly, the study considered consumer heterogeneity, allowing different types of customers to evaluate low carbon requirements in appropriate formats that reflected their degrees of uncertainty. Then, a nonlinear optimization model was proposed to establish the information aggregation factor of customers based on the genetic algorithm. The weight of customers was obtained simultaneously. Next, the key low carbon requirements of customer were identified. Finally, the effectiveness of the proposed method was illustrated with a case related to a low carbon liquid crystal display.

With the concept of sustainability gaining popularity, low-carbon tourism has been widely considered. In this paper, a multicriteria group decision making (MCGDM) process based on an uncertain environment is proposed to study the evaluation problem of low-carbon scenic spots (LSSs). In order to minimize the influence of subjective and objective factors, the traditional Vlse Kriterjumska Optimizacija I Kompromisno Resenje (VIKOR) method is expanded, using the improved best and worst method (IBWM) and Bayes approximation method, based on Dempster-Shafer Theory (B-DST). First, in order to make the evaluation process more professional, a number of evaluation criteria are established as effective systems, followed by the use of triangular intuitionistic fuzzy numbers (TIFNs) to evaluate alternatives of LSSs. Next, according to the evaluation results, the weights of the criteria are determined by the IBWM method, and the weights of the expert panels (Eps) are determined by B-DST. Finally, a weighted averaging algorithm of TIFN is used to integrate the above results to expand the traditional VIKOR and obtain the optimal LSS. The applicability of this method is proven by example calculation. The main conclusions are as follows: tourist facilities and the eco-environment are the two most important factors influencing the choice of LSSs. Meanwhile, the roles of management and participant attitudes in LSS evaluations cannot be ignored.

Sharing express boxes is an effective disposal method for obsolete express packages. Its appearance also represents an unstoppable trend in the development of green logistics. This paper takes the sharing express box as research object and conducts two-stage research. In the first stage, the collaborative consumption theory and calculus are used to analyze the economic benefits that sharing express boxes bring to operators, that is, to demonstrate the feasibility of this business model from an economic perspective. In the second stage, the design of the overlay network is studied from the management science perspective. Firstly, an optimal coverage model is established considering the characteristics of the sharing express box, and cleaning costs, relocation costs, etc. are all integrated into the model. Secondly, the genetic algorithm is used to solve the model. A numerical example is described to illustrate the feasibility of the proposed method. In addition, sensitivity analysis investigates the effect of hub coverage change on the results. Finally, the theoretical guidance gained from this paper can be beneficial to the sustainable development of sharing express boxes.