The evaluation and selection of cold chain logistics distribution centers are of vital importance for third-party logistics companies which want to build green cold chain logistics networks. To select distribution centers, the conflicts among multiple criteria should be considered. The combined compromise solutions (CoCoSo) method can help enterprises make a structural decision; however, in the original CoCoSo method, the evaluation information was expressed by crisp numbers. Nevertheless, in many cases, because of the imprecision and incompleteness of information, it may be more flexible for evaluators to provide imprecise and fuzzy values rather than crisp numbers. In addition, the judgment values are often expressed based on decision-makers' psychological expectations. The evaluation criteria of alternatives have relevance to some extent, which would influence the evaluation results. Based on these concerns, this study presents a modified CoCoSo method in the Pythagorean fuzzy environment in which evaluators can express psychological expectations on alternatives. To achieve this goal, the cumulative prospect theory is introduced to obtain the Pythagorean fuzzy prospect weights. Then, an objective weight determination method of criteria under the Pythagorean fuzzy environment is proposed to eliminate the influence of homogeneity of criteria. Based on the Pythagorean fuzzy prospect weights and the combined weights, the original CoCoSo method is extended to the Pythagorean fuzzy environment. A case of selection logistics distribution center is investigated to demonstrate the practicality of the proposed method. The advantages of the proposed method are verified by comparative analysis.

The standard cost function of multicategory single-layer perceptrons (SLPs) does not minimize the classification error rate. In order to reduce classification error, it is necessary to: 1) refuse the traditional cost function, 2) obtain near to optimal pairwise linear classifiers by specially organized SLP training and optimal stopping, and 3) fuse their decisions properly. To obtain better classification in unbalanced training set situations, we introduce the unbalance correcting term. It was found that fusion based on the Kulback-Leibler (K-L) distance and the Wu-Lin-Weng (WLW) method result in approximately the same performance in situations where sample sizes are relatively small. The explanation for this observation is by theoretically known verity that an excessive minimization of inexact criteria becomes harmful at times. Comprehensive comparative investigations of six real-world pattern recognition (PR) problems demonstrated that employment of SLP-based pairwise classifiers is comparable and as often as not outperforming the linear support vector (SV) classifiers in moderate dimensional situations. The colored noise injection used to design pseudovalidation sets proves to be a powerful tool for facilitating finite sample problems in moderate-dimensional PR tasks.