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Distribution centers (DCs) are an important part of the modern logistics system. The selection of a location for a DC is significant for saving costs and reducing externalities caused by distribution. In this paper, we propose a new hybrid method based on the analytic hierarchy process (AHP) and 2-tuple hybrid ordered weighted averaging (THOWA) to select the location of a DC in a megacity. First, we propose a new set of evaluation criteria integrating economic, political, social and ecological information based on the characteristics of Chinese megacities. Second, subjective criteria weights are calculated by AHP combining the evaluation of logistics experts. Third, experts from academia, enterprise and government assess the performance of alternatives. In addition, the overall evaluation values are aggregated by an improved THOWA operator to rank the alternatives. Finally, we conduct a sensitivity analysis to investigate the influence of criteria weights on the decision-making process. The proposed method is novel and addresses the uncertainty under limited quantitative information, which has the advantages of avoiding information loss and distortion problems in the integrating process and operating linguistic evaluation information effectively. The proposed method can be practically applied by municipal planning departments in deciding on the location of new DCs. A numerical application of the proposed method is provided. [ABSTRACT FROM AUTHOR]

Little is known about the interplay between affective and cognitive processes of decision making within the bounded rationality perspective, in particular for the debate on adaptive decision making and strategy selection. This gap in the knowledge is particularly important as affect and deliberation may direct preferences in opposite directions. How do decision makers solve such dissonance? In this paper, we address this question by exploring the use of integral affect as a choice heuristic in comparison with and in conjunction to “take the best,” and weighted addition of attributes (WADD). We operationalize theories of reliance on affect in choice through a "Take the emotionally best" algorithm. Its predictive power is experimentally tested against other models, including mixed-sequential cognitive/affective procedures. We find that individual decisions are better predicted by a sequential combination of "Take the emotionally best" and "Take the best" with a slight dominance of the former. Conditions of cognitive/affective ambivalence, low discrimination ability and high complexity provide the cognitive architecture where such blended choice strategies predict decisions more precisely. This implies that reliance on integral affect may precede the use of cognitive cues following an ecological rationality perspective rather than supporting a kind of competition between affect and cognition as implied in current literature. [ABSTRACT FROM AUTHOR]

For urban rail transit network, the space-time flow distribution can play an important role in evaluating and optimizing the space-time resource allocation. For obtaining the space-time flow distribution without the restriction of schedules, a dynamic assignment problem is proposed based on the concept of continuous transmission. To solve the dynamic assignment problem, the cell transmission model is built for urban rail transit networks. The priority principle, queuing process, capacity constraints and congestion effects are considered in the cell transmission mechanism. Then an efficient method is designed to solve the shortest path for an urban rail network, which decreases the computing cost for solving the cell transmission model. The instantaneous dynamic user optimal state can be reached with the method of successive average. Many evaluation indexes of passenger flow can be generated, to provide effective support for the optimization of train schedules and the capacity evaluation for urban rail transit network. Finally, the model and its potential application are demonstrated via two numerical experiments using a small-scale network and the Beijing Metro network. [ABSTRACT FROM AUTHOR]

Finding a universal description of the algorithm optimization is one of the key challenges in personalized recommendation. In this article, for the first time, we introduce a scaling-based algorithm (SCL) independent of recommendation list length based on a hybrid algorithm of heat conduction and mass diffusion, by finding out the scaling function for the tunable parameter and object average degree. The optimal value of the tunable parameter can be abstracted from the scaling function, which is heterogeneous for the individual object. Experimental results obtained from three real datasets, Netflix, MovieLens and RYM, show that the SCL is highly accurate in recommendation. More importantly, compared with a number of excellent algorithms, including the mass diffusion method, the original hybrid method, and even an improved version of the hybrid method, the SCL algorithm remarkably promotes the personalized recommendation in three other aspects: solving the accuracy-diversity dilemma, presenting a high novelty, and solving the key challenge of cold start problem. [ABSTRACT FROM AUTHOR]