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With respect to multiple attribute decision making (MaDM) problems in which attribute values take the form of interval grey linguistic variables, a new decision making analysis method is developed. In this paper, we propose the interval grey linguistic variables ordered weighted aggregation (IgLOWa) operator, and then use the Choquet integral to develop the interval grey linguistic correlated ordered arithmetic aggregation (IgLCOa) operator and the interval grey linguistic correlated ordered geometric aggregation (IgLCOga) operator. Those operators not only consider the importance of the elements, but also can reflect the correlations among the elements. Then, we develop an approach to multiple attribute decision making problems with correlative weights which attribute values are given in terms of interval grey linguistic variables information based on those operators. Finally an illustrative example is given to use the method in the range of uncertain multiple attribute decision making. The results show that the method proposed in this paper is feasible.Keywords: Choquet integral, multiple attribute decision making problems, interval grey linguistic variables, interval grey linguistic correlated ordered arithmetic aggregation (IgLCOa) operator, interval grey linguistic correlated ordered geometric aggregation (IgLCOga) operator. 1 1 0.4 m B x m x τ = = Nian ZHANG is working for a doctor's degree at the School of Economic & Management, Southwest Jiaotong University, China. he has contributed over several journal articles to professional journals, such as applied Mathematical Modelling and Mathematical Problems in Engineering. his current research interests include decision-making theory, logistics engineering and management, and cooperative game theory.

Due to inadequate designers in fast fashion industry and the development of the Internet, small-and-medium-sized garment makers have gradually turned to external talents to enhance their new product design efficiency via crowdsourcing initiative. This paper presents a new framework of crowdsourcing supply chain for fast fashion industry. First, a basic multiperiod order model is established in a crowdsourcing supply chain system, where a garment maker chooses the best one among available solutions submitted by online designers (i.e., crowdsources) in each period, then transforms it into finished product, and sells to consumers through a retailer. Second, we extend this model by integrating the factors of capital turnover, the retailer’s risk-aversion, and the garment maker’s minimum production quantity. Moreover, utilizing wholesale price, buyback, and profit-sharing scheme designs a mixed contract for coordinating crowdsourcees, retailer, and garment maker of crowdsourcing supply chain for achieving Pareto optimality. The models help the garment maker determining the optimal production quantities of crowdsourcing designed products and enable the retailer placing the optimum orders and setting the reasonable risk level. In addition, we also find that the incentive policy for crowdsourcing designers can be fulfilled by using a profit-sharing scheme with a piecewise function of order quantity instead of a linear function.