In the cloud manufacturing environment, innovative service composition is an important way to improve the capability and\nefficiency of resource integration and realize the upgrading and transformational upgrade of the manufacturing industry. In order\nto build a stable innovative service composition, we propose a novel composite model, which uses two-way selection according to\ntheir cooperation to recommend the most suitable partners. Firstly, a rough number is applied to quantify the semantic evaluation.\nUsing the expectation of cooperative condition as reference points, prospect theory is then applied to calculate the cooperative\ndesires for both sides based on participantsâ?? psychological attitudes toward gains and losses. Next, the cooperative desires are used\nto establish the two-way selection model of innovative service composition. The solution is determined by using an improved\nteaching-learning-based optimization algorithm. Compared with traditional combined methods in the cloud manufacturing\nenvironment, the proposed model fully considers the long-neglected needs and interests of service providers. Prospect theory\ntakes psychological expectations and varying attitudes of decision makers towards gains and losses into account. Moreover, an\ninterval rough number is used to better preserve the uncertain information during semantic quantification. Experimental results\nverify the applicability and effectiveness of the proposed method.
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