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Whether the upstream and downstream members in a supply chain (considering environmental objectives) simultaneously stabilize economic benefits has become an important problem in the process of green development. However, few quantitative studies on green supply chains have considered environmental and economic benefits to realize multi-objective optimization. To study operation and cooperation strategies with a consideration of the different objective on the level of supply chain, we first establish a green supply chain game model with profit and environment objectives simultaneously considered by the manufacturer. Then, we analyze the multi-objective decisions of the supply chain members under centralized control using a manufacturer-led Stackelberg game and revenue-sharing contract. Using the manufacturer's environmental preference as a variable, the effects of environmental benefits on the supply chain are also investigated. Finally, this study determines that the manufacturer's profit will be reduced after considering the objective of environmental benefits, while the retailer's profit, product greenness, and environmental benefits will be improved. Meanwhile, the total profit of the green supply chain will first increase and then decrease. In particular, a revenue-sharing contract can facilitate the coordination of multiple objectives; in this way, both the manufacturer and the retailer achieve higher profits and environmental benefits compared to a decentralized control condition, which is of great significance in achieving a win-win situation for the economy and the environment.

In this study, we examined the contract coordination between manufacturers with peer-induced and distributional fairness concerns. A revenue sharing contract was introduced to coordinate a competitive supply chain, in which the manufacturers have different fairness concerns based on centralized decision-making in terms of fairness neutrality. Then, we constructed two game models—the manufacturer’s peer-induced fairness concern model and the manufacturer’s distributional fairness concern model and analyzed the influence of a revenue sharing contract on the pricing decisions and profit distribution of a competitive supply chain considering fairness concerns. The results show that there is a revenue-sharing contract parameter in both the peer-induced and distributional fairness concerns of manufacturers, which can effectively realize Pareto improvements in a supply chain. Meanwhile, the retail and wholesale prices both decreased with the increase in the revenue-sharing ratio between retailers and manufacturers, and the profits of retailers decreased accordingly, but the overall utility of manufacturers and supply chains improved markedly. Moreover, the coordination condition is closely related to the level of fairness concerns of the manufacturers and the competition intensity between two manufacturers. The sharing contract designed in this study can not only effectively improve the utility of retailers and manufacturers but also enhance the total utility of the channel to ensure that node enterprises have long-term, stable, and cooperative relationships and to strengthen the overall competitiveness of the supply chain.

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As the first stage of the formation of a collaborative new product innovation (CNPI) team, member selection is crucial for the effective operation of the CNPI team and the achievement of new product innovation goals. Considering comprehensively the individual and collaborative attributions, the individual knowledge competence, knowledge complementarity, and collaborative performance among candidates are chosen as the criteria to select CNPI team members in this paper. Moreover, using the fuzzy set and social network analysis method, the quantitative methods of the above criteria are proposed correspondingly. Then, by integrating the above criteria, a novel multiobjective decision model for member selection of the CNPI team is built from the view of individual and collaborative attributions. Since the proposed model is NP-hard, a double-population adaptive genetic algorithm is further developed to solve it. Finally, a real case is provided to illustrate the application and effectiveness of the proposed model and method in this paper.

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Purpose Selecting suitable and competent partners is an important prerequisite to improve the performance of collaborative product innovation (CPI). The purpose of this paper is to propose an integrated multi-criteria approach and a decision optimization model of partner selection for CPI from the perspective of knowledge collaboration. Design/methodology/approach First, the criteria for partner selection are presented, considering comprehensively the knowledge matching degree of the candidates, the knowledge collaborative performance among the candidates, and the overall expected revenue of the CPI alliance. Then, a quantitative method based on the vector space model and the synergetic matrix method is proposed to obtain a comprehensive performance of candidates. Furthermore, a multi-objective optimization model is developed to select desirable partners. Considering the model is a NP-hard problem, a non-dominated sorting genetic algorithm II is developed to solve the multi-objective optimization model of partner selection. Findings A real case is analyzed to verify the feasibility and validity of the proposed model. The findings show that the proposed model can efficiently select excellent partners with the desired comprehensive attributes for the formation of a CPI alliance. Originality/value Theoretically, a novel method and approach to partner selection for CPI alliances from a knowledge collaboration perspective is proposed in this study. In practice, this paper also provides companies with a decision support and reference for partner selection in CPI alliances establishment.