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The cooperative and competitive (i.e., co-opetition) behavior between retailers plays a significant role in the development of operations and marketing strategies in a supply chain. Specifically, retailers’ co-opetition relationship pivotally influences the sustainable performance in a closed-loop supply chain. This study examines the impact of retailer co-opetition on pricing, collection decisions and coordination in a closed-loop supply chain with one manufacturer and two competing retailers. Based on observations in some industries (e.g., electronic manufacturing, fabric and textile, etc.), the cooperative and competitive relationships between retailers can be classified into the following three different modes: Bertrand competition, Stackelberg competition, and Collusion. In this paper, we establish a centralized and three decentralized game-theoretic models under these three co-opetition modes and characterize the corresponding equilibrium outcomes. The results indicate that the Bertrand competition mode yields the highest return rate, which is also superior to the other two modes for both the manufacturer and the supply chain system in terms of profitability. However, it can be shown that which mode benefits the retailers would depend on the degree of competition between the retailers and the relative remanufacturing efficiency. Interestingly, we find that the retailer’s first-move advantage does not necessarily lead to higher profits. In addition, we design a modified two-part tariff contract to coordinate the decentralized closed-loop supply chains under three different retailer co-opetition modes, and the results suggest that the optimal contractual parameters in the contracts highly rely on the remanufacturing efficiency and the competition degree between the two retailers. Several managerial insights for firms, consumers and policy makers are provided through numerical analysis.

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In this paper, an airport ground service task assignment problem is studied. A task represents a service, which must be performed by one or multiple ground crew of a shift with required qualification/proficiency within a prescribed time period. For every assigned task, define “task priority” times “task duration” as the “benefit” generated. The objective is to maximize the summation of “benefit” for all the assigned tasks. The problem is modeled as an integer linear programming problem with mathematical formulation. A branch-and-price algorithm is proposed for solving the problem instances to optimality. To expedite the column generation process, an acceleration strategy is proposed. The computational results show that our proposed branch-and-price algorithm is capable of solving large-sized instances and the acceleration strategy is quite effective in reducing the computational time. Moreover, the impact of changing various characteristics of tasks and shifts on the performance of the algorithm is studied in detail with supporting computational experiments. In particular, the impact of reducing the qualifications is significant with 20.82% improvement in the objective value.