This paper studies the scheduling optimization in casualty transportation after large-scale natural disasters, with a particular focus on the impact of injury heterogeneity on dispatch decisions. By considering different deprivation cost functions, we quantify the economic value of the pain endured by casualties before receiving medical treatment. An exact mathematical programming model is developed to minimize total economic and deprivation costs and optimize transportation strategies for critically and mildly injured patients. This model incorporates a collaborative ambulance-air rescue transportation mode that prioritizes air rescue for critically injured patients under emergency medical requirements. By using the historical medical rescue data from Jianyang City in Sichuan Province of China, we verify the effectiveness of the model through a case study by CPLEX 12.8.0 solver. Sensitivity analysis further provides some theoretical foundations and practical guidance for post-disaster casualty transportation planning.

To alleviate the problem of long waiting time in general hospitals and underutilization of community hospital resources, this paper explores the effect of government mixed subsidies on patient choice and utilization of community hospital resources. Based on hospital service quality, we establish a benchmark model without government subsidy and a bilevel optimization model in which the government makes the upper-level decisions while the community hospital responds at the lower level. Although the benchmark model is a nonconvex optimization model, we are based on its special structure to propose a globally optimal algorithm. Then, we combine this globally optimal algorithm with the Gauss-Seidel method to solve the bilevel optimization model. Furthermore, we present a sensitivity analysis of some key parameters such as service quality costs, basic medical expenses in community hospitals, and government subsidy budget. Theoretical and numerical results provide the following insights: Government mixed subsidy may improve community hospital service quality and so attract more patients to choose community hospital; Government should focus on fixed fund subsidies to improve community hospital service quality, since hospital service quality is more critical than medical expenses for patient choices; Controlling service quality costs and basic medical expenses in community hospital at a low level and paying more attention to fund subsidies can promote hierarchical healthcare. Meanwhile, the government subsidy budget should be decided aligning with defined societal goals.

This paper considers a dual-channel supply chain with two members, comprising a manufacturer and an online platform. We mainly investigate the influence of various key system variables on manufacturer encroachment strategy and all members’ optimal decisions through Stackelberg game models. Our findings show that, regardless of the size of each parameter, the encroachment strategy is always optimal to the manufacturer; the manufacturer may be motivated to choose the direct selling channel and the platform may opt for the agency selling channel due to a high commission rate. Moreover, when the inter-channel substitution rate is high, the encroachment strategy has a diminishing positive effect on the manufacturer and an increasing negative effect on the platform so that the platform may temporarily benefit from the manufacturer encroachment; in cases where the inter-channel substitution rate is not high, the encroachment strategy always yields advantages for the manufacturer while causing disadvantages for the platform. In addition, if the elasticity coefficient is large, both the manufacturer and the platform are inclined to the reselling channel, that is, if the platform service cost is high, it is advisable for the platform to reduce its investment of service to avoid negative effect.

This paper considers a sales mode selection problem under revenue sharing contracts between resale and agency modes for risk-averse manufacturers with traditional retail channel, direct selling channel, and e-commerce platform channel. By considering the factors including price competition intensity, market share, revenue sharing ratio, commission rate, and degree of risk aversion, we construct leader-follower game models with manufacturers as leaders and traditional retailers and e-commerce platforms as followers. To obtain optimal solutions, we discuss conditions to ensure the upper and lower models to be convex and then give the optimal strategies for all members in the network. Through numerical experiments, we analyze the involved parameters’ impact on sales mode selection strategy and the changing trends of each member's optimal pricing and profit under different sales modes. The numerical results reveal the following revelations: The manufacturer should choose the agency mode when the commission rate is low and the direct selling channel has a large market share. If both the commission rate and degree of risk aversion are high, direct selling channels have a low market share, and price competition intensity is weak, the manufacturer should choose the resale mode. The degree of risk aversion has an effect on each member’s optimal decision. Regardless of which sales mode the manufacturer chooses, the optimal price of each member decreases as the degree of risk aversion increases. Under certain conditions, the manufacturer’s choice of agency mode can create win-win situations with supply chain members.

The growing popularity of aggregation platforms has attracted widespread attention in the ride-hailing market in recent years. In order to obtain additional orders by charging commissions and slotting fees, many ride-hailing platforms choose to bundle with aggregation platforms. Unlike traditional reseller electronic channels, the bundle channels may affect pricing of platforms, service levels of drivers, market demands and they may further impact on profits. These different attitudes raise an interesting and key question about the influence of bundle channels in ride-hailing platforms. In this paper, we propose an analytical framework for pricing strategies of ride-hailing and aggregation platforms under bundle mode and analyze their pricing process from the perspective of Nash and Stackelberg games, where the platforms serve as leaders to determine optimal prices through Nash equilibrium and the drivers serve as followers to provide optimal service levels. Through sensitivity analysis of service levels and costs, we capture the distribution trends of profits between the platforms. Based on some numerical examples and results analysis, some interesting managerial insights on pricing of ride-hailing and aggregation platforms are gained.