To solve the problem of long logistics delivery time in supply chain, a Mixed Integer Non-linear Program (MINLP) model is built by using Mixed Integer nonlinear programming theory. Firstly, the General algebraic modeling system (GAMS) is used to build the model to fully integrate each parameter of logistics transportation, the total distribution time of the supply chain network, the coverage radius of the logistics base, the number of users, the total capacity of the logistics base, the mode of railway and road transportation, the nonlinear programming model is built and solved by DICOPT solver in GAMS. The cost of logistics can be decreased, transportation time can be reduced, and the logistics system's operating efficiency can be increased in the long term with the help of this algorithm. The proper operation of the logistics system is critical in encouraging the supply chain circulation of various industries and has a direct impact on the society's economic development. The optimal logistics distribution plan with 5 logistics bases covered users of 18 and railway capacity of 2. With the same railway capacity and the same total budget, the larger the number of covered users, the greater the total distribution time increases, but the larger the total budget, the growth of the total distribution time slows down significantly. Experiments show that MINLP model can solve the problem of logistics-based layout optimization in nonlinear logistics management.

The trend of big data implies novel opportunities and challenges for improving supply chain management. In particular, supply chain risk management can largely benefit from big data technologies and analytic methods for collecting, analyzing, and monitoring both supply chain internal data and environmental data. Due to the increasing complexity, particular attention must not only be put on the processing and analysis of data, but also on the interaction between big data information systems and users. In this paper, we analyze the role of big data in supply chains and present a novel framework of a supply chain risk management system for improving supply chain planning and supply chain risk management under stochastic environments by using big data technologies and analytics. The process-oriented framework serves as a guideline to integrate and analyze big data as well as to implement a respective supply chain risk management system. As such, this paper provides a novel direction of utilizing big data in supply chain risk management.

In this paper, we study the effectiveness of incentives on delivery service time slot choices. In particular, we focus on the use of green labels that specify time slots as environmentally friendly and that intrinsically motivate customers to choose a specific delivery time slot in lieu of price incentives based on extrinsic motivation. We argue that this is important since green labels’ intrinsic nature affects costumer choice in fundamentally different ways than price incentives. We conduct two experiments and two simulation studies to study the effects of using green labels. Our experimental findings suggest that: (i) green labels are an effective tool to steer shoppers toward a certain delivery option, (ii) green labels are more effective for people who are more eco‐conscious, (iii) green labels remain effective in the presence of price incentives, while price incentives offer little added value beyond that of just green labels, and (iv) the effectiveness of green labels vs. price discounts remains high when time slots are less appealing (i.e., longer). Our simulation findings suggest that green slots, compared to price incentives or no incentives, offer providers a way to effectively steer consumer time slot choices to yield shorter routes, fewer delivery vehicles used, and more per‐customer revenue. We thus conclude that steering individuals to select delivery time slots through intrinsic motivation via green labels may be a promising, no‐cost direction for (online) retailers and an important topic for further research.

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