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Supply chain management (SCM) refers to the processes from the beginning of procurement to the end customer, optimizing SCM with minimal cost. It is viewed as a business philosophy, which extends the concept of partnerships into a multi-firm effort to manage the total flow of goods from the supplier to the ultimate customer. It includes the movement and storage of raw materials, work-in-process inventory, and finished goods from point of origin to point of consumption. SCM is facing a variety of uncertainties, covering supplier inventory uncertainty caused by gradually exaggerated demand information deviation and delivery date uncertainty caused by the cumulative effect of time delay in logistics supply. However, the uncertainties and complexities are constantly rising and more complex models and more sophisticated novel methodologies are required to tackle them. This has driven the development of computation-intensive SCM methods based on stochastic simulation and optimization. The stochastic simulation and optimization can handle complex models with nonlinear and stochastic elements to get accurate and insightful results. It offers promising opportunities for the optimization of SCM, stochastic optimization algorithms, and efficient simulation techniques. A broad range of topics are discussed, especially in the following areas: optimization of single and multi-echelon inventory/capacity management, including application of the stochastic programming approach to inventory management, logistics distribution management, and customer demand with risk considerations; and sustainable supply chain, including recycling and reverse logistics, the stochastic method and algorithm in logistics, traffic and transportation, the timedependent routing problem in SCM, procurement management, the analytical performance model, the simulation and information model, Bayesian global optimization, discrete-event simulation, and Monte Carlo methods in SCM. In the paper entitled ‘‘Simulation of multi-echelon supply chain inventory transshipment models at different levels,’’ Yan and Liu established an inventory transshipment model using the system dynamics method. Then the authors analyzed the single-, double-, three-, and fourchain inventory transshipment models at different levels comparatively through simulation. The results of the model analysis indicated that the average stock level was minimally altered and declined as the number of chains in the inventory transshipment model increased from one to four. The average customer requirement satisfaction rate of the inventory transshipment system consistently increased as the number of chains added to the transshipment system increased, and the growth range of the average customer satisfaction rate continuously decreased with the increasing transshipment costs. The study of complex inventory transshipment of the supply chain could make a difference in the industrial cluster to achieve win–win and economies of scale. In the paper entitled ‘‘Optimization of a regional distribution center location for parts of end-of-life vehicles,’’ Sun et al. obtained a trade-off between the location cost of the distribution center and the transportation cost by a bilevel programming model. In the upper level, the sites of distribution centers were determined. The lower level represented the delivery routes under any location pattern of candidate sites of distribution centers. The shuffled complex evolution algorithm (SCE-UA) was applied for the upper and lower models of the distribution center location. A numerical test based on Liaoning Province in China was employed to validate the proposed model. Compared with the current scheme, the results demonstrated that the model can be an effective method to determinate a distribution center for end-of-life vehicles (ELVs), which could help improve the utilization of resources and lower the cost of the reverse logistics network of ELVs. In the paper entitled ‘‘A heuristic algorithm based on leaf photosynthate transport,’’ Yu et al. presented a bilevel programming model for transportation network design based on the non-deterministic polynomial-time of the transportation network design. The upper level layer pursued the minimum total travel time of users and the

Stochastic simulation and optimization in supply chain management