DESIGN SAMPLING AND REPLICATION ASSIGNMENT UNDER FIXED COMPUTING BUDGET

For many real world problems, when the design space is huge and unstructured, and time consuming simulation is needed to estimate the performance measure, it is important to decide how many designs to sample and how long to run for each design alternative given that we have only a fixed amount of computing time. In this paper, we present a simulation study on how the distribution of the performance measures and distribution of the estimation errors/noises will affect the decision. From the analysis, it is observed that when the underlying distribution of the noise is bounded and if there is a high chance that we can get the smallest noise, then the decision will be to sample as many as possible, but if the noise is unbounded, then it will be important to reduce the noise level first by assigning more replications for each design. On the other hand, if the distribution of the performance measure indicates that we will have a high chance of getting good designs, the suggestion is also to reduce the noise level, otherwise, we need to sample more designs so as to increase the chances of getting good designs. For the special case when the distributions of both the performance measures and noise are normal, we are able to estimate the number of designs to sample, and the number of replications to run in order to obtain the best performance.

Connecting the Belt and Road through sea-rail collaboration

As part of China's "the Belt and Road"strategy, China Railway Express provides alternative shipping routes and transportation modes from Asia to Europe and creates new opportunities for intermodal transportation in the shipping industry. A time–distancebased cost(time cost) function was proposed to compare China Railway Express with traditional transportation modes. Time cost was related to different types of cargoes,which exhibit distinct sensitivity to time. Using the proposed cost function as basis, we identified the cost indifference area where total costs are equal. Further analysis was performed for selecting the transportation mode and supply area for a specific cargo. This study provides various parties, such as business owners, the government, and the shipping industry, with many valuable insights.

A case study on sample average approximation method for stochastic supply chain network design problem

This study aims to solve a typical long-term strategic decision problem on supply chain network design with consideration to uncertain demands. Existing methods for these problems are either deterministic or limited in scale. We analyze the impact of uncertainty on demand based on actual large data from industrial companies.Deterministic equivalent model with nonanticipativity constraints, branch-and-fix coordination, sample average approximation(SAA) with Bayesian bootstrap, and Latin hypercube sampling were adopted to analyze stochastic demands. A computational study of supply chain network with front-ends in Europe and back-ends in Asia is presented to highlight the importance of stochastic factors in these problems and the efficiency of our proposed solution approach.