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Robust optimization of supply chain network design in fuzzy decision system

JOURNAL OF INTELLIGENT MANUFACTURING 2016年第6期27卷 1131-1149页

作者： Bai, Xuejie Liu, Yankui Hebei Univ Coll Management Baoding 071002 Hebei Peoples R China Agr Univ Hebei Coll Sci Baoding 071001 Hebei Peoples R China

This paper presents a new robust optimization method for supply chain network design problem by employing variable possibility distributions. Due to the variability of market conditions and demands, there exist some impreciseness and ambiguousness in developing procurement and distribution plans. The proposed optimization method incorporates the uncertainties encountered in the manufacturing industry. The main motivation for building this optimization model is to make tools available for producers to develop robust supply chain network design. The modeling approach selected is a fuzzy value-at-risk (VaR) optimization model, in which the uncertain demands and transportation costs are characterized by variable possibility distributions. The variable possibility distributions are obtained by using the method of possibility critical value reduction to the secondary possibility distributions of uncertain demands and costs. We also discuss the equivalent parametric representation of credibility constraints and VaR objective function. Furthermore, we take the advantage of structural characteristics of the equivalent optimization model to design a parameter-based domain decomposition method. Using the proposed method, the original optimization problem is decomposed to two equivalent mixed-integer parametric programming sub-models so that we can solve the original optimization problem indirectly by solving its sub-models. Finally, we present an application example about a food processing company with four suppliers, five plants, five distribution centers and five customer zones. We formulate our application example as parametric optimization models and conduct our numerical experiments in the cases when the input data (demands and costs) are deterministic, have fixed possibility distributions and have variable possibility distributions. Experimental results show that our parametric optimization method can provide an effective and flexible way for decision makers to design a

关键词： Supply chain network design Value-at-risk Variable possibility distribution parameter decomposition method

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Parallel computing of diatomic molecular rarefied gas flows

引用

PARALLEL COMPUTING 1997年第9期23卷 1249-1260页

作者： Matsumoto, Y Tokumasu, T Department of Mechanical Engineering The University of Tokyo Hongo Bunkyo-ku Tokyo 113 Japan

A parallel algorithm for direct simulation Monte Carlo calculation of diatomic molecular rarefied gas flows is presented. For reliable simulation of such flow, an efficient molecular collision model is required. Using the molecular dynamics method, the collision of N-2 molecules is simulated. For this molecular dynamics simulation, the parameter decomposition method is applied for parallel computing. By using these results, the statistical collision model of diatomic molecule is constructed. For validation this model is applied to the direct simulation Monte Carlo method to simulate the energy distribution at equilibrium condition and the structure of normal shock wave. For this DSMC calculation, the domain decomposition is applied. It is shown that the collision process of diatomic molecules can be calculated precisely and the parallel algorithm can be efficiently implemented on the parallel computer. (C) 1997 Elsevier Science B.V.

关键词： rarefied gas flows molecular collision model direct simulation Monte Carlo method parameter decomposition method domain decomposition load balance distributed memory multiprocessors

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