Search

The logistics of spare parts shows the characteristics of dynamics. We establish a support system based on demand-pull model. This paper analyses the feedback control mechanism of the system and the mathematical principles of the dynamics of the supply–demand model of spare parts based on system dynamics. Then we build a system state model, analyzing the dynamic actions of the support system. Our study shows that: (1) Excessive logistics delay may affect the stability of the system; (2) The stability and performance of the support system of spare parts based on the ˵demand-pull″ model are closely related to the supply chain management system and technical conditions, also depend on the interactions of multiple factors. Then we get the value threshold of the key parameters to make the system stable, and test the impacts of logistics delay on the stability of the system. Finally, through system dynamics simulation, we optimize the system. [ABSTRACT FROM AUTHOR]

In classic optimization theory, the concept of stability refers to the study of how much and in which way the optimal solutions of a given minimization problem π can vary as a function of small perturbations of the input data. Motivated by congestion problems arising in shortest-path based communication networks, in this paper we restrict ourselves to the case in which π is actually a network design problem on a given graph G = (V,E,w) of |V| = n nodes, |E| = m edges, and with a positive real weight w(e) on each edge e ϵ E. We focus on a subclass of perturbations, that we call stretching perturbations, in which the weights of the edges of G can be increased by at most a fixed multiplicative real factor λ ≥ 1. For this class of perturbations, we address the problem of computing the stability number of any given subgraph H of G containing at least an optimal solution of π, namely the maximum stretching factor for which H keeps on maintaining an optimal solution. Furthermore, given a stretching factor λ, we study the problem of constructing a minimal subgraph of G with stability number greater or equal to λ. We develop a general technique to solve both problems. By applying this technique to the minimum spanning tree and the single-source shortest paths tree (SPT) problems, we obtain ]> and ]> time algorithms, respectively, where α(·,·) is the functional inverse of Ackermann΄s function. Furthermore, for the SPT problem, we show that if H coincides with the set of all optimal solutions, then the time complexity can be reduced to ]> . Finally, for the single-source single-destination shortest path problem, if the optimal solutions of the input instance happen to form a set of vertex-disjoint paths, and H coincides with this set, then we show that we can compute the stability number in ]> time. [ABSTRACT FROM AUTHOR]