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Complex Logistics Simulation And Its Application Case Research

Posted on:2005-06-07Degree:DoctorType:Dissertation
Country:ChinaCandidate:W F ZhuFull Text:PDF
GTID:1116360152969053Subject:Systems Engineering
Abstract/Summary:PDF Full Text Request
An ideal logistics problem is considered as a network flow problem consisting of many geographically distributed entities. Many years' development experiences on information systems in materials dispatching domain and continuous considerations in complexity foment us thinking about the more general logistic systems. As a result, the concept of Complex Logistics (CL) is proposed. CL is a supply chain logistics consisting of several supplier entities, demand entities and so on. It is always in an uncertain environment, including customer demand, external supply, lead time and so on, influenced by many random parameters, having multi objectives, factors and levels. It is a complex mixed system which has both characteristics of continuous systems and those of discrete event systems. The complexities of CL comprise of structure complexity, uncertainty, information contortion and amplification, dynamic organization and so on, with distributional, autonomous, interactive and dynamic features.Inventory control is the main problem of CL. The goal of CL optimization is cost optimization and time optimization. Which inventory control policy should be adopted to deal with uncertainty in CL is an important and difficult problem to inventory managers. System simulation is a familiar theory and method for logistics study. Comparing with resolution method, it is an indirect system optimization method. Therefore, the research on how to construct a CL simulation tool having such functions as decision supporting, simulating, and training has very important theoretical significance and practical value. The emphasis of this paper is to build a complete methodology of CL modeling and simulation, including system depicting methods, simulation architecture, inventory models, simulation models construction and so on. Specifically, this work is outlined as follows: Using Colored Petri-Net, the Petri-Net model of agent's BDI (Belief, Desire and Intention) is built. Combining BDSPN (Batch Deterministic and Stochastic Petri Nets) an existing Petri-Net modeling method for supply chains, the Petri-Net models of inventory management policy and CL, called BDI-BDSPN, are constructed. An architecture for distributed multi-agent based simulation is proposed based on HLA/RTI(High Level Architecture/Run Time Infrastructure), where Colored Petri-Net is used to depict the run process and internal structural relationship of distributed simulation systems. Combining the general centralized simulation architecture, an integrated CL simulation architecture is proposed, where the dynamic connection and information interaction between centralized simulation and distributed simulation is discussed.CL fuzzy analytical model when customer demands are fuzzy is built using fuzzy sets and the relative computer solving arithmetic is designed. CL fuzzy analytical model is used to determine the optimal order-up-to levels of entities in CL. After summarizing uncertainty measurement methods, the concept of holding-shortage coefficient is proposed. Then how this holding-shortage coefficient affecting order-up-to level decision is also analyzed. CL simulation model is built and the relative computer implementation arithmetic is given too. The model is used to simulate CL behavior and performance in an uncertain environment. The simulation policy of CL simulation is based on Event Scheduling (ES) where time advances equally, i.e. simulation advances in equal increments. Entity Flow Diagram is used to erect CL simulation model based on CL Petri-Net model built before. After that, CLCSim, a centralized simulation tool for CL newly developed by us, is used to research on how customer demand uncertainty affecting CL behavior and performance. After that, how to simulate and adjust the order-up-to levels of entities in CL using CLCSim with experiments is also described.According to the methodology of CL modeling and simulation, an agile logistics system is constructed as a case. In addition, it illustrates the methodology is effective and applicable.The met...
Keywords/Search Tags:Complex Logistics, Colored Petri-Net, Simulation Architecture, Order-up-to level, Uncertainty, Simulation model
PDF Full Text Request
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