Thesis (Ph.D.)--University of Tulsa, 1971. / Bibliography: leaves 227-238.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1991. / Vita. Abstract. Includes bibliographical references (leaves 88-90). Also available via the Internet.

Computer simulation is one of the most widely used techniques in manufacturing systems study. The value of simulation increases constantly due to improvements in computing power. However models of large-scale systems tend to be very complex, and writing simulation programs to execute them can be an arduous task. Rapid modeling of such systems can play a significant role in the selecting manufacturing strategy. This dissertation deals with the design and implementation of tools that aid in such modeling activity by identifying some of the problems that occur frequently in the modeling of flexible manufacturing systems (FMS). This set of tools, collectively called the hierarchical modular modeling environment (HMME), is designed and implement by extending DEVS(Discrete EVent System Specification)-Scheme. The problems identified are in the field of model interconnections, embedding expert systems in models, model structuring and simulation display. An example, of operation overlapping strategy in a hierarchical FMS, demonstrates the utility of the environment. Although developed for FMS simulation, this modeling aid is applicable to many other domains of knowledge-based systems and intelligent control.

The group decision making literature defines three methods of decision making used by and within groups: consensus, negotiation, and voting. Group support systems (GSS) research, with the creation of its enabling technologies, has focused on issues that support consensus-building and negotiation, thus relegating voting to the relatively limited role of prioritization. Surprisingly, the technology of aggregating opinions in GSS has received little research attention, and the procedures for it that have been implemented in even the most sophisticated groupware systems are relatively unsophisticated. Influence allocation processes (IAP) are voting and opinion aggregating methods that allow members to distribute some or all of their decision making influence to others in the group in order to exploit not only the group's knowledge of the alternatives, but its knowledge of itself. Only with the common use of GSS has their use become practical. Two families of influence allocation processes are reconsidered and expanded in this dissertation: SPAN, introduced by MacKinnon (1966a) and a technique based on a proposal by Morris DeGroot (1974) called Rational Consensus by Lehrer and Wagner (1981), who developed it and explicated it as a normative standard for combining opinion. This dissertation first considers the development and implementation of a set of IAP voting tools within GroupSystemsᵀᴹ. Second, a series of empirical studies is used to show how decision groups use voting tools for the selection of a correct solution. Third, simulation studies are used to develop guidelines for the allocation of influence in an alternative forced-choice problem. Finally, a case study of a real group using IAP for an authentic decisional problem is described. Influence allocation processes help reconcile differences in opinion, knowledge, and judgment. As such, they show much potential for improving the efficiency and effectiveness of group decision-making.

Kong Wai = 歐姆阻尼系統與頻散耗散系統之連繫的研究 / 江偉. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 72-73). / Text in English; abstracts in English and Chinese. / Kong Wai = Ou mu zu ni xi tong yu pin san hao san xi tong zhi lian xi de yan jiu / Jiang Wei. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Review of ohmic systems and dispersive systems --- p.4 / Chapter 2.1 --- Damped ohmic systems --- p.4 / Chapter 2.1.1 --- Equations of motion --- p.4 / Chapter 2.1.2 --- Normal modes --- p.7 / Chapter 2.1.3 --- Bilinear mapping and general solutions --- p.8 / Chapter 2.2 --- Dissipative dispersive system --- p.10 / Chapter 2.2.1 --- Matrix representation --- p.13 / Chapter 2.2.2 --- Bilinear mapping and metric tensor --- p.14 / Chapter 2.2.3 --- Generalization to M relaxation frequencies --- p.16 / Chapter 3 --- Relation between dispersive and ohmic systems --- p.17 / Chapter 4 --- Odd dimension problem --- p.22 / Chapter 4.1 --- The ohmic system --- p.22 / Chapter 4.1.1 --- Fast mode --- p.22 / Chapter 4.1.2 --- e = 0 --- p.24 / Chapter 4.1.3 --- e→ 0 --- p.25 / Chapter 4.2 --- The dispersive system --- p.27 / Chapter 4.3 --- Connections in odd-dimensional case --- p.30 / Chapter 4.3.1 --- "Odd-dimensional cases, e = 0, ₁ت2= ´ؤi∞" --- p.30 / Chapter 4.3.2 --- "Limiting cases, e →0" --- p.31 / Chapter 4.4 --- Eigenvalues --- p.32 / Chapter 4.5 --- Conclusion --- p.34 / Chapter 5 --- Fluctuation-dissipation theorem --- p.35 / Chapter 5.1 --- FDT for a single damped oscillator --- p.35 / Chapter 5.2 --- FDT for two coupled ohmic oscillators --- p.38 / Chapter 5.3 --- Two couple damped oscillators in different baths --- p.40 / Chapter 5.3.1 --- Case I: Symmetric and T1 = T2 --- p.41 / Chapter 5.3.2 --- Case II: Symmetric and η2 = 0 --- p.42 / Chapter 5.3.3 --- Case III: Asymmetric and T1 = T2 --- p.44 / Chapter 5.3.4 --- Case IV: Asymmetric and η2 = 0 --- p.46 / Chapter 5.3.5 --- Discussion --- p.47 / Chapter 6 --- Pseudo-Boltzman distribution --- p.48 / Chapter 6.1 --- Fokker´ؤPlanck equation --- p.48 / Chapter 6.1.1 --- Single damped oscillator --- p.48 / Chapter 6.1.2 --- Two coupled damped oscillators --- p.50 / Chapter 6.2 --- Path integral method --- p.55 / Chapter 6.2.1 --- Single damped oscillator --- p.55 / Chapter 6.2.2 --- N coupled oscillators --- p.56 / Chapter 7 --- Energy stored in a dispersive system --- p.58 / Chapter 7.1 --- Correlations --- p.59 / Chapter 7.2 --- One-one mapping for N = 2 --- p.61 / Chapter 7.3 --- One-one mapping for N = 3 --- p.64 / Chapter 8 --- Conclusion --- p.70 / Bibliography --- p.72 / Chapter A --- Equipartition theorem --- p.74 / Chapter B --- General fluctuation-dissipation theorem --- p.76 / Chapter C --- Case I: Symmetric and T1 = T2 --- p.80 / Chapter D --- Fokker´ؤPlanck equation - Single damped oscillator --- p.82 / Chapter E --- Fokker-Planck equation - Two coupled damped oscillators --- p.86 / Chapter F --- Path integral method - Single damped oscillator --- p.88 / Chapter G --- Path integral method - N coupled oscillators --- p.90 / Chapter H --- Correlation of χ1χ1 --- p.94 / Chapter I --- Conditions for a dissipative dispersive system --- p.96 / Chapter J --- Solution of an ohmic system --- p.98

Chan Hoi-Yeung = 網絡與競爭系統的物理 : 個體為本模型中的網絡效應 / 陳凱揚. / Thesis submitted in: September 2005. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 191-197). / Text in English; abstracts in English and Chinese. / Chan Hoi-Yeung = Wang luo yu jing zheng xi tong de wu li : ge ti wei ben mo xing zhong de wang luo xiao ying / Chen Kaiyang. / Abstract --- p.i / Acknowledgments --- p.v / Contents --- p.vii / Chapter 1 --- Overview --- p.1 / Chapter I --- Networks --- p.3 / Chapter 2 --- Networks in nature --- p.4 / Chapter 2.1 --- Introduction --- p.4 / Chapter 2.2 --- Terminology of the networks studies --- p.6 / Chapter 2.2.1 --- Nodes --- p.6 / Chapter 2.2.2 --- Links --- p.6 / Chapter 2.2.3 --- Adjacency matrix --- p.9 / Chapter 2.2.4 --- Connectivity --- p.10 / Chapter 2.2.5 --- Clustering coefficient --- p.11 / Chapter 2.2.6 --- Shortest path --- p.11 / Chapter 2.2.7 --- Connectivity correlation --- p.12 / Chapter 2.3 --- Topology in the real-world networks --- p.13 / Chapter 2.3.1 --- The Internet --- p.13 / Chapter 2.3.2 --- The WWW --- p.15 / Chapter 2.3.3 --- Collaboration networks --- p.15 / Chapter 2.3.4 --- Food webs --- p.16 / Chapter 2.3.5 --- Power grids --- p.17 / Chapter 2.4 --- Discussion --- p.17 / Chapter 3 --- Review on Network Models --- p.19 / Chapter 3.1 --- Introduction --- p.19 / Chapter 3.2 --- Graph Theory --- p.20 / Chapter 3.2.1 --- Classical random graph --- p.20 / Chapter 3.3 --- Evolving networks --- p.23 / Chapter 3.3.1 --- Random growing network --- p.23 / Chapter 3.3.2 --- Fitness growing network --- p.25 / Chapter 3.3.3 --- Barabasi-Albert model --- p.27 / Chapter 3.3.4 --- Fitness model --- p.31 / Chapter 3.4 --- Lattice --- p.33 / Chapter 3.4.1 --- Regular hypercubic lattices (Periodic) --- p.33 / Chapter 3.4.2 --- Regular hypercubic lattices (Free boundary conditions) . --- p.35 / Chapter 3.5 --- Discussion --- p.35 / Chapter 4 --- Network Properties --- p.38 / Chapter 4.1 --- More derivations on existing models --- p.38 / Chapter 4.1.1 --- Classical random graphs --- p.38 / Chapter 4.1.2 --- Barabasi-Albert model --- p.40 / Chapter 4.1.3 --- Fitness Model --- p.42 / Chapter 4.1.4 --- Regular hypercubic lattices (Periodic) --- p.45 / Chapter 4.2 --- New model --- p.48 / Chapter 4.2.1 --- Fitness-BA hybrid model --- p.48 / Chapter 4.3 --- Link removal --- p.55 / Chapter 4.3.1 --- Introduction --- p.55 / Chapter 4.3.2 --- Formalism in connectivity --- p.55 / Chapter 4.3.3 --- Pruned BA Model --- p.56 / Chapter 4.4 --- Link addition --- p.58 / Chapter 4.4.1 --- Introduction --- p.58 / Chapter 4.4.2 --- Regular hypercubic lattices (Periodic) --- p.58 / Chapter 4.5 --- Discussion --- p.60 / Chapter II --- Games --- p.62 / Chapter 5 --- Review on Agent-based models of competing population --- p.63 / Chapter 5.1 --- Introduction --- p.63 / Chapter 5.2 --- The El Farol Bar attendance problem --- p.65 / Chapter 5.2.1 --- Model --- p.65 / Chapter 5.2.2 --- Strategies --- p.66 / Chapter 5.2.3 --- Discussion --- p.66 / Chapter 5.3 --- Minority game --- p.67 / Chapter 5.3.1 --- Model --- p.67 / Chapter 5.3.2 --- Strategies --- p.68 / Chapter 5.3.3 --- Attendance --- p.69 / Chapter 5.3.4 --- History and quasi-Eulerian state --- p.69 / Chapter 5.3.5 --- Success rate and Hamming distance --- p.71 / Chapter 5.3.6 --- Volatility --- p.73 / Chapter 5.3.7 --- Crowd-anticrowd theory --- p.75 / Chapter 5.3.8 --- Discussion --- p.76 / Chapter 6 --- B-A-R model : Dynamics --- p.78 / Chapter 6.1 --- Model --- p.78 / Chapter 6.2 --- Results: Plateaux and periodicity --- p.81 / Chapter 6.3 --- A microscopic view: Agents' decisions and strategy performance --- p.86 / Chapter 6.4 --- A macroscopic view: Bit-string patterns --- p.92 / Chapter 6.4.1 --- The history space --- p.92 / Chapter 6.4.2 --- Bit-string statistics of different states --- p.94 / Chapter 6.5 --- The (max = 1 states --- p.97 / Chapter 6.5.1 --- Values of wm3iX --- p.97 / Chapter 6.5.2 --- "Strategy ranking evolvement: ni, (w)" --- p.101 / Chapter 6.5.3 --- Substates . --- p.105 / Chapter 7 --- B-A-R model : Formalism --- p.108 / Chapter 7.1 --- Resource level at transitions of Cmax = 0 state --- p.108 / Chapter 7.2 --- Resource levels at transitions of Cmax 二 1 states --- p.109 / Chapter 7.2.1 --- Method --- p.109 / Chapter 7.2.2 --- Lmin for upper substate --- p.110 / Chapter 7.2.3 --- Lmin for lower substate --- p.113 / Chapter 7.3 --- Discussion --- p.116 / Chapter 8 --- B-A-R model : Statistics --- p.121 / Chapter 8.1 --- Problem --- p.121 / Chapter 8.2 --- Bit-string statistics --- p.122 / Chapter 8.2.1 --- Allowed transitions --- p.122 / Chapter 8.2.2 --- Grouping the history space --- p.122 / Chapter 8.2.3 --- "Grouping the states, Cmax" --- p.127 / Chapter 8.2.4 --- "Labelling each state, /(C)" --- p.129 / Chapter 8.3 --- Discussion --- p.130 / Chapter III --- Networked games --- p.131 / Chapter 9 --- Networked minority game --- p.132 / Chapter 9.1 --- Model --- p.132 / Chapter 9.2 --- Preliminary results: Agents' success rates --- p.133 / Chapter 9.3 --- Ranking the strategies --- p.135 / Chapter 9.3.1 --- Ranking pattern --- p.136 / Chapter 9.3.2 --- Fraction of strategies in each rank --- p.140 / Chapter 9.4 --- Number of agents using a best strategy belonging to rank r --- p.141 / Chapter 9.4.1 --- Unconnected population --- p.141 / Chapter 9.4.2 --- Networked population . --- p.142 / Chapter 9.5 --- Application: Mean success rate --- p.143 / Chapter 9.6 --- Mean success rate of agents with degree k --- p.147 / Chapter 9.7 --- Application in other networks --- p.149 / Chapter 9.8 --- Discussion --- p.151 / Chapter 10 --- Interacting agents: Networked B-A-R model --- p.154 / Chapter 10.1 --- Model --- p.154 / Chapter 10.2 --- The quasi-Eulerian state (wmax = 1/2 state) --- p.155 / Chapter 10.3 --- The emergent states --- p.159 / Chapter 10.3.1 --- General results --- p.159 / Chapter 10.3.2 --- The Cmax = 0 state --- p.160 / Chapter 10.3.3 --- The Cmax = 1 state --- p.161 / Chapter 10.4 --- Discussion --- p.162 / Chapter IV --- Conclusion --- p.164 / Chapter 11 --- Conclusion --- p.165 / Chapter V --- Appendices --- p.172 / Chapter A --- List of symbols --- p.173 / Chapter A.1 --- Networks --- p.173 / Chapter A.2 --- Games --- p.174 / Chapter A.3 --- Networked games --- p.176 / Chapter B --- Distance distribution in classical random graphs --- p.177 / Chapter B.1 --- Method --- p.177 / Chapter B.2 --- Distance distribution --- p.177 / Chapter B.3 --- Behaviour at small L --- p.178 / Chapter B.4 --- Behaviour at large L --- p.179 / Chapter C --- Co-ordination number in infinite hypercubic lattice --- p.181 / Chapter C.1 --- Method --- p.181 / Chapter C.1.1 --- ID lattice --- p.181 / Chapter C.1.2 --- 2D square lattice --- p.182 / Chapter C.1.3 --- Higher dimension hypercubic lattices --- p.183 / Chapter C.2 --- Coefficients --- p.185 / Chapter D --- Connectivity distribution in fitness-BA hybrid model --- p.187 / Chapter D.1 --- Mean field approach --- p.187 / Chapter D.2 --- Connectivity distribution --- p.188 / Chapter D.3 --- Power-law exponent --- p.190 / Bibliography --- p.191

Thesis (Ph.D.) -- University of Canberra, 2006. / Includes bibliography (p. 250 - 301) Also available online.

Thesis (D. Min.)--Gordon-Conwell Theological Seminary, 2001. / Abstract and vita. Includes bibliographical references (leaves 123-128).

No description available.

Thesis (Ph.D.)--Baylor University, 2007. / Includes bibliographical references (p. 128-130).