Includes bibliographical references (p. 62).

Thesis (M.S.)--West Virginia University, 2002. / Title from document title page. Document formatted into pages; contains viii, 112 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 110-112).

Thesis (Ph.D.)--University of Texas at Arlington, 2009.

Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Mavris, Dimitri; Committee Member: Costello, Mark; Committee Member: German, Brian. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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In an ideal Just-in-Time (JIT) production environment, parts should be delivered to the workstationsat the exact time they are needed and in the exact quantity required. In reality, formost components/subassemblies this is neither practical nor economical. In this study, thematerial flow of the crossdocking operation at the Toyota Motor Manufacturing plant inGeorgetown, KY (TMMK) is simulated and analyzed.At the Georgetown plant between 80 and 120 trucks are unloaded every day, with approximately1300 different parts being handled in the crossdocking area. The crossdocking areaconsists of 12 lanes, each lane corresponding to one section of the assembly line. Whereassome pallets contain parts designated for only one lane, other parts are delivered in such smallquantities that they arrive as mixed pallets. These pallets have to be sorted/crossdocked intothe proper lanes before they can be delivered to the workstations at the assembly line. Thisprocedure is both time consuming and costly.In this study, the present layout of the crossdocking area at Toyota and a layout proposed byToyota are compared via simulation with three newly designed layouts. The simulation modelswill test the influence of two different volumes of incoming quantities, the actual volumeas it is now and one of 50% reduced volume. The models will also examine the effects ofcrossdocking on the performance of the system, simulating three different percentage levelsof pallets that have to be crossdocked.The objectives of the initial study are twofold. First, simulations of the current system,based on data provided by Toyota, will give insight into the dynamic behavior and the materialflow of the existing arrangement. These simulations will simultaneously serve to validateour modeling techniques. The second objective is to reduce the travel distances in the crossdockingarea; this will reduce the workload of the team members and decrease the lead timefrom unloading of the truck to delivery to the assembly line. In the second phase of theproject, the design will be further optimized. Starting with the best layouts from the simulationresults, the lanes will be rearranged using a genetic algorithm to allow the lanes withthe most crossdocking traffic to be closest together.The different crossdocking quantities and percentages of crossdocking pallets in the simulationsallow a generalization of the study and the development of guidelines for layouts ofother types of crossdocking operations. The simulation and optimization can be used as abasis for further studies of material flow in JIT and/or crossdocking environments.

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Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”. Typically these algorithms maintain a population of individual solutions, each of which has a fitness attached to it, which in some way reflects the quality of the solution. The search proceeds via the iterative generation, evaluation and possible incorporation of new individuals based on the current population, using a number of parameterised genetic operators. In this thesis the phenomenon of Self Adaptation of the genetic operators is investigated. A new framework for classifying adaptive algorithms is proposed, based on the scope of the adaptation, and on the nature of the transition function guiding the search through the space of possible configurations of the algorithm. Mechanisms are investigated for achieving the self adaptation of recombination and mutation operators within a genetic algorithm, and means of combining them are investigated. These are shown to produce significantly better results than any of the combinations of fixed operators tested, across a range of problem types. These new operators reduce the need for the designer of an algorithm to select appropriate choices of operators and parameters, thus aiding the implementation of genetic algorithms. The nature of the evolving search strategies are investigated and explained in terms of the known properties of the landscapes used, and it is suggested how observations of evolving strategies on unknown landscapes may be used to categorise them, and guide further changes in other facets of the genetic algorithm. This work provides a contribution towards the study of adaptation in Evolutionary Algorithms, and towards the design of robust search algorithms for “real world” problems.

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