Thesis (Ph. D.)--University of California, Berkeley, 1993. / Includes bibliographical references (leaves 248-265).

Thesis (Ph. D.)--State University of New York at Buffalo, 1993. / Includes bibliographical references (leaves 189-195).

Thesis (M.Eng.(Control Engineering)--University of Pretoria, 2003. / Abstract in English. Includes bibliographical references.

Thesis (M.S.)--Ohio University, November, 2003. / Title from PDF t.p. Includes bibliographical references (p. 112-115)

by Stanley B. Gershwin. / Bibliography: leaf 12. / "September, 1984." / U.S. Army Human Engineering Laboratory Contract no. DAAK-11-82-K-0018

In this paper, a sequence of implementation steps will be developed through the application of axiomatic design. This sequence will provide a design methodology for lean production which connects manufacturing system design objectives to operation design parameters. / Lean Aerospace Initiative

by Ramakrishna Akella, Yong Choong and Stanley B. Gershwin. / "February, 1984." / Bibliography: p. 29. / NASA Grant No. NAG1-2

Bibliography: leaves 172-184. An analytical approach to the integrated problems of designing the dynamic cellular manufacturing systems layout concurrently with its material flow (handling) requirements, in such a manner that minimises the material handling within the system. The proposed strategy encourages the design of a dynamic layout to identify simultaneously the machine groups, economical machine distribution, and intracell and intercell layouts.

Thesis (MSc)--Stellenbosch University, 2011. / ENGLISH ABSTRACT: As the world-wide consumption of electricity continually increases, more and more pressure is put on the capabilities of power generating systems to maintain their levels of power provision. The electricity utility companies operating these power systems are faced with numerous challenges with respect to ensuring reliable electricity supply at cost-e ective rates. One of these challenges concerns the planned preventative maintenance of a utility's power generating units. The generator maintenance scheduling (GMS) problem refers to the problem of nding a schedule for the planned maintenance outages of generating units in a power system (i.e. determining a list of dates corresponding to the times when every unit is to be shut down so as to undergo maintenance). This is typically a large combinatorial optimisation problem, subjected to a number of power system constraints, and is usually difficult to solve. A mixed-integer programming model is presented for the GMS problem, incorporating constraints on maintenance windows, the meeting of load demand together with a safety margin, the availability of maintenance crew and general exclusion constraints. The GMS problem is modelled by adopting a reliability optimality criterion, the goal of which is to level the reserve capacity. Three objective functions are presented which may achieve this reliability goal; these objective functions are respectively quadratic, nonlinear and linear in nature. Three GMS benchmark test systems (of which one is newly created) are modelled accordingly, but prove to be too time consuming to solve exactly by means of an o -the-shelf software package. Therefore, a metaheuristic solution approach (a simulated annealing (SA) algorithm) is used to solve the GMS problem approximately. A new ejection chain neighbourhood move operator in the context of GMS is introduced into the SA algorithm, along with a local search heuristic addition to the algorithm, which results in hybridisations of the SA algorithm. Extensive experiments are performed on di erent cooling schedules within the SA algorithm, on the classical and ejection chain neighbourhood move operators, and on the modi cations to the SA algorithm by the introduction of the local search heuristic. Conclusions are drawn with respect to the e ectiveness of each variation on the SA algorithm. The best solutions obtained during the experiments for each benchmark test case are reported. It is found that the SA algorithm, with ejection chain neighbourhood move operator and a local search heuristic hybridisation, achieves very good solutions to all instances of the GMS problem. The hybridised simulated annealing algorithm is implemented in a computerised decision support system (DSS), which is capable of solving any GMS problem instance conforming to the general formulation described above. The DSS is found to determine good maintenance schedules when utilised to solve a realistic case study within the context of the South African power system. A best schedule attaining an objective function value within 6% of a theoretical lowerbound, is thus produced. / AFRIKAANSE OPSOMMING: Met die wêreldwye elektrisiteitsverbruik wat voortdurend aan die toeneem is, word daar al hoe meer druk geplaas op die vermoë van kragstelsels om aan kragvoorsieningsaanvraag te voldoen. Nutsmaatskappye wat elektrisiteit opwek, word deur talle uitdagings met betrekking tot betroubare elektrisiteitsverskaffing teen koste-e ektiewe tariewe in die gesig gestaar. Een van hierdie uitdagings het te make met die beplande, voorkomende instandhouding van 'n nutsmaatskappy se kragopwekkingseenhede. Die generator-instandhoudingskeduleringsprobleem (GISP) verwys na die probleem waarin 'n skedule vir die beplande instandhouding van kragopwekkingseenhede binne 'n kragstelsel gevind moet word ('n lys van datums moet tipies gevind word wat ooreenstem met die tye wanneer elke kragopwekkingseenheid afgeskakel moet word om instandhoudingswerk te ondergaan). Hierdie probleem is tipies 'n groot kombinatoriese optimeringsprobleem, onderworpe aan 'n aantal beperkings van die kragstelsel, en is gewoonlik moeilik om op te los. 'n Gemengde, heeltallige programmeringsmodel vir die GISP word geformuleer. Die beperkings waaruit die formulering bestaan, sluit in: venstertydperke vir instandhouding, bevrediging van die vraag na elektrisiteit tesame met 'n veiligheidsgrens, die beskikbaarheid van instandhoudingspersoneel en algemene uitsluitingsbeperkings. Die GISP-model neem as optimaliteitskriterium betroubaarheid en het ten doel om die reserwekrag wat gedurende elke tydperk beskikbaar is, gelyk te maak. Drie doelfunksies word gebruik om laasgenoemde doel te bereik (naamlik doelfunksies wat onderskeidelik kwadraties, nie-lineêr en lineêr van aard is). Drie GISP-maatstaftoetsstelsels (waarvan een nuut geskep is) is dienooreenkomstig gemodelleer, maar dit blyk uit die oplossingstye dat daar onprakties lank gewag sal moet word om eksakte oplossings deur middel van kommersiële programmatuur vir hierdie stelsels te kry. Gevolglik word 'n metaheuristiese oplossingsbenadering ('n gesimuleerde temperingsalgoritme (GTA)) gevolg om die GISP benaderd op te los. 'n Nuwe uitwerpingsketting-skuifoperator word in die konteks van GISP in die GTA gebruik. Verder word 'n lokale soekheuristiek met die GTA vermeng om 'n basteralgoritme te vorm. Uitgebreide eksperimente word uitgevoer op verskeie afkoelskedules binne die GTA, op die klassieke en uitwerpingsketting-skuifoperators en op die verbasterings van die GTA meegebring deur die lokale soekheuristiek. Gevolgtrekkings word oor elke variasie van die GTA se e ektiwiteit gemaak. Die beste oplossings vir elke toetsstelsel wat gedurende die eksperimente verkry is, word gerapporteer. Daar word bevind dat die GTA met uitwerpingsketting-skuifoperator en lokale soekheuristiek-verbastering baie goeie oplossings vir die GISP lewer. Die verbasterde GTA word in 'n gerekenariseerde besluitsteunstelsel (BSS) geïmplementeer wat 'n gebruiker in staat stel om enige GISP van die vorm soos in die wiskundige programmeringsmodel hierbo beskryf, op te los. Daar word bevind dat die BSS goeie skedules lewer wanneer dit gebruik word om 'n realistiese gevallestudie binne die konteks van die Suid-Afrikaanse kragstelsel, op te los. 'n Beste skedule met 'n doelfunksiewaarde wat binne 6% vanaf 'n teoretiese ondergrens is, word ondermeer bepaal.

For many years 2D machine vision has been used to perform automated inspection and measuring in the manufacturing environment. A strong drive to automate manufacturing has meant improvements in robotics and sensor technologies. So has machine vision seen a steady movement away from 2D and towards 3D. It is necessary to research and develop software that can use these new 3D sensing equipment in novel and useful ways. One task that is particularly useful, for a variety of situations is object recognition. It was hypothesised that it should be possible to train artificial neural networks to recognise 3D objects. For this purpose a 3D laser scanner was developed. This scanner and its software was developed and tested first in a virtual environment and what was learned there was then used to implemented an actual scanner. This scanner served the purpose of verifying what was done in the virtual environment. Neural networks of different sized were trained to establish whether they are a feasible classifier for the task of object recognition. Testing showed that, with the correct preprocessing, it is possible to perform 3D object recognition on simple geometric shapes by means of artificial neural networks.