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No description available.

No description available.

No description available.

No description available.

Production scheduling is a notoriously difficult problem. Manufacturing environments contain complex, time-critical processes, which create highly constrained scheduling problems. Genetic algorithms (GAs) are optimisation tools based on the principles of evolution. They can tackle problems that are mathematically complex, or even impossible to solve by traditional methods. They allow problem-specific implementation, so that the user can develop a technique that suits the situation, whilst still providing satisfactory schedule optimisation performance. This work tests GA optimisation on a real-life scheduling application, a chilled ready-meal factory. A schedule optimisation system is required to adapt to changing problem circumstances and to include uncertain or incomplete information. A GA was designed to allow successive improvements to its effectiveness at scheduling. Three objectives were chosen for minimisation. The GA proved capable of finding a solution that attempted to minimise the sum of the three costs. The GA performance was improved after experiments showed the effects of rules and preference modelling upon the optimisation process, allowing 'uncertain' data to be included. Multi-objective GAs (MOGAs) minimise each cost as a separate objective, rather than as part of a single-objective sum. Combining Pareto-optimality with varying emphasis on the conflicting objectives, a set of possible solutions can be found from one run of MOGA. Each MOGA solution represents a different situation within the factory, thus being well suited to a constantly changing manufacturing problem. Three MOGA implementations are applied to the problem; a standard weighted sum, two versions of a Pareto-optimal method and a parallel populations method. Techniques are developed to allow suitable comparison of MOGAs. Performance comparisons indicate which method is most effective for meeting the factory's requirements. Graphical and statistical methods indicate that the Pareto-based MOGA is most effective for this problem. The MOGA is demonstrated as being a highly applicable technique for production schedule optimisation.

Border Leicester x Scottish Blackface ewes were used in a series of experiments to investigate factors affecting the number of transferable ova produced in response to superovulation with follicle stimulating hormone or pregnant mare serum gonadotrophin. The effect of the method of oestrous synchronisation on ova production was studied and it was suggested that during the progesterone priming period the application of progestagens via intravaginal pessaries resulted in a higher ovulation rate than pure progesterone. There was also evidence that ewes primed with 40 as opposed to 30mg fluorogestone acetate produced ova of higher viability after transfer to recipient ewes. However, a sustained increase in plasma concentrations of progesterone, within physiological limits, did not improve ovulation rate or the development of 5-day old ova in an in vitro culture system. Failure to maintain adequate progesterone concentrations throughout the entire priming period leading to mistiming of superovulatory treatment in relation to follicular development was identified as a possible cause of reduced ovulation rates. Laparoscopic technique for intrauterine insemination and ovum recovery were used successfully in experimental and commercial pedigree ewes. Insemination close to the expected time of ovulation resulted in high fertilisation rates of ova. Delaying the insemination until after ovulation was expected to be complete improved the ovum recovery rate but resulted in an increased number of retarded and unfertilised ova. It was hypothesised that this was the result of oocyte ageing prior to fertilisation. Attempts to investigate the development of early stage (two-day old) ova in in vitro co-culture with oviductal cells demonstrated the unreliability of morphology as a guide to ovine ovum viability and the need for additional tests such as nuclear staining. Through the thesis the implications of the findings for application in commercial ovum transfer schemes are discussed.

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Approved for public release; distribution is unlimited / The analysis of a break in production is usually performed by a government negotiator or cost analyst. The more effectively they are able to estimate the loss of learning due to breaks in production, the more likely that the final contract will be fair and reasonable. The research of this study focused on identifying the factors which contribute to a loss of learning due to a break in production and the methods which are available to quantify these factors. The four methods identified were the George Anderlohr, the DCAA, the Pinchon and Richardson, and the Cubic Curve. These methods were then analyzed using the data from two aircraft, the Grumman C-2A and the Bell Helicopter Textron AH-1W, both of which experienced breaks in production. This study concludes that the George Anderlohr approach is the most effective method to evaluate the loss of learning due to a break in production. / http://archive.org/details/measuringlosseso00ever / Captain, United States Marine Corps