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

Economic scheduling needs to be considered for Grid computing environment, because it gives an incentive for resource providers to supply their resources. Moreover, it enforces efficient use of resources, because the users have to pay for their use. Tendering is a suitable model for Grid scheduling because users start the negotiations for finding suitable resources for executing their jobs. Furthermore, the users specify their job requirements with their requests and therefore the resources reply with bids that are based on the cost of taking on the job and the availability of their processors. In this thesis, a framework for economic Grid scheduling using tendering is proposed. The framework entities such as users, brokers and resources employ tender/contract-net model to negotiate the prices and deadlines. The brokers' role is acting on behalf of users. During the negotiations, the entities aim to maximise their performance which is measured by a number of metrics. In order to evaluate the entities' performance under different scenarios, a Java- based simulator, called MICOSim, supporting event-driven simulation of economic Grid scheduling is presented. MICOSim can perform a simulation of more than one hundred entities faster than real time. It is concluded from the evaluation that users who are interested in increasing the job success rate and paying less for executing their jobs have to consider received prices to select the most appropriate bids, while users who are interested in improving the job average satisfaction rate have to consider either received completion time or both price and completion time to select the most suitable bids when the submission of jobs is static. The best broker strategy is the one that doesn't take into account meeting the job deadlines in the bids it sends to job owners. Finally, the resource strategy that considers the price to determine if to reply to a request or not is superior to other resource strategies. The only exception is employing this strategy with price that is too low. However, there is a tiny difference between the performances of different user strategies in dynamic submission. It is also concluded from the evaluation that broker strategies have the best performance when the revenue they target from the users is reasonable. Thus, the broker's aim has to be receiving reasonable revenue (neither too low nor too high) from acting on behalf of users. It is observed from the results that the strategy performance is influenced by the behaviour of other entities such as the submission time of user jobs. Finally, it is observed that the characteristics of entities have an effect on the performance of strategies. For example, the two user strategies that consider the received completion time and both price and completion time to determine if to accept a broker bid have similar performance, because of the existence of resources with various prices from cheap to expensive and existence of resources which don't care about the price paid for the execution. So, the price threshold doesn't have a large effect on the performance.

This research investigates the problem of constrained sequencing of a set of jobs on a conveyor system with the objective of minimizing setup cost. A setup cost is associated with extra material, labor, or energy required due to the change of attributes in consecutive jobs at processing stations. A finite set of attributes is considered in this research. Sequencing is constrained by the availability of two elements ??orage buffers and conveyor junctions. The problem is motivated by the paint purge reduction problem at a major U.S. automotive manufacturer. First, a diverging junction with a sequence-independent setup cost and predefined attributes is modeled as an assignment problem and this model is extended by relaxing the initial assumptions in various ways. We also model the constrained sequencing problem with an off-line buffer and develop heuristics for efficiently getting a good quality solution by exploiting the special problem structure. Finally, we conduct sensitivity analysis using numerical experiments, explain the case study, and discuss the use of the simulation model as a supplementary tool for analyzing the constrained sequencing problem.

The systems development life cycle (SDLC) has been the traditional method of decision support systems design. However, in the last decade several methodologies have been introduced to address the limitations arising in the use of the traditional method. These approaches include Courban's iterative design, Keen's adaptive design, prototyping and a number of mixed methodologies incorporating prototyping into the SDLC. Each of the previously established design methodologies has a number of differing characteristics that make each of them a more suitable strategy for certain environments. However, in some environments the current methodologies present certain limitations or unnecessary expenditures. These limitations suggest the need for an alternative methodology. This dissertation develops a new methodology, priority design, to meet this need. To determine what methodology would be most effective in a given situation, an analysis of the operating environment must be performed. Such issues as project complexity, project uncertainty, and limited user involvement must be addressed. This dissertation develops a set of guidelines to assist in this analysis. For clarity, the guidelines are applied to three, well-documented case studies. As an application of the priority design methodology, a decision support system for nurse scheduling is developed. The development of a useful DSS for nurse scheduling requires that projected staff requirements and issues of both coverage and differential assignment of personnel be addressed. / Ph. D.

M. Tech. (Department of Industrial Engineering and Operations Management, Faculty of Engineering), Vaal University of Technology. / The current business environments in many companies are characterized by markets facing tough competitions, from which customer requirements and expectations are becoming increasingly high in terms of quality, cost and delivery dates, etc. These emerging expectations are even getting stronger due to rapid development of new information and communication technologies that provide direct connections between companies and their clients. As a result, companies should have powerful control mechanisms at their disposal. To achieve this, companies rely on a number of functions including production scheduling. This function has always been present within companies, but today, it is facing increasing complexities because of the large number of jobs that must be executed simultaneously. Amongst many factors, it is time driven. This study demonstrates that several disciplines can be married into one model (i.e. a unified model) to solve scheduling problems after disruptions, and clears the way for future multi-disciplinary research efforts. Scheduling problem is modeled as follows: Ito’s stochastic differential rule is used to analyse the time evolution of random or stochastic processes. Multifactor productivity is used to unify various disruption factors. Theory of line balancing is also employed to determine the required number of resources to minimize bottleneck. Reliability: disruptions are considered to be equivalent to system failure. The failure rate of the system is translated to the reliability of the system mathematically. The probabilities of failure are used as indicators of disruptions, and the theory of reliability is then applied. Bernoulli’s principle is also employed to relate pressure to production flow and aid in managing bottleneck situations. Results indicate that the amount of resources needed after disruption depends on the nature of disruption, and that the scheduler should plan to increase number of facilities following a trend that is only predicted by the nature of disruptions. It is also shown that disruption of one type may not greatly affect productivity of a certain company layout, whilst similar disruptions can have devastating effect on another type. It is further concluded that impacts of disruption are dependent on the type of company layouts.