In order to cope with the challenges in industry today, such as changes in product diversity and production volume, manufacturing companies are forced to react more flexibly and swiftly. Furthermore, in order for them to survive in an ever-changing market, they also need to be highly competitive by achieving near optimal efficiency in their operations. Production scheduling is vital to the success of manufacturing systems in industry today, because the near optimal allocation of resources is essential in remaining highly competitive. The overall aim of this study is the advancement of research in manufacturing scheduling through the exploration of more effective approaches to address complex, real-world manufacturing flow shop problems. The methodology used in the thesis is in essence a combination of systems engineering, algorithmic design and empirical experiments using real-world scenarios and data. Particularly, it proposes a new, web services-based, industrial scheduling system framework, called OPTIMISE Scheduling System (OSS), for solving real-world complex scheduling problems. OSS, as implemented on top of a generic web services-based simulation-based optimisation (SBO) platform called OPTIMISE, can support near optimal and real-time production scheduling in a distributed and parallel computing environment. Discrete-event simulation (DES) is used to represent and flexibly cope with complex scheduling problems without making unrealistic assumptions which are the major limitations of existing scheduling methods proposed in the literature. At the same time, the research has gone beyond existing studies of simulation-based scheduling applications, because the OSS has been implemented in a real-world industrial environment at an automotive manufacturer, so that qualitative evaluations and quantitative comparisons of scheduling methods and algorithms can be made with the same framework. Furthermore, in order to be able to adapt to and handle many different types of real-world scheduling problems, a new hybrid meta-heuristic scheduling algorithm that combines priority dispatching rules and genetic encoding is proposed. This combination is demonstrated to be able to handle a wider range of problems or a current scheduling problem that may change over time, due to the flexibility requirements in the real-world. The novel hybrid genetic representation has been demonstrated effective through the evaluation in the real-world scheduling problem using real-world data.

It has been broadly recognised that today’s manufacturing organisations face increasing pressures from continuous and unexpected changes in the business environment such as changes in product types, changes in demand pattern, changes in manufacturing technologies etc. To enable manufacturing organisations to rapidly and timely deal with these changes, operational decisions (e.g., process planning and production scheduling) have to be integrated with dynamic system restructure or reconfiguration so that manufacturing organisations do not only use the flexible resource utilisations to deal with these changes, but also can dynamically reconfigure their existing system structures in response these changes. A manufacturing system concept and implementation methodology is proposed by the Exeter Manufacturing Enterprise Centre (XMEC), which is called the Dynamically Integrated Manufacturing System (DIMS). The overall aim of DIMS is to provide a systematic modelling and control framework in which operational decisions can be integrated with the dynamic system restructuring decisions so as to help manufacturing systems to dynamically deal with changes in the business environment. This PhD research is a part of DIMS research, which focuses on the investigation on operational control in DIMS. Based on the established agent-based modelling architecture in DIMS, this research develops two agent bidding mechanisms for the hierarchical control of production planning and scheduling. These two mechanisms work together to assist manufacturing systems in making optimal and flexible operational decisions in response to changes in the business environment. The first mechanism is the iterative agent bidding mechanism based on a Genetic Algorithm (GA) which facilitates the determination of the optimal or near optimal allocation of a production job containing a set of sub-jobs to a pool of heterarchical resources. The second mechanism is the hierarchical agent bidding mechanism which enables product orders to be cost-efficiently and flexibly planned and scheduled to meet the orders’ due dates. The novelty of this mechanism is that it enables orders to be fulfilled within structural constraints of manufacturing systems as far as possible and however enables resources to be regrouped flexibly across system boundaries when orders cannot be fulfilled within structural constraints of manufacturing systems.

Zhang, Lianmin. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 130-139). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

Scheduling is concerned with the process of deciding how to commit resources between a variety of possible tasks with the aim of optimizing some performance criterion. Efficient scheduling is a vital tool in successful decision-making. To date, an enormous amount of research has been done on scheduling problems arising from various disciplines. Major attention has so far been dedicated to off-line (usually deterministic) scheduling problems. Off-line deterministic scheduling deals with perfect information. That is, all information with regard to a problem is known prior to any decision. However, this perfect information assumption violates the nature of many realistic issues with uncertainties, for example, a situation where the knowledge of problem instances is revealed over time, or a scenario in which processing tasks are temporarily disrupted or cancelled. To better formulate this sort of problem with high uncertainties, a new concept of on-line scheduling was introduced. On-line approaches have become increasingly important and are frequently encountered when only partial knowledge is available but instant or very fast solution methods are required and should nevertheless result in good outcomes. In on-line scheduling, a decision maker allocates resources between tasks as the information is gradually released. Obviously, given the same scheduling environment and problem instance, the result produced by an on-line scheduler cannot be better than that by the optimal off-line scheduler; but the on-line scheduling technique immunizes schedules from future disruptions and uncertainties. / This thesis extends the study of some scheduling problems derived from various industrial and computing situations to on-line scheduling environments, specifically the on-line-list and the on-line-time paradigms. The six topics studied are classified in two parts in this thesis. Part I consists of three machine scheduling problems taking into account various types of setup considerations. Part II includes the other three scheduling problems which are closely related to issues arising in the management of shipping containers and wind energy. / The effort is focused on constructing effective and efficient (on-line) decision-making strategies with the purpose of optimizing certain objective measures in those uncertain scheduling environments. The performance of the proposed heuristics as well as some existing on-line algorithms is evaluated and compared via competitive analysis. For some cases, empirical studies are also carried out to assess their average performance.

This report describes Processor Coupling, a mechanism for controlling multiple ALUs on a single integrated circuit to exploit both instruction-level and inter-thread parallelism. A compiler statically schedules individual threads to discover available intra-thread instruction-level parallelism. The runtime scheduling mechanism interleaves threads, exploiting inter-thread parallelism to maintain high ALU utilization. ALUs are assigned to threads on a cycle byscycle basis, and several threads can be active concurrently. Simulation results show that Processor Coupling performs well both on single threaded and multi-threaded applications. The experiments address the effects of memory latencies, function unit latencies, and communication bandwidth between function units.

Nursing costs account for over 50% of Health Service Organizations budgetary expenses. In a financially contracting Healthcare market that is amidst the focus of current National and International economic concerns and political agenda, here a counter-intuitive method to minimize exposure to rising nursing costs. Healthcare’s conundrum is marked by rising nursing costs, growing patient population, rising uninsured rates and decreasing insurance reimbursements. Participants traditionally focus on nurse staffing to minimize costs, but in its inextricable link to scheduling, budgets are often inaccurately projected as compared to actual staffing quantities and costs; this is largely due to front-line staffing policies and unpredictable nursing rates. This paper presents a nationwide experimental and empirical study of ten healthcare participants in a cross market “Hedging” application in Nursing Services as an approach to reduce exposure to rising nursing costs based on nursing rate volatility notwithstanding nursing quantity needs and day-to-day staffing decisions, and considering Options as a primary hedging approach to reduce budget disparity and yield nursing expense savings. Nursing monthly costs and demand were collected for all participants over varying range of time periods. A correlation analysis indicated that total nursing costs are highly correlated to nursing rate change, differing across participant types. Additionally, the data was analyzed for “asset” and “options” applicability, as well as tested for appropriateness of the Black-Scholes model for options pricing. The analysis concluded that nursing service qualifies as an underlying asset for options as a hedging technique and may be priced using the Black-Scholes model. The approach was tested on one of the participants, and indicated a savings of over 11% in nursing expenses and a decrease in budget disparity of approximately 14%. Hypothetical application across the non-tested participants alludes that the implementation results are likely to be sustainable across participant with dissimilar demographics.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 96-108). Available also in a digital version from Dissertation Abstracts.

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Scheduling is concerned with the process of deciding how to commit resources between a variety of possible tasks with the aim of optimizing some performance criterion. Efficient scheduling is a vital tool in successful decision-making. To date, an enormous amount of research has been done on scheduling problems arising from various disciplines. Major attention has so far been dedicated to off-line (usually deterministic) scheduling problems. Off-line deterministic scheduling deals with perfect information. That is, all information with regard to a problem is known prior to any decision. However, this perfect information assumption violates the nature of many realistic issues with uncertainties, for example, a situation where the knowledge of problem instances is revealed over time, or a scenario in which processing tasks are temporarily disrupted or cancelled. To better formulate this sort of problem with high uncertainties, a new concept of on-line scheduling was introduced. On-line approaches have become increasingly important and are frequently encountered when only partial knowledge is available but instant or very fast solution methods are required and should nevertheless result in good outcomes. In on-line scheduling, a decision maker allocates resources between tasks as the information is gradually released. Obviously, given the same scheduling environment and problem instance, the result produced by an on-line scheduler cannot be better than that by the optimal off-line scheduler; but the on-line scheduling technique immunizes schedules from future disruptions and uncertainties. / This thesis extends the study of some scheduling problems derived from various industrial and computing situations to on-line scheduling environments, specifically the on-line-list and the on-line-time paradigms. The six topics studied are classified in two parts in this thesis. Part I consists of three machine scheduling problems taking into account various types of setup considerations. Part II includes the other three scheduling problems which are closely related to issues arising in the management of shipping containers and wind energy. / The effort is focused on constructing effective and efficient (on-line) decision-making strategies with the purpose of optimizing certain objective measures in those uncertain scheduling environments. The performance of the proposed heuristics as well as some existing on-line algorithms is evaluated and compared via competitive analysis. For some cases, empirical studies are also carried out to assess their average performance.

Thesis (Ph. D.)--University of Canterbury, 2007. / Typescript (photocopy). Accompanied by CD-ROM: Appendix 1: Electronic copy of source code and input data. Includes bibliographical references (leaves 173-179). Also available via the World Wide Web.