Thesis (Ph.D.) -- McMaster University, 1999. / Includes bibliographical references (leaves 92-95). Also available via World Wide Web.

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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.

Allowing the production rate to be changed at any time, Chapter 4 investigates the optimal production and setup scheduling policy which minimizes the average inventory, backlog, and setup costs in a one-facility, two-product system. Under the optimal control, the system will reach a limit cycle in a finite time. In the cyclic schedule, each product is produced at its demand rate for a fraction of the production time. This contradicts the conventional wisdom where only one product should be produced at its demand rate. Moreover, we partially prove the optimality of the proposed policy. / Given that the unit inventory costs are nondecreasing along the route, and the last machine is the bottleneck, Chapter 2 provides an optimal production policy, which minimizes the total inventory and backlog costs in a multiple-product flow line. The production capacity is allocated to individual products according to the ranking of marginal benefits. The capacity allocation may change only when a buffer reaches the level of zero. This approach not only extends the study from the one-product system to the multiple-product system, but also has a computational complexity of O(MN), where M and N are the number of machines and the number of products in the system. In addition, it can be applied to the discounted-cost problem. The optimality of the proposed policy is further verified by the Hamilton-Jacobi-Bellman equation. / The problem of production and setup scheduling in a one-facility, multiple-product system is considered in Chapter 3. Besides the production cycle time and the lot sizes, the production rate is also a control variable. We demonstrate that the production of the product, which has the highest unit inventory and backlog costs weighted by its demand rate, should be slowed down. Our results outperform those of the classical Economic Lot Scheduling Problem. / This research significantly improves previous results of production and setup scheduling in complex, deterministic, and multiple-product systems. Insights and conditions of better production and setup scheduling are provided. These results are not only applicable to deterministic systems, but also suitable for constructing policies for stochastic systems. / This thesis is concerned with the problem of production and setup scheduling in continuous and deterministic manufacturing systems which produce multiple products with constant exogenous demands. / Jun Yang. / "October 1997." / Source: Dissertation Abstracts International, Volume: 59-11, Section: B, page: 6019. / Supervisors: Houmin Yan; Xiaoqiang Cai. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (p. 159-168). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest dissertations and theses, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Thesis (M.S.)--Ohio State University, 1984. / Includes bibliographical references (leaves 129-135). Available online via OhioLINK's ETD Center

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Within manufacturing, increasing interest in being placed in the possibilities of integrated process planning and scheduling. Separating these two related tasks can impose constraints, on the final schedule, which are both undesirable and unnecessary. These constraints arise from premature decisions regarding the allocation of manufacturing resources. By making use of flexible process plans, these decisions can be delayed until the most appropriate time: during scheduling. The decisions can then be made on the basis of objectives common to both tasks (such as the minimisation of manufacturing cost). This thesis outlines an approach to manufacturing planning which is based on a highly general formulation of the problem. This integrated process planning/scheduling problem can be viewed as a generalisation of process plan optimisation, a task which is also considered in detail. A novel approach to plan optimisation is proposed, which in turn forms the basis for integrated planning and scheduling. Some research into integrated planning/scheduling has been reported in the literature. However, researchers differ in the way they formulate the integrated task. This thesis therefore attempts to outline a general framework for the characterisation of integrated process planning and scheduling problems. This considers both the degree and representation of process plan flexibility, and also the level of detail at which the shop floor is modelled. This framework forms a basis for a comparison of solution approaches. Published solution approaches are mostly based on the use of dispatching rules, but attempts have been made to use optimal search. The use of dispatching rules is essentially an ad hoc approach and, although relatively easy to apply in practice, produces solutions of mediocre quality. However, new research using simulated annealing suggests that neighbourhood search may offer a valuable alternative. This observation is supported by ambitious research published on the use of genetic algorithms. Because of the extreme combinatorial complexity of the combined task, optimal search methods are unlikely to be usable in practice. Furthermore, such methods exhibit a severe lack of generality because they make highly specific assumptions about problem formulation. Neighbourhood search techniques have inherent properties which give them a much higher level of generality. Although it is not an optimal search method, simulated annealing has been shown to provide solutions of significantly higher quality than those achieved by dispatching rule techniques. Also, and unlike optimal search techniques, it appears able to handle the immense complexity of the integrated planning/scheduling task. For the above reasons, it is argued that neighbourhood search techniques, such as simulated annealing, provide the best compromise available between solution quality and practical applicability.

The majority of the commercially available systems developed for capacity planning have been designedf or organisationsw hich are involved with make-to-stockm anufacturingT. heses ystemsa re difficult to implementi n bespokem anufacturing( make-to-ordero) rganisationss ince they are unable to accommodateth e requirementso f the volatile productione nvironment. This thesisi dentifies the uniqueo peratingc haracteristicos f the manufacturinge nvironmenta ssociated with bespokem anufacturingw hich caused ifficulty with the implementationo f commerciallya vailable systemsT. he findings from this investigationa re usedt o formulatea design for a capacityp lanning systemw hich fulfils the unique requirementso f the bespokem anufacturinge nvironment. The proposed system executes the capacity planning function at two separate levels of detail by integrating rough cut capacity planning with finite capacity planning. The two planning mechanisms are also integrated with a shop floor data collection system which has been designed for the assembly environment. Data interaction is achieved between the capacity planning modules and the shop floor data collection system via an Interface ManagemenSt ystem which ensurest hat the scheduled ata containedi n the individual systemsr emainsi n synchronisationa t all times. The capacity planning system also includes the design and development of a large scale display facility which is able to accommodateth e large volume of scheduled ata required to be viewed for finite capacity planning. This facility has been designed using multiple co-ordinated screens which are linked by a local area network. A data interface and parallel processing facility is incorporated in the design which enable the processing of a software application to be distributed over multiple nodes.