Fuzzy Optimization for Order and Production Scheduling

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9500037 Luh This research addresses the problem of integrating scheduling decisions on order.s deliveries and job scheduling in a manufacturing enterprise. The problem of scheduling order deliveries is focused on when materials and supplies are to be delivered at a manufacturing site to support the manufacturing function. Job scheduling is focused on the problem of determining the sequence in which jobs are to be processed on machines and when to start and complete each job so as to meet some objective. In most operations, these two forms of scheduling decision are independently made from one another even though decision in one area affects the other. Because of this relationship, this research aims to develop decision procedures to integrate them. The multiple objectives pursued in the research include: (1) development of optimization models and near-optimal solution methodologies for the integrated problem for job shops, flow shops, and hybrid shops in a deterministic setting by using Lagragian relaxation, (2) extension of the different shop models to handle uncertainty by using fuzzy set theory, and develop fuzzy optimization techniques within the Lagrangian relaxation framework, and (3) development of algorithms for the unconstrained optimization of nonsmooth functions with a large number of variables, which arise in one of the steps of the Lagrangian relaxation approach. Given the traditional scheduling problems are known to be notoriously hard to solve, the integrated problem will pose additional challenges to solve. However, given the critical nature of the problem, attempts to characterize the problem to provide some insight will be beneficial to the research and the practitioners community. Industries stand to benefit economically if a successful solution to the problem is developed.