Robust scheduling to hedge against processing time uncertainty in single-stage production

Article Abstract:

Schedulers faced with high uncertainty in processing time usually find out that schedules that are optimal in relation to a deterministic or stochastic model often provide very poor results when compared to actual processing times. In such instances, the concept of schedule robustness is often the more useful guide to the selection of schedules. This concept, which selects the schedule with the best worst-case showing when compared to the optimal solution for all job-processing-time realizations, is formalized for situations involving variable and uncertain processing times. It is then applied to a problem involving a single-machine environment where the total flow time across all jobs is used as the performance criterion of interest. In relation to this problem, two schedule robustness measures are provided, along with the problem formulation, its complexity, a description of optimal schedule properties and the relevant solution procedures.

Research

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Scheduling parallel manufacturing cells with resource flexibility

Article Abstract:

Job scheduling in parallel, single-machine manufacturing cells and the allocation of single resource across cells to improve system performance are investigated. The study examines manufacturing performance improvements that can be achieved by expanding the scope of the production scheduling function by including job sequencing as well as processing-time control by allocating a flexible resource. Two mechanisms for deploying the resource among parallel processors are modelled through the formulation and resolution of a static problem. One of these is a static problem in which a single resource-allocation decision is made and maintained throughout the production horizon. The other is a dynamic problem in which resource can be re-deployed among the manufacturing cells depending on the shifting of local bottlenecks. Computational results show that effective use of resource flexibility can significantly improve operational performance.

Analysis, Resource allocation

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