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Person# Byung Jun Joo

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Related publications (3)

This paper focuses on the scheduling problem of minimizing makespan for a given set of jobs in a two-stage hybrid flowshop subject to a product-mix ratio constraint. There are identical parallel machines at the first stage of the hybrid flowshop, while there is a single batch-processing machine at the second stage. Ready times of the jobs (at the first stage) may be different, and a given product-mix ratio of job types should be kept in each batch at the second stage. We present three types of heuristic algorithms: forward scheduling algorithms, backward scheduling algorithms, and iterative algorithms. To evaluate performance of the suggested algorithms, a series of computational experiments are performed on randomly generated test problems and results are reported.

2009Paul Xirouchakis, Byung Jun Joo

A scheduling problem in a real production line with uncertain sequence-dependent set-up times and a random yield is considered. The production line can produce multiple product types as production lots, each of which is composed of a number of products of the same product type. To changeover product types, a sequence-dependent set-up operation should be performed, and only the lower and upper bounds are known for the sequence-dependent set-up times. Moreover, the processing time to produce the required number of product for each production lot is uncertain due to the random yield. For the objective of minimising the average tardy probability of given production lots, a systematic approximation scheme to estimate tardy probabilities of lots in any given production sequence is developed by taking not only the uncertainties but also the computational efficiency into account. As practical solution approaches, a simulated annealing and a discrete particle swarm optimisation algorithms using the approximation scheme are developed, and their performance are evaluated by computational experiments. Since there has been no research on the scheduling problems with uncertain sequence-dependent set-up times and random yield, the authors expect this research will make an excellent contribution to develop practical scheduling methodologies in uncertain scheduling environments.

Paul Xirouchakis, Yong Chan Choi, Byung Jun Joo

We consider a three-stage dynamic flexible flow shop scheduling problem in which jobs of multiple types arrive dynamically over time, a quality feedback mechanism is present, and the setup timing and the process defect rate are closely related. At each machine in the second stage, a sequence-independent setup operation is necessary to changeover job types. Once a setup is done for a job type at a machine, the defect rate for the job type at the machine is reset to a low and stable phase which will be maintained for a predetermined time periods. However, after the phase, it turns to a relatively high and unstable phase. At the final inspection stage, jobs are inspected and the quality feedback will be given to the previous stage when the accumulative defect rate of each job type exceeds a certain tolerance level. To cope with the dynamic nature of the flexible flow shop scheduling problem, we propose two dispatching rule-based scheduling algorithms which consider the quality feedback as well as the real time shop information for the objectives of maximizing the quality rate and the mean tardiness of the finished jobs. The results of a series of simulation experiments will be given to evaluate the performance of the suggested algorithms. Since there have been few research on the shop floor scheduling problems with quality feedback, we expect that this research will make a contribution to the development of a practical real time scheduling methodology in multi-stage production systems with the consideration of the imperfect process quality.