Dynamic optimization of job allocation using constant job-mix stages and priority factors

The dynamic optimization proposed in this work uses a linear programming technique to maximize the quantity of job orders processed on the machines at each constant job-mix stage. Priority factors guide the sequential allocation of partial and complete setups by ranking job/machine combinations in order of processing performance and capacity to meet due dates.The job allocation is governed by an algorithm which constitutes the backbone of the dispatching software that was developed and used to solve the examples presented in this thesis.Priority factors offer an effective mean of guiding the selection of setups by ranking job/machine combinations by processing speed and capacity to meet due times. The necessity for priority factors becomes more important as setup times increase in relation with quantities to be manufactured and the processing times. Priority factors also guide the allocation of partial setups on the machines to speed up job completion.The job allocation system has a substantial potential for providing shorter makespan than the Shortest Operating Time methodology by increasing machine utilization