Setting Optimal Production Lot Sizes and Planned Lead Times in a Job Shop System

In this research we model a job shop that produces a set of discrete parts in a make-to-stock setting. The intent of the research is to develop a planning model to determine the optimal operating tactics that minimize the relevant manufacturing costs subject to workload variability and capacity limits. We model the interplay of three key components in the job shop, namely, the production frequency for each part, the variability of production at each work station, and the level of parts inventory. We consider two operating tactics (decision variables): the production lot size for each part and the planned lead time for each work station. We model the relevant manufacturing costs, entailing production overtime costs and inventory-related costs (finished parts, work-in-process, and raw materials), as functions of these decision variables. We formulate a non-linear optimization model and implement it in the Excel Spreadsheet. We solve the model with the premium Excel Solver to determine the minimum-cost operating tactics. We test the model with both hypothetical and actual factory data from our research sponsor. The target factory processes 133 product parts on 59 work stations. The results are consistent with our intuition and demonstrate the potential value from optimizing over these tactics; these tests also provide some managerial insights on the application of these operating tactics. Thesis Supervisor: Stephen C. Graves Title: Abraham J. Siegel Professor of Management Science & Engineering Systems 1