Modeling and analysis of manufacturing processes

In this dissertation, we model and analyze three important manufacturing processes. These processes, important to pharmaceutical manufacturing, are applicable to other industries. The first problem addressed is a multiple stage deterministic flow shop manufacturing scheduling problem. We solve this problem using a dynamic programming approach. The second problem considered is a stochastic parallel process manufacturing scheduling problem. We solve this problem using a probability analysis. Finally, the third problem we address is a manufacturing plant material handling problem. We first develop an approach to solve the single vehicle routing problem, which is equivalent to the rural postman problem (RPP). The approach formulates the RPP as an “ultra-network”. Then, solving the RPP is equivalent to computing a shortest path in the ultra-network. Thus, any method for computing a shortest path in a cyclic network can be used for obtaining an optimal solution for the RPP. We also extend the approach to solve the multiple and stochastic vehicle routing problems.; All of these problems are associated with the manufacture of an active pharmaceutical ingredient (API). Typically, an API is blended with inactive ingredients to achieve the correct dosage of a pharmaceutical product. Thus, this dissertation focus mainly on production scheduling and material handling in API manufacturing. API manufacturing process modeling and analysis have been under utilized in the pharmaceutical industry. Manufacturing process optimization can bring significant benefit to the pharmaceutical industry. It is our intent that the application of this research will result in better use of manufacturing facilities, better use of labor, shorter production cycle times, and increased productivity.