Optimizing scheduling in a pharmaceutical company

Abstract

Scheduling is very important task which is used on a daily basis. A "good" schedule will increase the company's' profit and customers will be more willing to buy products as they are being satisfied in the shortest period of time. It is also important as it may solve many different objective functions such as minimization of makespan; minimization of delays; and minimization of total completion time. There has been an extensive research about algorithms to solve such problems. An overview of these algorithms both from a deterministic theoretical part and stochastic theoretical part is provided. This dissertation mainly focuses on solving the minimization of makespan on identical parallel machines scheduling problem. A mixed integer linear program (MILP) is built to solve this scheduling problem by using real live data from a local pharmaceutical company. In addition, the Longest Processing Time (LPT) heuristic algorithm which is one of the famous and oldest scheduling algorithms is used to compare its results with the MILP problem results and the original schedule by the company.