The Optimization Inventory Process on Identical Machine Job Shop with Multiple Setups Using Genetic Algorithm

Abstract

In every production process, whether in a flow shop or a job shop, inventory is inevitable. However, for some people or companies, its presence is often not a significant concern. On the production floor, inventory is categorized into two types: product inventory and work-in-process inventory. Inventory arises when production scheduling incorporates lot size into production planning. In flow shop scheduling, inventory typically accumulates at the beginning and end of the process (finished goods). In contrast, in job shop scheduling, inventory not only appears at the start and end of each process but also during the middle of the machine process. This occurs if a machine requires more than one setup to complete a product. Currently, there is no research that addresses the impact of inventory on job shop scheduling where each machine necessitates multiple setups while ensuring the completion time does not exceed the due date and production capacity. Given that job shop scheduling with multiple machines, products, and setups is an NP-hard problem, this study will employ a genetic algorithm for the optimization process. The results of inventory optimization using a genetic algorithm show that production costs were reduced from $48,519 to $43,140, representing an 11% decrease.