Scheduling Unrelated Parallel Batch Processing Machines With Non-Identical Job Sizes, Incompatible Families And Unequal Ready Times

Abstract

In recent years, the field of parallel batch processing machine (BPM) scheduling has gained significant attention in various manufacturing industries. Unrelated parallel machine scheduling, which considers workstations with diverse machine capabilities, is particularly relevant for real-world manufacturing settings. The study aims to create a scheduling algorithm that optimizes the usage of processing machines, reducing production time, and increasing throughput, thereby generating cost savings, and enhancing competitiveness and sustainability in the manufacturing industry. The process implemented in this study involves a rigorous methodology that integrates both Mixed Integer Linear Programming (MILP) and Ant Colony Optimization (ACO) heuristics. This problem is solved by developing a mathematical model, solving it using CPLEX for small scale data, and solving it using an ACO method (selected after evaluating many algorithms) for large scale data. The results of this study show that the approaches used are appropriate and efficiently solve current problems, such as obtaining optimum or nearly optimal solutions within limited time periods. Additionally, this study has revealed a number of fascinating new directions of research for the future.