| dc.description.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. | en_US |
