Dynamic integrated process planning, scheduling and due-date assignment using ant colony optimization

Abstract

This paper presents two well-known meta-heuristics which are Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) to solve the dynamic integrated process planning, scheduling and due date assignment problem (DIPPSDDA) in which jobs arrive to the shop floor randomly. In this study, it is aimed to find the best combination of dispatching rule, due date assignment rule and route of all job with the objective of minimizing earliness, tardiness and due-dates of each jobs. 8 different size shop floors for the comparison of the GA and ACO algorithms performances have been developed. As a result of the experimental study, it was concluded that ACO algorithm outperformed GA algorithm. In addition, it has been suggested that integrated approaches can provide more global manufacturing efficiency than individual approaches.