A Greedy Search Based Ant Colony Optimization Algorithm for Large-Scale Semiconductor Production

Ramsha Ali, Shahzad Qaiser, Mohammed M. S. El-Kholany, Peyman Eftekhari, Martin Gebser, Stephan Leitner, Gerhard Friedrich

Abstract: This paper presents a hybrid ant colony optimization algorithm for solving large-scale scheduling problems in semiconductor production, which can be represented as a Flexible Job Shop Scheduling Problem (FJSSP) with the objective of minimizing the makespan required to complete all jobs. We propose a Greedy Search based Ant Colony Optimization (GSACO) algorithm, where each ant constructs a feasible schedule using greedy search. For the sequencing of operations, accomplished in the first phase of GSACO, the ants adopt a probabilistic decision rule taking pheromone trails into account. The flexible machine assignment is then greedily performed in the second phase by allocating operations one by one to an earliest available machine. We evaluate our approach using classical FJSSP benchmarks as well as large-scale instances with about 10000 operations from the domain of semiconductor production scheduling. On these large-scale scheduling problems, our GSACO algorithm successfully overcomes