Job Shop Scheduling using ACO Meta-heuristic with Waiting_Time-based Pheromone Updating

In the vast optimization field, many computer-aided techniques were proposed and tested in the last decades. The artificial intelligence meta-heuristics constitute the widest part of such techniques, which proved to be adequate to (near) optimally solve big difficult instances, as the most real optimization problems are. Among them, the agent-based techniques are the most recent ones and they reported in the literature very good results compared to many other optimization methods. Such methods are: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Wasp Behavior Model (WBM) and negotiation techniques. In this paper a research study on ACO applicability to Job Shop Scheduling Problems (JSSP) is reported and a waiting timebased pheromone updating formula is proposed. This is tested on a simple JSSP case study using job list representation. The results show that ACO is able to optimally solve JSS optimization problems. Moreover, ACO is a meta-heuristic relatively easy to apply and has a wide optimization scope for static combinatorial optimization problems.