Improvement Of Ant Colony System By Using Pheromone Updation

General job shop problem is the probably most studied one by academic research during the last three decades and is notoriously difficult problem to solve. The JSSP is an NP (Nondeterministic Polynomial) hard problem and among those optimization problems, it is one of the least tractable known problem [1]. It is purely deterministic, since processing time and constraints are fixed and no stochastic events occur. Each operation has its own processing time and has to be processed on a dedicated machine. Given a nuber of „n‟ jobs, the jobs have to be processed on „m‟ machines. Each job has its own machine order and no relation exists between machine orders of any two jobs. For each job, the machine order of operations is prescribed and is known as technological production recipe or technological constraints, which are static to a problem instance. A feasible schedule is a sequence of operations on a machine ensuring the job shop constraints. A makespan is defined as the maximum completion time of the jobs. The objective of the JSSP is to find a feasible schedule with minimum makespan. A recent adaptive algorithm, named Ant System, is introduced and used to solve the problem of jobshop scheduling. The algorithm was first introduced by Dorigo, Maniezzo and Colorni in 1991[2] and is derived from the foraging and recruiting behaviour observed in an ant colony. It can be applied to combinatorial optimisation problems. The study from biology of an ant colony shows that its behaviour is highly structured. Knowing that a single ant has limited capacities (i.e., a single ant is not capable of communicating directly with other ants about past experiences), it is curious to know how the ants co-operate so as to achieve such a complex and organised behaviour of the whole colony. Maybe one of the most studied cooperation phenomena among ants is the socalled foraging and recruiting behaviour [3][4][5][6]. This behaviour describes how ants explore the world in search of food sources, then find their way back to the nest and indicate the food source to the other ants of the colony. To do so, ants use an indirect way to communicate through tracks of Abstract: Scheduling theory is one of the significant research areas in operations research. It has been the subject of research with techniques ranging from simple dispatching rules to sophisticated learning algorithms. Effective scheduling can improve production systems and the utilization of resources. The job shop problem is under this category and is combinatorial in nature. Research on optimization of job shop problem is one of the most significant and promising areas of optimization. The nature has inspired several metaheuristics, outstanding among these is ant colony optimization (ACO), which have proved to be very effective and efficient in problems of high complexity (Np-hard) in combinatorial optimization. This paper presents an application of the ant colony optimization metaheuristic to job shop problem. The inspiring source of ant colony optimization is pheromone trail laying and following behavior of real ant. The methods of updating the pheromone have more influence in solving instances of job shop problem. An algorithm is introduced to improve the basic ant colony system by using a pheromone updating strategy.