Job shop scheduling problem (JSP or JSSP) is one of the problems in manufacturing industry. In this problem, a sequential job allocation on resources (machines) that optimizes a particular objective function is to be determined. In a typical JSSP, a finite set of jobs is executed on a finite set of machines. Each job is featured by a fixed order of operations, each of which is to be executed on a specific machine for a specified duration. The objective is to generate a schedule that indicates starting processing time of each job on a particular machine. At the same time, the generated schedule must minimum in completion time, or the makespan.

Recently, biologically inspired approaches such as Ants Colony Optimization (ACO) and Bee Colony Optimization (BCO) have been proposed to address some complex scheduling problems including JSSP. Both techniques are inspired from a cooperative foraging and communication strategy adopted in real ants and bees. In an ant colony, it was known that ants deposited substance known as pheromone along the way from their colony to food source. These pheromone trails are used as a communication medium among ants as the ants will tend to follow the trail with high concentration of pheromone. On the other hand, in a bee colony, the communication among the bees about location of the food source is via the waggle dance. Upon returning to the hive with pollen and nectar, a bee will perform an elaborate dance (round dance and figure-eight dance) to hint other bees about the direction and distance to the food source.

This project intends to investigate on the use of different biologically inspired approaches to address the JSP. A few major tasks will be carried out along the project duration including development and implementation of the optimization algorithms and comparison study of the developed algorithms with the existing techniques such as GA, TS and SA.