Ant colony-based algorithms for scheduling parallel batch processors with incompatible job families

In current dynamic business environments, meeting the customer due dates and avoiding delay penalties are very important. Our work concerns with the static scheduling of jobs to parallel identical batch processors for minimising the total weighted tardiness. It is assumed that the jobs are incompatible in respect of job families indicating that jobs from different families cannot be processed together. In practice, the problem cannot be solved using any classical OR algorithms in polynomial time due to the problem complexity. We develop metaheuristics, namely, the ant colony optimisation (ACO) approach to solve the problem efficiently. We propose three ant colony-based algorithms by using the structural properties of the problem. An extensive experimentation is conducted to evaluate the performance of the proposed algorithms on different problem sizes with the varied tardiness factors. Our experimentation shows that ACO-based algorithms perform better as compared to the available best dispatching rules and algorithms.