Comparative Study of Multi-item batch Scheduling and the Hybrid of Proposed and Colony Algorithm-Tabu Search

This paper addresses the problem of scheduling jobs with multi batches. Multi-Item Batch Scheduling Model divides jobs into several batches so that the scheduling problem does not only determine the sequence of batches, but also determines the number of batches and batch sizes are used. The research attempts to compare the methods of multi-item batch scheduling to the combination of tabu search and proposed ant colony algorithm (PACO-TABU) to minimizing mean tardiness.
Multi-item batch scheduling model uses two methods to determine the size, sequence, and many batches are used. The method used is slack method and PACO-TABU algorithm. Experimental analyses are presented to compare the performance of the proposed PACO-TABU algorithm and multi-item batch scheduling in term of mean tardiness. The results show PACO-TABU algorithm has better performance than the method of slack in term of mean tardiness.