Evolutionary algorithm and Threshold accepting algorithm for scheduling in two-machine flow shop with lot streaming

In this paper, two different approaches have been proposed for solving two machine flow shop problems with multiple jobs requiring lot streaming with the objectives of minimizing the makespan and the total flow time of jobs. A population based evolutionary algorithm that involves evolution during the search process and a single point local search metaheuristics that work on a single solution are proposed. The population based approach employs local search, after the genetic search is over, to improve the effectiveness of the search procedure and hence it is called hybrid evolutionary algorithm (HYBRID). A threshold accepting algorithm (TA) proposed in this paper is a single point local search metaheuristic. A job here implies many identical items. Lot streaming creates sub lots to move the completed portion of a production sub lots to down stream machine. Proposed algorithms are evaluated using a set of randomly generated test problems. Experimental results are presented for comparison.