Hybridization of multiple intelligent schemes to solve economic lot scheduling problem using basic period approach

Economic Lot Scheduling Problem (ELSP) has been an area of active research for many years. Different approaches have been proposed to find the optimal solution for the problem. Traditionally, researchers have used a single algorithm to find the solution. In this paper, we argue that better results can be obtained for the ELSP problem, if we use a hybridization scheme instead of the traditional single algorithm approach. In this context, we suggest multiple hybridization of an "intelligent" technique with Golden Section Search (GSS) to solve ELSP using basic period approach. We have used three hybrid approaches based on Simulated Annealing (SA), Cuckoo Search (CS), and Particle Swarm Optimization (PSO) to find the optimum value of integer multiple k_i's and GSS to find the optimum value of basic period T. The proposed hybridized schemes are applied on Bomberger's dataset [1], random data generated using distribution given in Dobson's [2] and also on random data generated using new distribution derived from Bomberger's dataset [1]. Comparative analyses are presented in which the hybridized algorithms based on SA, CS and PSO incorporated with GSS are compared. These hybridized schemes were found efficient for both low and high machine utilization.