Local Search Methods for Solving Single Machine with Family Setup Time to Minimize the Multi-Objective Function Problem

Abstract

In this paper, three of the local search algorithms are used Bee Colony Optimization (BCO),Invasive Weed Optimization (IWO) and genetic Algorithm (GA), in scheduling number of products n jobs on a single machine with setup time to minimize a multi-objective functions and discount total completion time and maximum tardiness respectively, which is denoted as 1// +). In this paper we used branch and bound method and local search methods (BCO, IWO, GA) respectively, to comparing the results for n=5,6,7,,.,,17, which the n of jobs more than 18 jobs we used only local search method for find near optimal solution. The results show that the three algorithms have found the near optimal solutions in an appropriate time and the genetic algorithm is better comparison to other algorithms and faster time.