An Improved Particle Swarm Optimization for Selective Single Machine Scheduling with Sequence Dependent Setup Costs and Downstream Demands

Abstract

This paper investigates a special single machine scheduling problem derived from practical industries, namely, the selective single machine scheduling with sequence dependent setup costs and downstream demands. Different from traditional single machine scheduling, this problem further takes into account the selection of jobs and the demands of downstream lines. This problem is formulated as a mixed integer linear programming model and an improved particle swarm optimization (PSO) is proposed to solve it. To enhance the exploitation ability of the PSO, an adaptive neighborhood search with different search depth is developed based on the decision characteristics of the problem. To improve the search diversity and make the proposed PSO algorithm capable of getting out of local optimum, an elite solution pool is introduced into the PSO. Computational results based on extensive test instances show that the proposed PSO can obtain optimal solutions for small size problems and outperform the CPLEX and some other powerful algorithms for large size problems.

Mixed Production Line Optimization of Industrialized Building Based on Ant Colony Optimization Algorithm

“…As is known to all, a metaheuristic algorithm can be used to solve such problems [20], and the manufacturing industry has done a lot of related research. According to the literature review, mainly including genetic algorithm (GA) [29,[32][33][34][35][36][37], particle swarm calculation (PSO) [30,38,39] and ant colony algorithm (ACO) [31,34,40], etc. At present, ACO has been proved to be an effective method to solve multiobjective optimization problems [31,34,40].…”