A modified particle swarm optimization for aggregate production planning

• Published in: Expert systems with applications 2014-05, Vol.41 (6), p.3069-3077
• Main Authors: Wang, Shih-Chang, Yeh, Ming-Feng
• Format: Article
• Language: English
• Subjects: Aggregate production planning (APP); Aggregates; Algorithmics. Computability. Computer arithmetics; Applied sciences; Artificial intelligence; Coding; Computer science; control theory; systems; Constraints; Exact sciences and technology; Genetic algorithms; Integer linear programming model; Inventory control, production control. Distribution; Learning and adaptive systems; Mathematical programming; Operational research and scientific management; Operational research. Management science; Particle swarm optimization (PSO); Production planning; Search process; Swarm intelligence; Theoretical computing
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2013.10.038

•An APP model for the manufacturer of gardening equipment is proposed.•A modified PSO introducing the concept of sub-particles to the update rules is proposed.•Some experiments implemented by MATLAB and LINGO are evaluated among MPSO, SPSO and GA.•The MPSO gains particular qualities in accuracy, reliability, and convergence speed than others. Particle swarm optimization (PSO) originated from bird flocking models. It has become a popular research field with many successful applications. In this paper, we present a scheme of an aggregate production planning (APP) from a manufacturer of gardening equipment. It is formulated as an integer linear programming model and optimized by PSO. During the course of optimizing the problem, we discovered that PSO had limited ability and unsatisfactory performance, especially a large constrained integral APP problem with plenty of equality constraints. In order to enhance its performance and alleviate the deficiencies to the problem solving, a modified PSO (MPSO) is proposed, which introduces the idea of sub-particles, a particular coding principle, and a modified operation procedure of particles to the update rules to regulate the search processes for a particle swarm. In the computational study, some instances of the APP problems are experimented and analyzed to evaluate the performance of the MPSO with standard PSO (SPSO) and genetic algorithm (GA). The experimental results demonstrate that the MPSO variant provides particular qualities in the aspects of accuracy, reliability, and convergence speed than SPSO and GA.