A possibilistic environment based particle swarm optimization for aggregate production planning. (October 2015)

• Record Type: Journal Article
• Authors: Chakrabortty, Ripon K.; Akhtar Hasin, Md.A.; Sarker, Ruhul A.; Essam, Daryl L.
• Is Part Of: Computers & industrial engineering. Volume 88(2015)
• Page Start: 366
• Page End: 377
• Publication Date: 2015-10
• Subjects: Particle swarm optimization -- Possibilistic environment -- Aggregate production planning; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2015.07.021
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Considered an aggregate production planning problem with imprecise parameters. Developed a mathematical model for representing the problem. A variant of PSO algorithm is proposed to solve the model. A case study is conducted to demonstrate the usefulness of the proposed approach. Our approach provides better solutions than the known approaches compared. Abstract: Development of an Aggregate Production Plan (APP), the top most level in a hierarchical production planning system, is a difficult task, especially when input and other production planning parameters are uncertain because of their inherent impreciseness. This therefore makes generation of a master production schedule highly complex. Regarding this point, in this paper, we present a scheme of a multi-period and multi-product APP which is formulated as an integer linear programming model. The proposed approach uses a triangular possibility distribution for handling all the imprecise operating costs, demands, and also for the capacity data. The proposed approach uses the strategy of simultaneously minimizing the most possible value of the imprecise total costs, maximizing the possibility of obtaining lower total costs and minimizing the risk of obtaining higher total costs. A modified variant of a possibilistic environment based particle swarm optimization (PE-PSO) approach is used to solve the APP model. A numerical model for demonstrating the feasibility of the proposed model is also carried out. In the computational study, the considered case study data were experimented with and analyzed to evaluate the performance of the PE-PSO over both a standard genetic algorithm (GA) and a fuzzy based genetic algorithm (FBGA). The experimental results demonstrate that the PE-PSO variant provides better qualities in the aspects of its accuracy when compared to the other two algorithms.