Fertilizer Production Planning Optimization Using Particle Swarm Optimization-Genetic Algorithm

Dinita Rahmalia, Teguh Herlambang, Thomy Eko Saputro

= http://dx.doi.org/10.20473/jisebi.5.2.120-130
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Abstract


Background: The applications of constrained optimization have been developed in many problems. One of them is production planning. Production planning is the important part for controlling the cost spent by the company.

Objective: This research identifies about production planning optimization and algorithm to solve it in approaching. Production planning model is linear programming model with constraints : production, worker, and inventory.

Methods: In this paper, we use heurisitic Particle Swarm Optimization-Genetic Algorithm (PSOGA) for solving production planning optimization. PSOGA is the algorithm combining Particle Swarm Optimization (PSO) and mutation operator of Genetic Algorithm (GA) to improve optimal solution resulted by PSO. Three simulations using three different mutation probabilies : 0, 0.01 and 0.7 are applied to PSOGA. Futhermore, some mutation probabilities in PSOGA will be simulated and percent of improvement will be computed.

Results: From the simulations, PSOGA can improve optimal solution of PSO and the position of improvement is also determined by mutation probability. The small mutation probability gives smaller chance to the particle to explore and form new solution so that the position of improvement of small mutation probability is in middle of iteration. The large mutation probability gives larger chance to the particle to explore and form new solution so that the position of improvement of large mutation probability is in early of iteration.

Conclusion: Overall, the simulations show that PSOGA can improve optimal solution resulted by PSO and therefore it can give optimal cost spent by the company for the  planning.

Keywords:

 Constrained Optimization, Genetic Algorithm, Linear Programming, Particle Swarm Optimization, Production Planning


Keywords


Linear Programming; Constrained Optimization; Production Planning; Particle Swarm Optimization; Genetic Algorithm

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References


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