Particle swarm optimization – Genetic algorithm (PSOGA) on linear transportation problem

Rahmalia, Dinita

doi:10.1063/1.4994433

Cited by 10 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In equation (3), the number of available workers is the function of previous period workers, hired worker, and fired worker. In equation (4), the number of current inventory is the function previous period inventory, the number of units produced, and the current demand…”

Section: Mathematical Model Of Production Planningmentioning

confidence: 99%

“…Production planning model is linear integer programming model with constraints : production, worker, and inventory. Linear integer programming as optimization problem can be solved by exact method like branch and bound, cutting plane [1][2] or heuristic method like Genetic Algorithm (GA) [3], Particle Swarm Optimization (PSO) [4], Artificial Bee Colony (ABC) [5], Firefly Algorithm (FA) [6].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Genetic Algorithm and Particle Swarm Optimization on Fertilizer Production Planning Optimization

Anshori¹,

Herlambang²,

Rahmalia³

2019

Proceedings of the Proceedings of the 1st International Conference on Business, Law and Pedagogy, ICBLP 2019, 13-15 February 20

Self Cite

View full text Add to dashboard Cite

Production planning is the important part of controlling the cost spent by the company. In this research, production planning model is linear integer programming model with constraints : production, worker, and inventory. Linear integer programming as optimization problem can be solved by exact method like branch and bound, cutting plane or heuristic method. In this paper, we use heurisitic like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for solving production planning optimization in approaching. GA uses natural selection process of chromosomes while PSO is inspired by the behavior of flocks of birds, swarm of insects, or school of fish. The simulations show that both GA and PSO can find optimal solution of fertilizer production planning in approaching.

show abstract

Section: Mathematical Model Of Production Planningmentioning

confidence: 99%

mentioning

confidence: 99%

Genetic Algorithm and Particle Swarm Optimization on Fertilizer Production Planning Optimization

Anshori¹,

Herlambang²,

Rahmalia³

2019

Proceedings of the Proceedings of the 1st International Conference on Business, Law and Pedagogy, ICBLP 2019, 13-15 February 20

Self Cite

View full text Add to dashboard Cite

show abstract

“…Linear programming as optimization problem can be solved by exact method like simplex method and if the solutions are integer then branch and bound and cutting plane method are required [5], [6]. Heuristic methods like Genetic Algorithm (GA) [1], Particle Swarm Optimization (PSO) [7], [8], [9], [10], Artificial Bee Colony (ABC) [11], Firefly Algorithm (FA) [2], Simulated Annealing [12] have applied in many linear programming problem. In constrained optimization using heuristic methods, approaching of new solution must satisfy the given constraints.…”

mentioning

confidence: 99%

“…In constrained optimization using heuristic methods, approaching of new solution must satisfy the given constraints. The satisfication of constraints can be done by modified new solution [7], penalty technique [9], or goal programming [13].…”

mentioning

confidence: 99%

“…PSOGA is the algorithm combining PSO and mutation operator of GA to improve optimal solution resulted by PSO. There are many literatures where either PSO or GA can be combined with standard algorithm to improve optimal solution resulted by standard algorithm as : in the researches [7] and [8] explaining about the combining PSO and GA for the transportation problem, in research [13] explaning about the combining Ant Colony Optimization (ACO) and GA for goal programming, in research [3] explaining about the combining PSO and GA for the supply chain model, in research [17] explaining about the combining PSO and Neural Network (NN) for forecasting the data, in research [18] explaining about the combining ACO and GA for the optimization problem.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Fertilizer Production Planning Optimization Using Particle Swarm Optimization-Genetic Algorithm

Rahmalia

Herlambang

Saputro

2019

JISEBI

Self Cite

View full text Add to dashboard Cite

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

show abstract

Particle Swarm Optimization (PSO)Based Intelligent System to Optimize Fuzzy Transportation Models

Kumar,

Sharma

2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Particle swarm optimization – Genetic algorithm (PSOGA) on linear transportation problem

Cited by 10 publications

References 5 publications

Genetic Algorithm and Particle Swarm Optimization on Fertilizer Production Planning Optimization

Genetic Algorithm and Particle Swarm Optimization on Fertilizer Production Planning Optimization

Fertilizer Production Planning Optimization Using Particle Swarm Optimization-Genetic Algorithm

Particle Swarm Optimization (PSO)Based Intelligent System to Optimize Fuzzy Transportation Models

Contact Info

Product

Resources

About