An Alternative Model of Aggregate Production Planning for Cement Company: Solving with Particle Swarm Optimization

2016 ◽  
Author(s):  
Sayed Islam ◽  
Md. Arifuzzaman ◽  
Nokib Parvez
Keyword(s):  
Particle Swarm Optimization ◽  
Production Planning ◽  
Alternative Model ◽  
Particle Swarm ◽  
Aggregate Production Planning ◽  
Swarm Optimization ◽  
Aggregate Production

scholarly journals Algoritme Particle Swarm Optimization (PSO) untuk Optimasi Perencanaan Produksi Agregat Multi-Site pada Industri Tekstil Rumahan

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Agung Mustika Rizki ◽  
Afina Lina Nurlaili
Keyword(s):  
Particle Swarm Optimization ◽  
Production Planning ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Efficient Solutions ◽  
Production Costs ◽  
Aggregate Production Planning ◽  
Swarm Optimization ◽  
Aggregate Production

In the industrial world, companies need to manage their production areas well. One way is to implement aggregate production planning. The goal is that the production costs incurred by the company can be controlled properly. However, production planning cannot be formulated quickly. The problem is more complicated if the company has several production locations. The difference in location also affects the production references and standards applied in each location. Based on these problems, the authors propose to apply the Particle Swarm Optimization (PSO) algorithm to solve the problem of aggregate production planning in order to obtain the optimal solution for each production location. As a result, the algorithm proposed by the author can produce optimal and efficient solutions for 6 production sites. This is evidenced by the relatively short time required compared to the previous planning by the company.

scholarly journals Fertilizer Production Planning Optimization Using Particle Swarm Optimization-Genetic Algorithm

2019 ◽  
Vol 5 (2) ◽  
pp. 120
Author(s):  
Dinita Rahmalia ◽  
Teguh Herlambang ◽  
Thomy Eko Saputro
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Production Planning ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Swarm Optimization ◽  
Mutation Probability ◽  
Planning Optimization

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

scholarly journals Linear programming embedded particle swarm optimization for solving an extended model of dynamic virtual cellular manufacturing systems

2009 ◽  
Vol 7 (01) ◽  
Author(s):  
H. Rezazadeh ◽  
M. Ghazanfari ◽  
S. J. Sadjadi ◽  
Mir.B. Aryanezhad ◽  
A. Makui
Keyword(s):  
Linear Programming ◽  
Particle Swarm Optimization ◽  
Production Planning ◽  
Manufacturing System ◽  
Cellular Manufacturing ◽  
Particle Swarm ◽  
Cellular Manufacturing System ◽  
Swarm Optimization ◽  
Integer Variables ◽  
Proposed Model

The concept of virtual cellular manufacturing system (VCMS) is finding acceptance among researchers as an extension to group technology. In fact, in order to realize benefits of cellular manufacturing system in the functional layout, the VCMS creates provisional groups of resources (machines, parts and workers) in the production planning and control system. This paper develops a mathematical model to design the VCMS under a dynamic environment with a more integrated approach where production planning, system reconfiguration and workforce requirements decisions are incorporated. The advantages of the proposed model are as follows: considering the operations sequence, alternative process plans for part types, machine timecapacity, worker time‐capacity, cross training, lot splitting, maximal cell size, balanced workload for cells and workers. An efficient linear programming embedded particle swarm optimization algorithm is used to solve the proposed model. The algorithm searches over the 0‐1 integer variables and for each 0‐1 integer solution visited; corresponding values of integer variables are determined by solving a linear programming sub‐problem using the simplex algorithm. Numerical examples show that the proposed method is efficient and effective in searching for near optimal solutions.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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