scholarly journals System of Parametric Modelling and Assessing the Production Staff Utilisation as a Basis for Aggregate Production Planning

2021 ◽  
Vol 11 (19) ◽  
pp. 9347
Author(s):  
Martin Krajčovič ◽  
Beáta Furmannová ◽  
Patrik Grznár ◽  
Radovan Furmann ◽  
Dariusz Plinta ◽  
...  

The requirement to achieve effective solutions in the shortest possible time in the manufacturing environment is essential, and it can be solved only by effective production planning methods. The scientific problem is that traditional methods for creating and assessing the production plans are insufficient for the future and it is necessary to look for new alternatives. The planners in the framework of designing the production layouts and subsequent capacity planning of the employees are missing the information, methods and tools for making clear decisions. The production costs in general and especially the costs for the workforce create a large part of the operating costs in many manufacturing enterprises. The scientific goal of the article is to present a design of the system for parametric modelling and assessing the working utilisation of the production staff intended for reducing costs. The described solution is based on object-oriented analysis and contains a methodology of planning and controlling the production process in the industrial environment. The designed methodology was used for developing a planning module of project software and was shown through a case study in a company dealing with the production of automotive components. Effective modelling of the digital copy of the manufacturing system in the software environment is one of the most difficult and important steps for developing reliable information systems for planning and inspection in the industry. The methodology’s results in a company are that the solution can be used as a basis for the aggregate production planning that brings savings and efficiency increases. The research results can be used in any company with strictly defined working positions, working activities, and limiting conditions.

Author(s):  
Agung Mustika Rizki ◽  
Afina Lina Nurlaili

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.


2018 ◽  
Vol 11 (2) ◽  
pp. 1
Author(s):  
Ridwan Al Aziz ◽  
Himangshu Kumar Paul ◽  
Touseef Mashrurul Karim ◽  
Imtiaz Ahmed ◽  
Abdullahil Azeem

<p>Aggregate production planning has attracted the attention of researchers for quite a long time now; and the continued researches depict the significance and scope for improvement in this arena. Here, a multi-product, multi-level and multi-period model has been formulated to identify the required aggregate plan for meeting the forecast demand, by regulating production rates, inventory, workforce, various production costs, and other controllable variables. Several new contributing factors, such as costs related to material handling, raw material inventory and worker training have been included in the objective function and constraint equations to make the model more realistic. A case study has been presented for a cosmetics and toiletries manufacturer in Bangladesh. Eventually, the problem has been solved using Genetic Algorithm and Particle Swarm Optimization approach. The solution illustrates that the model can be applied in a real world scenario to enhance productivity and profitability.</p>


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