Optimization of simulation of the production program using genetic algorithm

Dissertation

To receive a Master's degree

Industrial Management - Industrial Production

Abstract

Currently, the increasing complexity and dynamism of production environments, the application of analytical models in their evaluation and decision-making with limitations has faced significant Therefore, the use of computer simulation as a tool that has a wide capability in formulating the above systems has been widely welcomed. Nevertheless, providing the best solution is always one of the main challenges of this field.

In this regard, the upcoming research dealt with how to solve the planning problem of the tile production line of the studied factory. The problem was determining the number of buffers allocated to each line. Using the combination of computer simulation technique and genetic optimization algorithm, the optimal number of allocated buffers was determined and the furnace stop time was minimized. First, the production system was modeled using the simulation technique, and then optimal solutions were obtained by combining it with the genetic optimization algorithm.

In short, the purpose of this research can be stated as follows:

Reducing the furnace stop time through the optimal allocation of buffers to the production lines through the combination of the simulation technique and the genetic optimization algorithm.

Key words: optimization Simulation, meta-heuristic algorithms, genetic algorithm, production planning

- Introduction

In the process of human development, more complex systems are always created and their management, monitoring and control become more difficult [1]. Complexity, ever-increasing dynamism and random nature of system operation have made it more difficult to investigate and analyze them. Production and industrial environments are no exception to this rule. From this point of view, the use of analytical models due to simplification and lack of attention to all dimensions related to the system are associated with significant limitations, and providing the best solution is always one of the main challenges in this approach, and it is not possible to determine the optimal values ??of variables through common methods [2]. To solve these problems, the manager has to provide solutions to improve the current situation. One of these solutions is simulation optimization. Fortunately, the diverse and extensive methods of simulation optimization have made it possible to analyze complex problems. The obvious advantage of these methods is that they try to find the optimal solution without searching all the available space points [2]. Simulation, known as a powerful system analysis technique, can play an important role in the efficient management of production units.

Due to the mutual effects that different parts of a system have on each other, the science of systems simulation has come to help managers and engineers to study and examine the results of these effects. In fact, one of the most efficient and advanced tools of the modern industrial and information age to analyze systems is computer simulation [1].

Optimization in mathematics and computer science is the process of choosing or finding the best member in a set of available options. Every process has the potential to be optimized and complex problems can be modeled as optimization problems in various fields such as engineering sciences, economics and management. The purpose of modeling optimization problems is to minimize time, cost and risk or to maximize profit, quality and effectiveness. Some optimization problems are complex and it is difficult to obtain optimal solutions in a reasonable time with the exact solution method. Therefore, developing solution methods in this type of problem that can obtain optimal or near-optimal solutions in a reasonable time is economically more efficient. In recent years, researchers have achieved good results in most of the complex optimization problems by implementing meta-heuristic methods [3]. The simulation model of a production system is a precise analytical tool that enables managers, designers and planners of a production unit to easily evaluate the effect of changing the parameters and variables of each of the existing subsystems on the performance of the production system and their importance. [4] in the influencing process.

In today's industrial world, simulation as a methodology for solving problems and analyzing systems is very important.

In today's industrial world, simulation is very important as a methodology for solving problems and analyzing systems. The power and ability of this technique in modeling complex industrial and service systems, the simplicity of modeling, ease of understanding on the one hand, and the advancement of hardware and software systems to solve the created models on the other hand, distinguish simulation from modeling techniques. Simulation allows its user to test with systems without which testing is impossible or impractical [5]. This factor is one of the most important competitive factors in any industry. Tile factories are no exception to this rule. Since the survival of any economic enterprise is dependent on its profitability, it should be achieved by in-depth investigation and providing an optimal production plan.

The tile production process of the studied factory consists of three main stages: preparation (pressing and glazing), tile firing (oven) and packaging, and between these stages, there are warehouses to keep the tiles during the manufacturing process. In this factory, due to the fact that there are 4 pressing and glazing lines, 3 kiln lines and 4 packaging lines, and the production of each type of tile occupies a certain proportion of the capacity of the lines, the optimal use of the intermediate storage capacity between the glazing and kiln and packaging lines, as well as the right time to change the size in the packaging lines, in addition to the possibility of producing a variety of product sizes and designs, can minimize kiln stoppages and bottlenecks[1]. production units (pressing and glazing and packaging). In addition, the arrangement of different sizes of tiles on the lines creates different capacities of production. It should be noted that the stoppages of each department have a different behavior, which affects the production capacity, and depending on the conditions, it is necessary to be able to manage the production layout. 1-3- Necessity of conducting research: Today, the complexity and random nature of the operation of production systems has made it more difficult to make decisions for management. In this regard, the lack of available resources and the significant speed of competition require the use of analytical models.

Production lines are considered as one of the most important components of any manufacturing company. The cost of the space between workstations on the one hand and the cost of downtime of the stations due to the lack of input parts on the other hand cause the determination of the capacity of the intermediate warehouses to be considered as one of the important issues. Furnaces have high downtime costs including: energy consumption to restart the furnace and negative effects of downtime on product quality. For this reason, the production planning department must operate in such a way that the maximum efficiency of the furnace can be obtained without stopping and constantly being fed. Research

1-5-1- General objectives

The general objectives of this research can be presented in several sections:

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1-5-2- Specific objectives and examples

Depending on the type of simulated system and the issues and influencing factors within this system, more details and examples can be given. presented that for this particular research, several objectives are stated as examples:

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1-6- Scope of the research

The present research was carried out in Kashi Gladis Company of Yazd. The initial stages related to objective observations and interviews with related people were collected in the first half of 2013 to complete the information required for the different stages of the research. In addition, in order to collect the necessary information related to the simulation model, the data available in the archives of the planning unit of Kashi Gladis Company (2013-2016) were used. Also, the steps of programming and creating a model for simulation were carried out in the second half of 2013.

1-7- Definition of key words

Simulation optimization[4]: The concept of "simulation optimization" in the simulation of systems using a computer is the process of finding the best input values ??among all possible states, without evaluating all possible states. [6] Meta-heuristic algorithms [5]: Meta-heuristic algorithms are a type of exact algorithms that are used to find the optimal solution.