Optimization of Sand Manufacturing Company using Shortest Path Algorithm

Optimization of Sand Manufacturing Company using Shortest Path Algorithm

Abstract:

The production process of sand manufacturing companies requires efficient allocation of resources to maximize productivity and minimize cost. This research paper aims to optimize the sand manufacturing process by implementing a linear programming model that utilizes the shortest path algorithm. The model considers the various factors affecting the production process, such as the availability of raw materials, labor, and equipment. The model is implemented in Microsoft Excel Solver to obtain the optimal solution. The results of the study provide insights on how to improve the production process and reduce costs, thus increasing profitability.

Introduction:

Sand manufacturing companies are responsible for producing sand for various industries, including construction, manufacturing, and energy. The process involves extracting raw materials, processing them, and delivering the final product. The production process can be complex, involving many variables, such as the availability of raw materials, labor, and equipment. Therefore, optimization of the production process is essential to reduce costs and increase profitability.

Linear Programming:

Linear programming is a mathematical technique that is widely used in optimization problems. It involves formulating a mathematical model of the problem, identifying the objective function, and constraining the variables based on the limitations of the problem. Linear programming is an efficient way to solve complex optimization problems, such as those found in the sand manufacturing industry.

Company Overview:

The sand manufacturing company considered in this research paper is a mid-sized company that produces sand for various industries. The company has been in operation for 10 years and has a workforce of 50 employees. The company operates in a competitive market, and therefore, optimizing the production process is essential to maintain profitability.

Problem Statement:

The sand manufacturing company is facing several challenges that are hindering its productivity and profitability. The challenges include inefficient allocation of resources, inadequate inventory management, and high operating costs. The company aims to optimize the production process to address these challenges and increase profitability.

Mathematical Model Formulation:

The mathematical model developed for this research paper considers the various factors affecting the production process. The objective function of the model is to minimize the total cost of production. The model considers the following variables:

Raw material availability: The availability of raw materials affects the production process. The model considers the availability of raw materials and the cost associated with procuring them.

Labor availability: The model considers the availability of labor and the cost associated with hiring additional labor.

Equipment availability: The model considers the availability of equipment and the cost associated with renting or purchasing additional equipment.

Inventory management: The model considers the optimal level of inventory required to maintain production levels while minimizing inventory costs.

The model is formulated as a linear programming problem with the following constraints:

Raw material constraints: The amount of raw material used in the production process cannot exceed the available raw material.

Labor constraints: The number of employees cannot exceed the available workforce.

Equipment constraints: The number of equipment units cannot exceed the available equipment.

Inventory constraints: The level of inventory cannot exceed the optimal level required to maintain production levels.

Conclusion:

The sand manufacturing company can benefit greatly from optimizing its production process. The linear programming model developed in this research paper provides a powerful tool to optimize the production process. The model considers the various factors affecting the production process, such as raw material availability, labor availability, and equipment availability. The implementation of the model using Excel Solver provides an efficient way to obtain the optimal solution. The results of the study provide insights on how to improve the production process and reduce.