This study explores the optimization of non-normal assignments utilizing the Hungarian method for both maximization and minimization. The Hungarian algorithm is a combinatorial optimization technique that efficiently solves assignment problems, ensuring optimal allocation of resources. By applying this method to non-normal assignment scenarios, we aim to demonstrate its effectiveness in enhancing productivity and reducing costs in various industries. The findings indicate that the Hungarian method can significantly improve decision-making processes in complex assignment frameworks, leading to more efficient operations.

The assignment problem discusses the problem of creating maximum sales, CV Surya Pelangi assigns its selected employees to enter the target market. CV Surya Pelangi has problems allocating its employees to the target market that has been determined. Before allocating its employees, CV Surya Pelangi has conducted training first. The income data of each employee in these markets is in (thousands) during training, therefore it is necessary to solve the employee allocation problem. The method used in this study is the Hungarian Method, with a focus on the maximization approach. The Hungarian method is known to be effective in solving assignment problems where there are similarities in the number of employees and allocations, and the goal is to optimize the allocation. In this case, maximization means finding a combination of assignments that produce the highest total profit value.

This paper discusses the methodologies for balanced transportation problems and their solutions, focusing on efficient and cost-effective distribution of goods from multiple sources to various destinations. The study highlights the importance of maintaining supply and demand balance and explores systematic approaches such as initial solution methods—North-West Corner, Least Cost, and Vogel Approximation Method—and the optimal solution technique, Stepping Stone. An illustrative case involving a company with multiple factories and markets demonstrates the application of these methods, resulting in minimized transportation costs. Additionally, the process of fertilizer allocation and cost analysis is examined, emphasizing the use of the Stepping Stone method to achieve cost minimization. The findings underscore the effectiveness of combining initial heuristics with iterative optimization to attain optimal transportation solutions, thereby enhancing logistical efficiency and reducing operational expenses.

Unbalanced transportation is one of the main challenges in logistics that can affect efficiency and operational costs. The imbalance between supply and demand can cause high transportation costs, delays in delivery, and decreased product quality. Therefore, it is necessary to analyze and develop solutions to overcome the challenges of unbalanced transportation and improve logistics efficiency. This study aims to identify the challenges and solutions of unbalanced transportation towards logistics efficiency. This study is expected to contribute to the development of unbalanced transportation solutions and improve logistics efficiency.
 

Makmur Jaya is a company engaged in the production of snacks and faces challenges in controlling raw material inventory, such as inaccuracy in the number of orders and high storage costs. This study aims to apply the Economic Order Quantity (EOQ) method to improve efficiency in inventory management. The EOQ method is used to determine the optimal number of orders that can minimize the total cost of inventory, including ordering and storage costs. The results of the analysis show that the implementation of EOQ is able to significantly reduce the total cost of inventory compared to the conventional method that has been used by the company. In addition, EOQ also provides benefits in scheduling raw material orders more efficiently and systematically. Thus, the application of the EOQ method is an effective strategy in improving the efficiency of raw material inventory control in Makmur Jaya.

Economic Order Quantity (EOQ) is one of the methods in inventory management used to determine the optimal order quantity to minimize total inventory costs, which include ordering costs and storage costs. This study aims to analyze the application of the EOQ model in managing inventory in a company. By using data on raw material usage, ordering costs, and storage costs, the results show that the application of EOQ can optimize the amount of purchases and order frequency more efficiently. The results of the analysis show that the application of the EOQ model contributes to reducing operational costs and increasing the effectiveness of stock management. Therefore, EOQ can be used as an appropriate inventory management strategy to support the efficiency and productivity of the company. Economic Order Quantity (EOQ) in controlling coffee inventory at the Suteki Medan Coffee Shop to minimize total inventory costs. The EOQ method is used to determine the optimal order quantity with the most efficient cost, as well as considering safety stock, reorder point, and total inventory cost.
 

The home industry "Eka Jaya" is a small business engaged in the production of snacks. In an effort to increase efficiency and daily profits, an optimal production planning method is needed. This study aims to maximize daily profits through the application of the simplex method in compiling the right production strategy. The simplex method is used to solve linear programming problems by maximizing the objective function in the form of profit and considering resource constraints such as raw materials, labor, and production time. The results of the study indicate that the application of the simplex method is able to provide an optimal production combination that produces maximum profit compared to the conventional method that has been used so far. Thus, this method can be used as a reference in making production decisions in the home industry "Eka Jaya".
 
 

Micro, Small, and Medium Enterprises (MSMEs) have a significant role in the economy, including MSMEs in the culinary sector such as Putri Ice Cream Business. Decision making related to optimal resource allocation is crucial to increase the efficiency and profitability of MSMEs. This study aims to apply the linear programming method with the primal simplex algorithm to minimize production costs in the case study of Putri Ice Cream MSME. The objective function is to minimize the total production cost, as well as the limitations related to the availability of main raw materials and machine production capacity. Data on main raw materials and machine production capacity were obtained from the Putri Ice Cream MSME which was the case study. The results of the study show that the application of the primal simplex method produces an optimal solution that provides recommendations for product combinations for each type of Putri Ice Cream with minimal production costs. Sensitivity analysis was also conducted to test the stability of the optimal solution to changes in capital. This study provides a practical contribution to Putri Ice Cream MSME in optimizing their resource allocation and increasing cost efficiency through a quantitative approach.