Publication Search

Micro, Small, and Medium Enterprises (MSMEs) are strategic sectors in the Indonesian economy, but often face challenges in efficient and data-driven production management. This article highlights the urgency of the Real Work Lecture (KKN) program as a form of student service in assisting MSMEs, especially in optimizing the production of snacks typical of Tebing Syahbandar. This research aims to optimize the production output of the Untir-untir Titik Factory with an Integer Linear Programming (ILP) approach using the Branch and Bound algorithm. Primary data is obtained through interviews and production documentation, including product type, raw material needs, operational costs, selling prices, and profit margins. The initial analysis was carried out using the simplex method using POM QM software to obtain a linear solution, which was then refined with the Branch and Bound algorithm so that the results were in the form of integers. The results of the study showed that the optimal solution was achieved by producing 25 bales of kolong-kounder and not producing other types of snacks, resulting in a profit of Rp1,650,000 per day. These findings show that the ILP approach with Branch and Bound is able to significantly increase the efficiency and profitability of MSMEs. In addition, this method can be used as a basis for quantitative-based production decision-making. This research also emphasizes the strategic role of KKN in technology transfer and real solution-based assistance for MSME actors in the region, thereby supporting the sustainable strengthening of the local economy.

This study aims to optimize the cake production profit at Cake by Cece using the Branch and Bound algorithm. The data used include raw material requirements per batch, daily raw material availability, and selling prices for three types of cakes: Cookies, Brownies, and Cinnamon Roll. The optimization model is formulated as an Integer Linear Programming problem with the objective of maximizing total daily profit. The model is solved using the simplex method followed by the Branch and Bound algorithm to obtain valid integer solutions. The results indicate that the optimal production combination is 2 batches of Cookies, 2 batches of Brownies, and 3 batches of Cinnamon Roll, yielding a maximum profit of IDR 233,000 per day. This solution satisfies all raw material constraints and is feasible for daily operational implementation. This study provides quantitative recommendations to support production decision-making in culinary sector MSMEs.

This research explores the effectiveness of heuristic techniques for solving combinatorial optimization problems, with a particular focus on the Traveling Salesman Problem (TSP). Combinatorial optimization is a critical area of study, especially in fields like computer science, engineering, and economics, where finding optimal solutions from a finite set of possibilities is crucial. However, the NP-hard nature of many combinatorial problems, such as the TSP, makes traditional exact methods like Branch-and-Bound and Dynamic Programming computationally expensive and inefficient for larger problem sizes. The primary objective of this research is to evaluate the performance of heuristic methods, including Simulated Annealing (SA), Genetic Algorithms (GA), and Iterative Computation techniques, such as Tabu Search (TS) and Particle Swarm Optimization (PSO). These methods are tested for their ability to provide approximate solutions efficiently. The findings reveal that while ACO provided the best solution quality, it had the longest runtime. TS was the fastest, though with slightly lower solution quality. SA and GA demonstrated a balance between solution quality and computational efficiency, but their performance heavily depended on parameter tuning. The hybridization of SA and GA showed potential for improving solution quality but introduced additional complexity. The research concludes that heuristic methods, especially when combined, offer viable solutions for large-scale combinatorial optimization problems, though the trade-off between solution quality and computational time must be considered when selecting an algorithm.

Optimizing distribution routes is an important step for MSMEs in increasing operational efficiency and customer satisfaction. This research was conducted on Bolen Crispy Mak Tin MSMEs which face distribution challenges with routes that are not yet optimal, causing increased transportation costs and the risk of decreasing product quality. This research uses the Branch and Bound and Nearest Neighbor algorithms to solve the Traveling Salesman Problem (TSP) problem in determining efficient distribution routes. The results of data processing are optimal routes that have the minimum distance with a total distance of 282.5 KM with route P-1-2-7-4-5-6-3-0 for the branch and Bound algorithm and 239 km with route P- 2-3-4-5-6-7-1-P for Nearest Neighbor This result is more optimal when compared to the previous route, namely P-1-2-3-4-5-6-7-P with a distance of 291 km analysis shows that Method Nearest Neighbor is able to provide an optimal solution by minimizing travel distance and distribution costs, while the Branch and Bound algorithm also provides an optimal solution but is less efficient. and distribution cost efficiency from Rp. 570,320.9 to 565,511.36 or 0.84% ​​more savings for the Branch and Bound Algorithm and 540,795.45 or 5.18% more savings for Nearest Neighbor

Combinatorial optimization is a fundamental area in operations research and computer science, focusing on identifying optimal solutions from a finite set of possibilities. This study explores the integration of branch and bound methods with heuristic algorithms to address optimization problems in graph theory and discrete mathematics. Python was employed for algorithm implementation due to its flexibility and comprehensive computational libraries, enabling efficient data analysis and visualization. Several benchmark problems were examined, including the Traveling Salesman Problem (TSP), Minimum Spanning Tree (MST), and Graph Coloring. Simulations were conducted using datasets of varying sizes (small, medium, and large) to evaluate performance across different scales. The results demonstrate that the hybrid approach achieves a balance between solution quality and computational efficiency, outperforming brute-force methods in terms of speed while maintaining near-optimal accuracy. Tabulated results and graphical comparisons highlight the reduction in computation time and improved scalability of the proposed method. The findings suggest that combining systematic search strategies with heuristics offers a practical and effective framework for solving complex combinatorial optimization problems. Recommendations for future research include testing scalability with larger datasets, incorporating advanced metaheuristics, and applying the approach to real-world domains such as logistics and network design.