Optimasi Produksi Cold Pressed Juice Menggunakan Algoritma Particle Swarm Optimization (Pso) pada Umkm Dapur Bunda Wulan Jember

Abstract

Production optimization is an essential aspect of industrial processing operations, including those of healthy beverage UMKM. UMKM Dapur Bunda Wulan Jember produces various types of cold-pressed juice and faces challenges in determining production quantities for each type to maximize profits. Limitations in raw materials, production time, storage capacity, and minimum and maximum demand limits are the main constraints in the production process. This study aims to apply the Particle Swarm Optimization (PSO) algorithm to determine a cold-pressed juice production plan that maximizes profits while satisfying all production constraints. The PSO algorithm represents each production plan as a particle that moves in a search space and is updated based on the best individual and group experiences. Calculations are performed using a Python-based programming language. The data used are primary data from interviews with UMKM and secondary data from UMKM production records, including profit per bottle, raw material requirements and availability, production time, machine capacity, cold storage capacity, and market demand limits for each product variant. The optimization model was developed with decision variables representing the production quantities of ten variants of cold-pressed juice and an objective function that maximizes total profit. The results of the study show that the PSO algorithm produces a production plan that meets all the constraints set. The optimal solution produces a total production of 1,436 bottles per period with a maximum profit of IDR 8,217,500. The production quantity of each variant is within the minimum and maximum demand limits, with a resource utilization rate close to the available capacity. Based on these results, the Particle Swarm Optimization (PSO) algorithm can be used to optimize cold-pressed juice production.