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Introduction: Additive Manufacturing (AM) has revolutionized the production of complex geometries, offering flexibility, customization, and precision across various industries. However, optimizing multiple process parameters simultaneously to enhance AM performance remains a significant challenge. This study focuses on improving both mechanical properties and surface quality by utilizing multi-objective optimization techniques. Literature Review: The research reviews existing approaches in AM optimization, highlighting the limitations of single-objective optimization and the potential of multi-objective evolutionary algorithms (MOEAs). Previous studies demonstrate the difficulty of balancing competing objectives, such as tensile strength and surface roughness, within AM processes. Materials and Method: This study employs NSGA-II, MOEA/D, and SPEA2 algorithms to optimize AM parameters like layer thickness, build orientation, and infill density. The optimization aims to improve mechanical performance, including tensile strength and impact resistance, while reducing build time and surface roughness. The methodology integrates experimental validation with computational predictions to evaluate the effectiveness of these algorithms. Results and Discussion: The optimization process yielded Pareto-optimal solutions that balanced mechanical strength and surface quality. The results demonstrated improvements in tensile strength and surface finish without significantly increasing build time. Trade-off analysis highlighted the inherent conflicts between mechanical performance and surface quality, allowing for better decision-making in industrial applications. The study contributes to the AM industry by offering a comprehensive optimization framework for improving both efficiency and product quality.

The selection process for prospective cadets at a maritime college is routinely carried out every year. In the current system, the processing and calculation of prospective cadet scores uses Microsoft Excel tools, but this takes a long time and can lead to errors in the calculation of scores and the recapitulation of selection results. In addition, unintegrated data can lead to data duplication, resulting in inaccurate selection results that can be detrimental to prospective cadets. A fast, precise, and accurate integrated decision support system is needed to support the selection process for prospective cadets. The author conducted a study on the prospective cadet admission system at UNIMAR AMNI Semarang using the Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA) method to assist in the selection of qualified prospective cadets who meet the established criteria. With this decision support system using the Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA) method, it can be used to solve the selection problem for prospective cadets because it has a short calculation time, is simple, transparent, and has high flexibility. This system will be built using the Codeigniter Framework and a MySQL database.

When making a decision to purchase a motorcycle, it is necessary to consider several criteria. These include price, environmental impact, fuel consumption, design and technology, performance and power, availability of spare parts, after-sales service, warranty on spare parts and distance to the nearest service centre. This research aims to help consumers make informed decisions when buying motorbikes by using the Multi-Objective Optimisation by Ratio Analysis (MOORA) method. The MOORA method was selected because it can handle many criteria efficiently and simply, and produce very objective decisions. Nine criteria were used with four alternatives: motor A, motor B, motor C and motor D. The MOORA calculation results ranked motor C as the highest with a value of 1.90, motor B as the second highest with a value of 1.77, motor A as the third highest with a value of 1.25 and motor D as the fourth highest with a value of 1.20. This research proves that the MOORA method can effectively and accurately provide recommendations for making decisions about purchasing motorbikes with many criteria.

PT Tibeka Jaya Abadi merupakan perusahaan yang memproduksi kopi menjadi kopi sengah jadi yang dimana belum dijadikan bubuk kopi. PT Tibeka Jaya Abadi memiliki lebih dari satu target yang ingin dicapai yaitu Perusahaan menetapkan memaksimalkan pendapatan sebagai prioritas utama, disusul dengan menekan biaya bahan baku sebagai prioritas kedua, memaksimalkan waktu pengolahan sebagai prioritas ketiga, memaksimalkan hasil produksi prioritas keempat dan menentukan jumlah produksi yang optimal sebagai prioritas kelima, yang artinya tujuan perusahaan tidak lagi tunggal melainkan multi (lebih dari satu) dengan harapan setiap tujuan dapat dipenuhi dengan baik.  Metode Goal Programming merupakan metode yang tepat untuk dipergunakan pada masalah perusahan. Berdasarkan analisa data yang telah dilakukan oleh peneliti dengan bantuan software LINDO dalam proses pengolahan data didapat hasil yang lebih menguntungkan dibandingkan dengan metode yang selama ini dilaksanakan perusahaan. Perusahan mampu menentukan produksi yang optimal sesuai dengan permintan pasar sebesar 99.530 kg. Begitu pula waktu pengolahan dapat dimaksimalkan selama 120 jam. Dan sebelum mengunakan metode Goal Programming pendapatan Perusahaan sebesar Rp. 7.512.831.000. Dan setelah menggunakan metode Goal Programming maka pendapatan perusahaan adalah sebesar Rp. 8.324.780.000. Sehingga tujuan perusahaan yaitu meningkatkan optimal produksi tercapai.