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Abstrak :
Seiring dengan perkembangan di dunia bisnis saat ini, peranan jaringan distribusi yang handal sangat berpengaruh dalam rangka menjaga eksistensi usaha. Sebagai perusahaan yang mendapatkan tugas dari pemerintah untuk melaksanakan peningkatan pemakaian LPG Domestik mengantikan (konversi) Minyak Tanah untuk rumah tangga, selain juga bisnis produk LPG dan gas lainnya yang berorientasi laba, maka perlu dilakukan peningkatkan kehandalan pada rantai suplainya serta peningkatan laba dengan mengoptimalkan jaringan distribusi LPG yang ada saat ini. Metode yang digunakan untuk mengoptimalkan jaringan distribusi adalah Algoritma Tabu Search dengan model Two Echelon - Mixed Integer Programming Problem. Dengan menggunakan metode ini diharapkan perusahaan dapat menentukan rantai suplai yang baik demi mendapatkan jaringan logistik yang optimal. ......Along with the development in today's business world, the reliable distribution network is very influential in maintaining the existence of the business. As a company that get the job of government to implement the increased use of domestic LPG to replace kerosene households, as well as LPG and gas products business more profit-oriented. It is necessary for increasing the reliability of the supply chain and increase profits by optimizing the existing LPG distribution network today. The method used to optimize the distribution network is a Tabu Search Algorithm with Two Echelon - Mixed Integer Programming Problem Model. By using this method can determine the company expected, a good supply chain in order to obtain an optimal logistics network.

Abstrak :
ABSTRAK
Biaya bahan bakar adalah komponen terbesar dalam biaya penyediaan tenaga listrik, oleh karena itu perlu dilakukan optimasi penggunaan bahan bakar sehingga biaya penyediaan tenaga listrik dapat ditekan se minimal mungkin. Pada sistem tenaga listrik yang besar seperti sistem 500 KV Jawa-Bali terdapat beberapa jenis pembangkit dengan beberapa jenis bahan bakar digunakan. Setiap unit pembangkit mempunyai karakteristik tersendiri dan bahan bakar mempunyai perbedaan harga yang beragam yaitu gas, batubara, air. Untuk memenuhi kebutuhan sistem tenaga listrik maka perlu dilakaukan pengaturan pembebanan pembangkitan sehingga memenuhi kebutuhan sistem tersebut. Pada penelitian ini dengan menggunakan adaptive cuckoo search algorithm untuk melakukan optimasi pembebanan pembangkit tenaga listrik, mendapatkan hasil biaya total bahan bakar terendah jika dibandingkan dengan metode PLN saat ini dan cuckoo search algorithm dengan tetap memenuhi kehandalan yang dipersyaratkan.

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ABSTRACT
Fuel costs are the largest component in the cost of electricity supply, therefore it is necessary to optimize fuel use so that the cost of providing electricity can be minimized as much as possible. In large power systems such as the Java-Bali 500 KV system there are several types of plants with several types of fuel used. Each generating unit has its own characteristics and fuel has a different price difference, namely gas, coal, water. To meet the needs of the electric power system, it is necessary to carry out a setting of generating charges so that it meets the needs of the system. In this study, using the adaptive cuckoo search algorithm to optimize power plant dispatch, get the lowest total fuel cost compared to the current PLN method and the cuckoo search algorithm while still meeting the required reliability.

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ABSTRACT
Zone pricing is one of the most important pricing policies that are prevalently used by very companies. This paper considers integrating zone pricing and location-routing problems for profit maximization. Hence, a node-based model is proposed as mixed integer nonlinear programming to solve the problem. In order to handle the nonlinearity elements and change the model to a mixed integer linear programming, a piecewise linearization method is employed to approximate the problem. Moreover, we introduce a new heuristic variable neighborhood search (VNS) and tabu search algorithm for large-scale problems. The proposed algorithms consist of four intra-tour and seven inter-tour operators in its local search. Finally, some test instances with 20, 200, 400, and 600 nodes are solved and the results of them confirm the efficiency of the proposed algorithms and comparative superiority of the VNS algorithm over the tabu search algorithm.

Abstrak :
Penelitian ini membahas masalah penjadwalan job shop pada suatu perusahaan. Pada sistem ini akan dihasilkan sejumlah produk dalam beberapa jenis dengan rute yang dapat berbeda satu sama lain. Penjadwalan produksi merupakan suatu permasalahan yang kompleks sehingga dibutuhkan metode yang tepat untuk mendapatkan solusi yang optimal untuk permasalah ini. Metode penelitian yang digunakan yaitu algoritma Tabu Search. Fungsi tujuan dari permasalahan ini adalah meminimumkan total penalti keterlambatan. Sebagai model job shop digunakan 7 jobs dengan total 219 komponen dan 6 mesin yang berbeda. Penjadwalan dengan metode algoritma Tabu Search memberikan solusi yang cepat. Hasil dari penjadwalan dapat menghemat 22.7% total biaya produksi, juga keterlamabatan mengalami perbaikan 31% terhadap jadwal solusi awal. Dengan demikian perusahaan bisa meningkatkan jumlah pesanan. ......This study presents a job shop scheduling problem on a specific company. This research presents job shop scheduling at a company. This system yields large amount of different products with some different manufacture processes. Production scheduling is a complex problem so that appropriated method to produces the optimal solution of it is needed. Method of this research is using Tabu Search (TS) algorithm. The objective function in this problem is to minimize total penalty of tardiness. The model used 7 jobs, 219 parts and 6 different machines. Tabu Search (TS) algorithm method is used to solve the problem, which minimizes the total penalty of tardiness. Scheduling with TS algorithm provides a quick solution. The results save 22.7% of total production costs, and improve tardiness 31% compared to old scheduling. With the proposed method the company can increase the numbers of orders.

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An assembly-type just-in-time (JIT) supply chain system is composed of a main serial supply chain and several branching serial supply chains merging to the main supply chain. Under the assumption of constant demand rate, the replenishment problem in the assembly-type JIT supply chain systems can be formulated as a mixed-integer non-linear programming problem. We conduct thorough theoretical analysis on the properties of the objective function value curve in this study. Following our theoretical analysis, we propose a search algorithm that effectively solves an optimal solution. Our numerical experiments show that the average run time of the proposed algorithm grows in a linear order of the problem size, and the proposed algorithm may serve as an efficient solution approach for the decision-maker.

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In this paper a new approach has been used for finding the optimal location for placing the Interline Power Flow Controller (IPFC). The IPFC is used to reduce the system loss and power flow in the heavily loaded lines and improve stability of the system. Here a new method, the Disparity Line Utilization Factor (DLUF) is used for determining the optimal placement of the (IPFC) to control the congestion in transmission lines. The DLUF ranks the transmission lines in terms of line congestion. The IPFC is accordingly placed in between the most congested and the least congested line connected to the same bus. The Galaxy-Based Search Algorithm (GBSA) is used for the optimal sizing of the IPFC. The results obtained by the proposed algorithm have been compared with that of the Genetic Algorithm (GA) to ascertain the effectiveness of the proposed method on the power system performance. The proposed method is tested on an IEEE 30 bus test system. It has been shown that by using the GBSA, the tuning of the IPFC further reduces congestion in the system by about 15%.

Abstrak :
Wearable sensor technology is evolving in parallel with the demand for human activity monitoring applications. According to World Health Organization (WHO), the percentage of health problems occurring in the world population, such as diabetes, heart problem, and high blood pressure rapidly increases from year-to-year. Hence, regular exercise, at least twice a week, is encouraged for everyone, especially for adults and the elderly. An accelerometer sensor is preferable, due to privacy concerns and the low cost of installation. It is embedded within smartphones to monitor the amount of physical activity performed. One of the limitations of the various classifications is to deal with the large dimension of the feature space. Practically speaking, a large amount of memory space is demanded along with high processor performance to process a large number of features. Hence, the dimension of the features is required to be minimized by selecting the most relevant feature before it is classified. In order to tackle this issue, the hybrid feature selection using Relief-f and differential evolution is proposed. The public domain activity dataset from Physical Activity for Ageing People (PAMAP2) is used in the experimentation to identify the quality of the proposed method. Our experimental results show outstanding performance to recognize different types of physical activities with a minimum number of features. Subsequently, our findings indicate that the wrist is the best sensor placement to recognize the different types of human activity. The performance of our work also been compared with several state-of-the-art of features for selection algorithms.

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Penelitian ini mengeksplorasi penerapan algoritma Ant Colony Optimization (ACO) yang dilengkapi dengan algoritma pencarian A* untuk memecahkan masalah tata letak di galangan kapal, guna meningkatkan produktivitas dan meminimalkan biaya operasional, terutama penanganan material. Menurut Dorigo et al. (1996), ACO adalah teknik probabilistik yang terinspirasi dari perilaku semut dalam mencari makan dan cocok untuk optimasi kombinatorial. Algoritma pencarian A*, seperti yang dijelaskan oleh Hart et al. (1968), adalah algoritma pencarian terbaik-pertama yang menemukan jalur biaya terendah menggunakan heuristik. Penelitian ini memodelkan galangan kapal sebagai masalah optimasi yang kompleks, dengan fokus pada biaya penanganan material. ACO digunakan untuk mengeksplorasi konfigurasi tata letak potensial, sementara algoritma pencarian A* diintegrasikan untuk meningkatkan efisiensi dan akurasi. Melalui simulasi dan analisis data dunia nyata, pendekatan gabungan ACO dan A* menunjukkan peningkatan signifikan dalam efisiensi tata letak dan pengurangan biaya penanganan material. Studi ini berkontribusi pada bidang teknik industri dengan memperkenalkan pendekatan baru untuk optimasi tata letak galangan kapal dan menunjukkan potensi algoritma yang terinspirasi bio dalam menyelesaikan masalah teknik praktis. ......This research explores the application of Ant Colony Optimization (ACO) algorithms, supplemented by A* search algorithms, to solve layout problems in shipyards to enhance productivity and minimize operational costs, especially material handling costs. According to Dorigo et al. (1996), ACO is a probabilistic technique inspired by ant foraging behavior, well-suited for combinatorial optimization problems. The A* search algorithm, as described by Hart et al. (1968), is a best-first search algorithm that finds the lowest-cost path using heuristics. This research models the shipyard as a complex optimization problem, focusing on material handling costs. ACO is used to explore potential layout configurations, while the A* search algorithm is integrated to improve efficiency and accuracy. Through simulations and real-world data analysis, the combined ACO and A* approach shows significant improvements in layout efficiency and reductions in material handling costs. This study contributes to industrial engineering by introducing a new approach to optimizing shipyard layouts and demonstrating the potential of bio-inspired algorithms in solving practical engineering problems.

Abstrak :
Indonesia merupakan salah satu negara maritim terbesar di dunia,untuk menjamin pergerakan barang dan penumpang terus berjalan pemerintah membuat kapal perintis sebagai kapal subsidi untuk pemerataan.wilayah yang menjadi perhatian adalah Regional Indonesia Timur,namun untuk diciptakan biaya operasional yang optimal agar cost dan tujuan bisa tepat sasaran perlu dilakukan optimasi,salah satu hal yang berperan dari terciptanya biaya operasional lebih rendah adalah pemilihan rute dikarenakan  cost lebih banyak dihabiskan pada bahan bakar.dalam pengoptimalan rute yang bersifat travelling salesman problem dapat digunakan penyelesaian berdasarkan metode heuristik,dua metode heuristik yang yang dapat menjadi pilihan adalah Algoritma genetika dan Tabu search dengan membangun pengoptimalan dalam kedua metode tersebut didapatkan kesimpulan keduanya bisa menjadi solusi dan perbedaan hasil kedua metode tidak berbeda jauh,keduanya  menciptakan berbagai rute dalam permasalahan harga bahan bakar optimal yang dapat dijadikan solusi pemilihan rute yang baru. ......Indonesia is one of the largest maritime countries in the world, To ensure the movement of commodity and passengers continues, the government makes pioneer ships as subsidized ships for equity. The area of concern Eastern Indonesia Region, but in order to create optimal operational costs so that costs and goals can be right on target, optimization needs to be done. One of the things that plays a role in creating lower operational costs is route selection because more costs are spent on fuel.When optimizing routes that are traveling salesman problems, solutions based on heuristic methods can be used, Two heuristic methods that can be chosen are genetic algorithms and taboo search by building optimization in the two methods, it can be concluded that both of them can be a solution and the difference in the results of the two methods is not much different, both create various routes in the optimal fuel price problem which can be used as a new route selection solution.