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"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.

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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."

"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.

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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."

"ABSTRACTZone 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."

"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."

"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.

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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."