SISTEM REKOMENDASI TEMPAT WISATA MENGGUNAKAN ALGORITMA CHEAPEST INSERTION HEURISTIC DAN NAÏVE BAYES

Bali is one of the best tourism areas in Indonesia, as evidenced in 2016 Bali received a number of awards on the TripAdvisor Travelers Choice Award in global and Asian scope. However, the Corona virus outbreak from 2019, caused the tourism sector in Bali to decline, thus a solution is needed to restore the tourism sector in Bali, where one solution is to increase cultural tourism to the maximum, as the main attraction of tourist destinations in Bali. Bali. So the author proposes a tourism recommendation system, which aims to recommend tourist attractions that are suitable for tourists, which in this recommendation system is also recommended cultural tourism destinations that are directly recommended by the community, and there is also a mapping of tourist attractions as part of a tourist recommendation system, mapping of tourist attractions public and cultural attractions. In this tourism recommendation system, using the Naïve Bayes Algorithm to recommend general tourist destinations based on the personal motivation of tourists, which is based on the attributes of age, gender, natural interest, artificial interest, cultural interest of tourists, using 200 training data consisting of 14 classes of tourist attractions. . In addition, this tourist recommendation system is equipped with recommendations for routing tourist attractions using the Cheapest Insertion Heuristic Algorithm, to arrange a list of tourist attractions. Keywords: Recommendation System, Naïve Bayes Algorithm, Cheapest Insertion Heuristic Algorithm, Personal Motivation, Place Mapping.

OPTIMALISASI PENDISTRIBUSIAN DENGAN METODE TRAVELLING SALESMAN PROBLEM UNTUK MENENTUKAN RUTE TERPENDEK DI PT XYZ

Optimalisasi rute pendistribusian menjadi salah satu target utama perusahaan dalam pendistribusian setiap produknya. Hal tersebut bertujuan untuk mendapatkan jarak pendistribusian optimal, minimasi biaya bahan bakar, dan waktu pengiriman yang lebih cepat. Travelling Salesman Problem (TSP) menjadi salah satu masalah yang melibatkan optimalisasi proses pendistribusian produk. Dalam penelitian ini, permasalahan TSP digunakan untuk mendapatkan rute pendistribusian optimal pada PT XYZ. Metode TSP yang digunakan sebagai perbandingan adalah Branch and Bound, Nearest Neighbor, Cheapest Insertion Heuristic, dan Two-Ways Exchange Improvement. Dari hasil yang didapat menggunakan WinQSB, didapat bahwa keempat metode TSP tersebut dapat meminimalkan rute pendistribusian, sehingga biaya bahan bakar juga dapat menurun. Namun rute yang memiliki jarak terpendek berasal dari metode Two-Ways Exchange Improvement dengan selisih jarak pendistribusian sebesar 16,78 KM dan biaya bahan bakar sebesar Rp. 219.410.

PERENCANAAN RUTE DISTRIBUSI PRODUK SOSIS MERAH MENGGUNAKAN NEAREST INSERTION HEURISTIC DI PT. XYZ

PT.XYZ merupakan perusahaan yang bergerak dalam bidang Industri Perunggasan Terpadu di Indonesia. Saat ini rute pengiriman yang terbentuk berdasarkan perkiraan saja tanpa adanya suatu metode untuk menghitung jarak yang ditempuh. Selain itu terdapat 15 agen yang harus dilayani dengan kapasitas angkut yang terbatas dan setiap agen menentukan jadwal pelayanan yang berbeda-beda, mengakibatkan beberapa agen bisa dilalui lebih dari sekali sehingga mengakibatkan bertambahnya jarak tempuh yang mengakibatkan meningkatnya biaya transportasi. Untuk mengatasi masalah tersebut, perusahaan membutuhkan solusi permasalahan dengan menggunakan metode Nearest Insertion Heuristic. Tujuan penelitian ini adalah menentukan rute pendistribusian agar diperoleh jarak tempuh dan ongkos transportasi yang minimum dengan mempertimbangkan kapasitas angkut kendaraan dan waktu pelayanan tertentu yang ditetapkan oleh para agen dengan menggunakan metode Nearest Insertion Heuristic. Metode Nearest Insertion Heuristic merupakan metode yang digunakan untuk evaluasi kenaikan minimum jarak antar pemasok dengan pemasok baru yang akan dikunjungi (pelanggan yang terdekat). Hasil penelitian ini adalah didapatkan rute, yaitu total jarak tempuh yang optimal sebesar 320,7 km dengan memberikan penghematan jarak 19,2% dan total waktu sebesar 17,16 jam dengan memberikan penghematan waktu 22,77 %. Biaya transportasi yang dikeluarkan sebesar Rp.1.086.321,- dengan memberikan penghematan biaya sebesar 22 %. Dengan demikian, dapat disimpulkan bahwa rute optimal metode nearest insertion heuristic lebih baik dari rute awal perusahaan.

Algoritma Simulated Annealing untuk Menentukan Rute Kendaraan Heterogen (Studi Kasus)

<p>Permasalahan transportasi dalam supply chain management sangat penting untuk dikaji karena dapat menimbulkan biaya logistik yang sangat besar. Salah satu cara untuk mengurangi biaya transportasi adalah dengan penentuan rute kendaraan atau dikenal dengan istilah vehicle routing problem. Objek yang menjadi kajian merupakan perusahaan yang bergerak pada bidang distribusi produk untuk area kota Banda Aceh dan sekitarnya. Dalam proses distribusi, perusahaan ini menggunakan dua jenis kendaraan dengan kapasitas dan biaya operasional yang berbeda sehingga permasalahan menjadi heterogeneous fleet vehicle routing problem. Penentuan rute kendaraan dalam penelitian ini dilakukan dengan tiga metode, yaitu metode analitik, algoritma insertion heuristic sebagai metode heuristik, dan algoritma simulated annealing sebagai metode metaheuristik. Berdasarkan hasil yang diperoleh dari data ujicoba, algoritma simulated annealing merupakan algoritma yang paling baik dalam menyelesaikan permasalahan. Secara rata-rata, algoritma simulated annealing dapat menghasilkan kualitas solusi yang sama dengan metode analitik, namun dengan waktu komputasi yang lebih singkat. Selain itu, algoritma simulated annealing menghasilkan kualitas solusi yang lebih baik dibandingkan algoritma insertion heuristic yang dikembangkan dalam penelitian dan dapat meningkatkkan kualitas solusi sebesar 20,18% dari penelitian sebelumnya dengan waktu komputasi 19,27 detik.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Transportation problems </em><em>in supply chain </em><em>are very important </em><em>to be discussed </em><em>because </em><em>they </em><em>can </em><em>raises</em><em> enormous logistic cost. </em><em>Route determination of the vehicles known as vehicle routing problem is the one of ways to reduce transportation cost</em><em>. </em><em>The object discussed in this study is the distribution company</em><em> </em><em>for Banda Aceh city and its surroundings</em><em>.</em><em> The company uses two types of vehicle to distribute the product for customers.</em><em> </em><em>The differences each vehicle are vehicle capacity and operational cost. To cover these differences, the problem becomes heterogenous fleet vehicle routing problem. The study uses three methods to solve the problem. Analitycal method, insertion heuristic algorithm as heuristic method and simulated annealing algorithm as metaheuristic method are the methods used. According to the results, simulated anneling algorithm produces the better solutions than two others. On average, solutions produced by simulated annealing algorithm from dataset have same quality with analitycal method, but with faster computation. Furthermore, </em><em>simulated anneling </em><em>algorithm </em><em>produces better quality of solutions than insertion heuristic algorithm both from this stu</em><em>dy and previous study. The solution improves 20,18% with computation time 19,27 seconds.</em></p><p class="Judul2"> </p><p><em><strong><br /></strong></em></p><p class="Abstrak" align="center"> </p>

Komparasi Algoritma Cheapest Insertion Heuristic (CIH) Dan Greedy Dalam Optimasi Rute Pendistribusian Barang

This study was aimed to compare algorithms that can effectively provide better solutions related to the problem of determining the shortest route in the distribution of goods. This research was a qualitative research. The object of research was the route of shipping goods of a business that is engaged in printing and convection. The algorithms compared in this study were Cheapest Insertion Heuristic (CIH) and Greedy algorithms. Both algorithms have advantages and disadvantages in finding the shortest route. From the results of the analysis using these two algorithms, the Cheapest Insertion Heuristic (CIH) and Greedy algorithm can provide almost the same optimization results. The difference was only the selection of the journey. The strength of the Greedy algorithm was that the calculation steps are simpler than the Cheapest Insertion Heuristic (CIH) algorithm. While the disadvantage of the Greedy algorithm was that it is inappropriate to find the shortest route with a relatively large number of places visited. The advantage of the Cheapest Insertion Heuristic (CIH) algorithm was that this algorithm is still stable, used for the relatively large number of places visited. While the lack of Cheapest Insertion Heuristic (CIH) algorithm was a complicated principle of calculation and was relatively longer than the Greedy algorithm.

A Variable Block Insertion Heuristic for Solving Permutation Flow Shop Scheduling Problem with Makespan Criterion

In this paper, we propose a variable block insertion heuristic (VBIH) algorithm to solve the permutation flow shop scheduling problem (PFSP). The VBIH algorithm removes a block of jobs from the current solution. It applies an insertion local search to the partial solution. Then, it inserts the block into all possible positions in the partial solution sequentially. It chooses the best one amongst those solutions from block insertion moves. Finally, again an insertion local search is applied to the complete solution. If the new solution obtained is better than the current solution, it replaces the current solution with the new one. As long as it improves, it retains the same block size. Otherwise, the block size is incremented by one and a simulated annealing-based acceptance criterion is employed to accept the new solution in order to escape from local minima. This process is repeated until the block size reaches its maximum size. To verify the computational results, mixed integer programming (MIP) and constraint programming (CP) models are developed and solved using very recent small VRF benchmark suite. Optimal solutions are found for 108 out of 240 instances. Extensive computational results on the VRF large benchmark suite show that the proposed algorithm outperforms two variants of the iterated greedy algorithm. 236 out of 240 instances of large VRF benchmark suite are further improved for the first time in this paper. Ultimately, we run Taillard’s benchmark suite and compare the algorithms. In addition to the above, three instances of Taillard’s benchmark suite are also further improved for the first time in this paper since 1993.