Pengembangan Metode Ant Colony Optimization Dan Tabu Search Untuk Menyelesaikan Vehicle Routing Problem With Time Windows

Vehicle Routing Problem with Time Windows (VRPTW) merupakan salah satu dari beberapa permasalahan yang sering terjadi pada suatu sistem dalam penyaluran logistik. Sistem pengiriman barang kepada pelanggan yang terdapat pada ecommerece merupakan bagian dari permasalahan tersebut. Dimana setiap depot yang memiliki sejumlah kendaraan dengan kapasitas tertentu dapat melayani semua customer pada lokasi tertentu. Dengan ketentuan setiap customer memiliki jumlah permintaan serta batasan waktu yang berbeda-beda. Serta dalam setiap pengiriman memiliki tujuan untuk meminimalkan biaya distribusi tanpa mengabaikan batasan yang ada. VRPTW dapat diselesaikan menggunakan metode eksak, heuristik, maupun meta-heuristik. Dalam tugas akhir ini VRPTW diselesaikan menggunakan Ant Colony Optimization (ACO) yang berdasarkan pada observasi perilaku koloni semut dalam menentukan jalur untuk mencari lokasi makanan yang kemudian disempurnakan menggunakan Tabu Search (TS) dalam pengambilan keputusan. Penyelesaian VRPTW menggunakan algoritma ACOTS ini diujicobakan pada data set Solomon Problem, yang merupakan standar permasalahan internasional VRPTW. Hasil dari uji coba tersebut menunjukkan bahwa algoritma ACOTS memberikan hasil solusi yang mendekati optimal atau mendekati solusi terbaik pada Solomon Data Se

The tabu ant colony optimizer and its application in an energy market

A new ant colony optimizer, the \u27tabu ant colony optimizer\u27 (TabuACO) is introduced, tested, and applied to a contemporary problem. The TabuACO uses both attractive and repulsive pheromones to speed convergence to a solution. The dual pheromone TabuACO is benchmarked against several other solvers using the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and the Steiner tree problem. In tree-shaped puzzles, the dual pheromone TabuACO was able to demonstrate a significant improvement in performance over a conventional ACO. As the amount of connectedness in the network increased, the dual pheromone TabuACO offered less improvement in performance over the conventional ACO until it was applied to fully-interconnected mesh-shaped puzzles, where it offered no improvement. The TabuACO is then applied to implement a transactive energy market and tested with published circuit models from IEEE and EPRI. In the IEEE feeder model, the application was able to limit the sale of power through an overloaded transformer and compensate by bringing downstream power online to relieve it. In the EPRI feeder model, rapid voltage changes due to clouds passing over PV arrays caused the PV contribution to outstrip the ability of the substation to compensate. The TabuACO application was able to find a manageable limit to the photovoltaic energy that could be contributed on a cloudy day --Abstract, page iii

Algorithm for a Tabu -- Ant Colony Optimizer

A novel Ant inspired method is introduced in which both positive and negative pheromones are used to guide the ant\u27s selection process. The negative pheromone serves to influence the decision (much like a tabu search) to discourage the exploration of known bad paths. The positive pheromone serves to attract ants to known good paths (as in any conventional ACO.) Psuedocode for the new algorithm is provided. The dual-pheromone, Tabu-ACO is tested against a classic (positive pheromone only) ACO and the results compared. The Prize Collecting Steiner Tree problem is used to benchmark results