Program Dinamis Pada Penentuan Rute Kendaraan Dengan Time Windows

Penentuan rute armada merupakan salah satu permasalahan optimisasi kombinatorik yang memiliki pengaruh pada distribusi barang. Pengiriman barang cepat busuk (perishable good) seperti produk darah, dengan karakteristik jarak tempuh yang pendek memungkinkan untuk dilakukan dengan satu kendaraan. Terdapat kendala time windows pada pelanggan dan depot yang membatasi pengiriman. Masalah penentuan rute dalam penelitian ini dipandang sebagai single vehicle routing problem dengan time windows. Penelitian ini bertujuan untuk mendeskripsikan algoritma yang ditulis berdasarkan program dinamis untuk masalah penentuan rute kendaraan dengan time windows. Pada algoritma diterapkan tes yang bertujuan meningkatkan performa algoritma. Pada bagian akhir diberikan contoh penyelesaian masalah penentuan rute kendaraan dengan time windows menggunakan algoritma. Kata kunci: penentuan rute kendaraan; program dinamis; algoritma eksak Routing problem is kind of combinatoric optimization problem that has an influence on the distribution of goods. Delivery of perishable good such as blood products with short travel characteristics makes it possible to do with one vehicle. There are time-windows constraints on customer and depots that limit delivery. This research aims to describe algorithms written based on dynamic programs for the problem of determining vehicle routes with time windows. In the algorithm applied a test that aims to improve the performance of the algorithm. In the end, given an example of solving the problem of determining a vehicle route with time windows using an algorithm. Keywords: vehicle routing problem; dynamic programming; exact algorithm

Solving the Traveling Salesman Problem with Ant Colony Optimization: A Revisit and New Efficient Algorithms

Ant colony optimization (ACO) techniques are known to be efficient for combinatorial optimization. The traveling salesman problem (TSP) is the benchmark used for testing new combinatoric optimization algorithms. This paper revisits the application of ACO techniques to the TSP and discuss some general aspects of ACO that have been previously overlooked. In fact, it is observed that the solution length does not reflect exactly the quality of a particular edge belong to the solution, but it is only used for relatively evaluating whether the edge is good or bad in the process of reinforcement learning. Based on this observation, we propose two algorithms– Smoothed Max-Min Ant System and Three-Level Ant System– which not only can be easily implemented but also provide better performance, as compared to the well-known Max-Min Ant System. The performance is evaluated by numerical simulation using benchmark datasets.