Abstract
<p><em>Determining the vehicle routing is one of the important components in existing logistics systems. It is because the vehicle route problem has some effect on transportation costs and time required in the logistics system. In determining the vehicle routes, there are some restrictions faced, such as the maximum capacity of the vehicle and a time limit in which depot or customer has a limited or spesific opening hours (time windows). This problem referred to Vehicle Routing Problem with Time Windows (VRPTW). To solve the VRPTW, this study developed a meta-heuristic method called Hybrid Restart Simulated Annealing with Variable Neighborhood Search (HRSA-VNS). HRSA-VNS algorithm is a modification of Simulated Annealing algorithm by adding a restart strategy and using the VNS algorithm scheme in the stage of finding neighborhood solutions (neighborhood search phase). Testing the performance of HRSA-VNS algorithm is done by comparing the results of the algorithm to the Best Known Solution (BKS) and the usual SA algorithm without modification. From the results obtained, it is known that the algorithm perform well enough in resolving the VRPTW case with the average differences are -2.0% with BKS from Solomon website, 1.83% with BKS from Alvarenga, and -2.2% with usual SA algorithm without any modifications.</em></p><p><em>Keywords : vehicle routing problem, time windows, simulated annealing, VNS, restart</em></p>
Highlights
Penentuan rute kendaraan merupakan salah satu komponen penting dalam sistem logistik saat ini
Windows merupakan permasalahan NP-hard (Nondeterministic Polynomial Time) sehingga metode exact optimization sulit untuk menyelesaikan kasus VRP
A simulated annealing heuristic for the capacitated location routing problem.Computers
Summary
Windows merupakan permasalahan NP-hard (Nondeterministic Polynomial Time) sehingga metode exact optimization sulit untuk menyelesaikan kasus VRP. Apabila dapat diselesaikan dengan metode eksak maka akan menghabiskan waktu yang sangat lama terutama untuk jumlah nodes yang besar (large instance). Untuk mendapatkan solusi yang relevan dengan kondisi real dan sangat dekat dengan solusi yang optimal maka digunakanlah metode heuristik atau metaheuristik (Kumar dan Panneerselvam, 2012)
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