he Vehicle Routing Problem (VRP) is

he Vehicle Routing Problem (VRP) is an important management problem in the field of physical
distribution and logistics. Good vehicle routing can not only increase the profit of logistics but also make
logistics management more scientific. The Capacitate d Vehicle Routing Problem (CVRP) constrained by
the capacity of a vehicle is the extension of VRP. Our research applies a two-phase algorithm to address
CVRP. It takes the advantages of Simulated Annealing (SA) and ant colony optimization for solving the
capacitated vehicle routing problem. In the first phase of proposed algorithm, simulated annealing provides
a good initial solution for ant colony optimization. In the second phase, Iterative Local Search (ILS) method
is employed to seeking the close-to-optimal solution in local scope based on the capacity of the vehicle.
Experimental results show that the pr oposed algorithm is superior to original ant colony optimization and
simulated annealing separately reported on partial benchmark problems.

Keywords : ant colony system, capacitated vehicle routing problem, iterative local search, simulated
annealing

he Vehicle Routing Problem (VRP) is an important management problem in the field of physical 
distribution and logistics. Good vehicle routing can not only increase the profit of logistics but also make 
logistics management more scientific. The Capacitate d Vehicle Routing Problem (CVRP) constrained by 
the capacity of a vehicle is the extension of VRP. Our research applies a two-phase algorithm to address 
CVRP. It takes the advantages of Simulated Annealing (SA) and ant colony optimization for solving the 
capacitated vehicle routing problem. In the first phase of proposed algorithm, simulated annealing provides 
a good initial solution for ant colony optimization. In the second phase, Iterative Local Search (ILS) method 
is employed to seeking the close-to-optimal solution in local scope based on the capacity of the vehicle. 
Experimental results show that the pr oposed algorithm is superior to original ant colony optimization and 
simulated annealing separately reported on partial benchmark problems. 
 
Keywords : ant colony system, capacitated vehicle routing problem, iterative local search, simulated 
annealing

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he Vehicle Routing Problem (VRP) is an important management problem in the field of physical distribution and logistics. Good vehicle routing can not only increase the profit of logistics but also make logistics management more scientific. The Capacitate d Vehicle Routing Problem (CVRP) constrained by the capacity of a vehicle is the extension of VRP. Our research applies a two-phase algorithm to address CVRP. It takes the advantages of Simulated Annealing (SA) and ant colony optimization for solving the capacitated vehicle routing problem. In the first phase of proposed algorithm, simulated annealing provides a good initial solution for ant colony optimization. In the second phase, Iterative Local Search (ILS) method is employed to seeking the close-to-optimal solution in local scope based on the capacity of the vehicle. Experimental results show that the pr oposed algorithm is superior to original ant colony optimization and simulated annealing separately reported on partial benchmark problems. Keywords : ant colony system, capacitated vehicle routing problem, iterative local search, simulated annealing

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Results (Indonesian) 2:[Copy]

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ia Vehicle Routing Problem (VRP) merupakan masalah manajemen yang penting di bidang fisik
distribusi dan logistik. Baik kendaraan routing yang tidak hanya dapat meningkatkan keuntungan logistik tetapi juga membuat
manajemen logistik yang lebih ilmiah. The memberi hak d Vehicle Routing Problem (CVRP) dibatasi oleh
kapasitas kendaraan adalah perpanjangan dari VRP. Penelitian kami menerapkan algoritma dua tahap untuk mengatasi
CVRP. Dibutuhkan keuntungan dari Simulated Annealing (SA) dan optimasi koloni semut untuk memecahkan
masalah rute kendaraan berkapasitas. Pada tahap pertama dari algoritma yang diusulkan, simulasi anil memberikan
solusi awal yang baik untuk optimasi koloni semut. Pada tahap kedua, berulang Pencarian Lokal metode (ILS)
yang digunakan untuk mencari solusi dekat-ke-optimal dalam lingkup lokal berdasarkan kapasitas kendaraan.
Hasil penelitian menunjukkan bahwa pr oposed algoritma lebih unggul optimasi koloni semut asli dan
simulasi anil secara terpisah melaporkan masalah patokan parsial. Kata kunci: sistem koloni semut, kendaraan berkapasitas masalah routing, berulang pencarian lokal, simulasi annealing

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