Bidang kejuruteraan biasanya memerlukan rekabentuk yang paling baik untuk membawa prestasi optimum. Oleh itu, pengoptimuman memainkan peranan yang penting dalam bidang ini. Masalah penghalaan kenderaan (VRP) adalah satu masalah yang penting dalam bidang pengedaran dan logistic dari sekurang-kurangnya awal tahun 1960. Oleh itu, kajian ini adalah tentang kegunaan algoritma pengoptimuman koloni semut (ACO) untuk menyelesaikan masalah penghalaan kenderaan. Pertama, kajian ini membina model bagi masalah ini untuk diselesaikan dalam kajian ini. Seterusnya, kajian ini memberi perhatian kepada algoritma pengoptimuman koloni semut. Fungsi objektif bagi algoritma ini dikaji dan diguna dalam kajian. Keberkesanan algoritma bertambah dengan pengurangan kriteria berhenti. Parameter kawalan dikaji untuk mencari nilai terbaik bagi setiap parameter kawalan. Selepas nilai terbaik bagi setiap parameter dikenalpasti, penilaian bagi prestasi ACO bagi VRP dijalankan. Prestasi yang baik bagi algoritma menonjolkan kepentingan parameter iaitu: bilangan semut (nAnt), alpha (α), beta (β) dan rho (ρ). Alpha mewakili kepentingan relative jejak, beta mewakili kepentingan penglihatan and rho mewakili the parameter mentadbir pereputan pheromone. Laluan bagi lelaran yang berbeza dibandingkan supaya prestasi algoritma dianalisis. Set terbaik bagi parameter kawalan adalah 20 semut, α = 1, β = 1 and ρ = 0.05. Kos purata dan sisihan piawai dari 20 pelaksanaan algoritma dengan set terbaik bagi parameter kawalan juga dinilai, 1057.839 km and 25.913 km masing-masing. Akhir sekali, satu kesimpulan dibuat untuk meringkaskan pencapaian kajian ini
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Engineering field usually requires to have the best design for an optimum performance, thus optimization plays an important part in this field. The vehicle routing problem (VRP) has been an important problem in the field of distribution and logistics since at least the early 1960s. Hence, this study was about the application of ant colony optimization (ACO) algorithm to solve vehicle routing problem (VRP). Firstly, this study constructed the model of the problem to be solved through this research. The study was then focused on the Ant Colony Optimization (ACO). The objective function of the algorithm was studied and applied to VRP. The effectiveness of the algorithm was increased with the minimization of stopping criteria. The control parameters were studied to find the best value for each control parameter. After the control parameters were identified, the evaluation of the performance of ACO on VRP was made. The good performance of the algorithm reflected on the importance of its parameters, which were number of ants (nAnt), alpha (α), beta (β) and rho (ρ). Alpha represents the relative importance of trail, beta represents the importance of visibility and rho represents the parameter governing pheromone decay. The route results of different iterations were compared to analyse the performance of the algorithm. The best set of control parameters obtained is with 20 ants, α = 1, β = 1 and ρ = 0.05. The average cost and standard deviation from the 20 runtimes with best set of control parameters were also evaluated, with 1057.839 km and 25.913 km respectively. Last but not least, a conclusion was made to summarize the achievement of the study.
Solving vehicle routing problem using Ant Colony Optimization (ACO) algorithm / Wong Haw Ngie