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Hybrid particle swarm optimization with genetic algorithm for solving capacitated vehicle routing problem with fuzzy demand – A case study on garbage collection system
Kuo R.J.a, Zulvia F.E.a,b, Suryadi K.b
a Department of Industrial Management, National Taiwan University of Science and Technology, Taiwan
b Department of Industrial Engineering and Management, Bandung Institute of Technology, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]This study intends to propose hybrid particle swarm optimization (PSO) with genetic algorithm (GA) (HPSOGA) for solving capacitated vehicle routing problems with fuzzy demand (CVRPFD). The CVRPFD is developed by using change-constraint program model with credibility measurement. The proposed method uses the idea of a particle’s best solution and the best global solution in a PSO algorithm, then combining it with crossover and mutation of GA. This method also modifies the particle’s coding to ensure that particle always generate a new feasible solution. The proposed method is verified using some CVRPFD datasets which are modified from CVRP instances. Then, it is applied for solving garbage collection system data in Indonesia. Computational results indicate that the proposed HPSOGA outperforms single DPSO and GA for CVRPFD. © 2012 Elsevier Inc. All rights reserved.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Capacitated vehicle routing problem,Computational results,Crossover and mutation,Data sets,Feasible solution,Fuzzy demand,Garbage collection,Global solutions,Hybrid method,Hybrid Particle Swarm Optimization,Indonesia,Meta heuristics,Program models,PSO algorithms[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]CVRP with fuzzy demand,Genetic algorithm,Hybrid method,Metaheuristics,Particle swarm optimization[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1016/j.amc.2012.08.092[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]