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A heuristic method for location-inventory-routing problem in a three-echelon supply chain system
Saragih N.I.a,b, Bahagia S.N.a, Suprayogia, Syabri I.a
a Faculty of Industrial Technology, Bandung Institute of Technology, Bandung, Indonesia
b Faculty of Engineering, Widyatama University, Bandung, 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]© 2018 Elsevier LtdLocation-inventory-routing problem belongs to the class of NP-hard problems. It needs a heuristic method to solve the problem in large scales so it can be applied in a real system case. This paper develops a heuristic method for location-inventory-routing problem in a three-echelon supply chain system where inventory decisions are made in the three involved entities. The involved entities in the system are single supplier, multi depots, and multi retailers. The demand of the retailers is probabilistic, single product, and following a normal distribution. The heuristic method consists of two stages, which are constructive stage and improvement stage. At the improvement stage, there are three phases developed to improve solution iteratively. The phases are location phase, inventory phase, and routing phase. SA (simulated annealing) is used at the improvement stage to improve the solution. The heuristic method is evaluated using instances and the solutions are compared to the MINLP (mixed integer nonlinear programming) model. Average gap between the heuristic method and the MINLP model is 0.55% in terms of total cost. The proposed heuristic is applied in a real system case which is food supply chain system of DKI Jakarta to design a new supply chain system that can increase availability. The new design can increase the availability from 76% to 95%.[/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]Inventory decisions,Inventory routing problems,Location decisions,Mixed integer nonlinear programming models,Mixed-integer nonlinear programming,Multi-retailers,Multiechelon,Supply chain systems[/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]Inventory control,Location decision,Mixed integer nonlinear programming model,Multi echelons,Simulated annealing,Vehicle routing[/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.cie.2018.11.026[/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]