A heuristic method for location-inventory-routing problem in a three-echelon supply chain system

•A heuristic method for LIRP where inventory decisions are made in all entities.•Inventory decisions are improved at improvement stage of the proposed heuristic.•Improving inventory decisions gives improvement of solution.•The proposed heuristic is applied in food supply chain of DKI Jakarta. Locati...

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Bibliographic Details
Published in:Computers & industrial engineering Vol. 127; pp. 875 - 886
Main Authors: Saragih, Nova Indah, Bahagia, Senator Nur, Suprayogi, Syabri, Ibnu
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2019
Subjects:
Inventory control
Location decision
Mixed integer nonlinear programming model
Multi echelons
Simulated annealing
Vehicle routing
Inventory control
Mixed integer nonlinear programming model
Multi echelons
Vehicle routing
Location decision
Simulated annealing
ISSN:0360-8352, 1879-0550
Online Access:Get full text
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Summary:•A heuristic method for LIRP where inventory decisions are made in all entities.•Inventory decisions are improved at improvement stage of the proposed heuristic.•Improving inventory decisions gives improvement of solution.•The proposed heuristic is applied in food supply chain of DKI Jakarta. Location-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%.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2018.11.026