Two parameter tuned multi-objective evolutionary algorithms for a bi-objective vendor managed inventory model with trapezoidal fuzzy demand

Inventory levels in the supply chain with two retailers. •We present a bi-objective inventory model for a supply chain problem.•We consider trapezoidal fuzzy demand and two constraints in the modeling.•We employ MOEA to optimize a bi-objective integer nonlinear programming problem.•The parameters of...

Full description

Saved in:
Bibliographic Details
Published in:Applied soft computing Vol. 30; pp. 567 - 576
Main Authors: Sadeghi, Javad, Niaki, Seyed Taghi Akhavan
Format: Journal Article
Language:English
Published: Elsevier B.V 01.05.2015
Subjects:
NRGA
NSGA-II
Parameter tuning
Taguchi method
Trapezoidal fuzzy demand
Vendor managed inventory
NSGA-II
NRGA
Vendor managed inventory
Parameter tuning
Trapezoidal fuzzy demand
Taguchi method
ISSN:1568-4946, 1872-9681
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Inventory levels in the supply chain with two retailers. •We present a bi-objective inventory model for a supply chain problem.•We consider trapezoidal fuzzy demand and two constraints in the modeling.•We employ MOEA to optimize a bi-objective integer nonlinear programming problem.•The parameters of metaheuristics are tuned by the Taguchi method. This paper presents a bi-objective vendor managed inventory (BOVMI) model for a supply chain problem with a single vendor and multiple retailers, in which the demand is fuzzy and the vendor manages the retailers’ inventory in a central warehouse. The vendor confronts two constraints: number of orders and available budget. In this model, the fuzzy demand is formulated using trapezoidal fuzzy number (TrFN) where the centroid defuzzification method is employed to defuzzify fuzzy output functions. Minimizing both the total inventory cost and the warehouse space are the two objectives of the model. Since the proposed model is formulated into a bi-objective integer nonlinear programming (INLP) problem, the multi-objective evolutionary algorithm (MOEA) of non-dominated sorting genetic algorithm-II (NSGA-II) is developed to find Pareto front solutions. Besides, since there is no benchmark available in the literature to validate the solutions obtained, another MOEA, namely the non-dominated ranking genetic algorithms (NRGA), is developed to solve the problem as well. To improve the performances of both algorithms, their parameters are calibrated using the Taguchi method. Finally, conclusions are made and future research works are recommended.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2015.02.013