Optimization of multi-plant capacitated lot-sizing problems in an integrated supply chain network using calibrated metaheuristic algorithms

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Optimization of multi-plant capacitated lot-sizing problems in an integrated supply chain network using calibrated metaheuristic algorithms. / Mohammadi, Maryam; Musa, Siti Nurmaya; Omar, Mohd Bin.

julkaisussa: International Journal of Operational Research, Vuosikerta 39, Nro 3, 39(3), 05.2020, s. 325-363.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Bibtex - Lataa

@article{b63ae3835ad54cdeafa9859b480a859f,
title = "Optimization of multi-plant capacitated lot-sizing problems in an integrated supply chain network using calibrated metaheuristic algorithms",
abstract = "In this paper, a mathematical model for a multi-item multi-period capacitated lot-sizing problem in an integrated supply chain network composed of multiple suppliers, plants and distribution centers is developed. The combinations of several functions such as purchasing, production, storage, backordering and transportation are considered. The objective is to simultaneously determine the optimal raw material order quantity, production and inventory levels, and the transportation amount, so that the demand can be satisfied with the lowest possible cost. Transfer decisions between plants are made when demand at a plant can be fulfilled by other production sites to cope with the under-capacity and stock-out problems of that plant. Since the proposed model is NP-hard, a genetic algorithm is used to solve the model. To validate the results, particle swarm optimization and imperialist competitive algorithm are applied to solve the model as well. The results show that genetic algorithm offers better solution compared to other algorithms.",
keywords = "capacitated lot-sizing, multi-plant, production and distribution planning, integrated supply chain, optimization, metaheuristic algorithms, genetic algorithm, particle swarm optimization, imperialist competitive algorithm",
author = "Maryam Mohammadi and Musa, {Siti Nurmaya} and Omar, {Mohd Bin}",
year = "2020",
month = "5",
language = "English",
volume = "39",
pages = "325--363",
journal = "International Journal of Operational Research",
issn = "1745-7645",
publisher = "Inderscience Enterprises Ltd",
number = "3",

}

RIS - Lataa

TY - JOUR

T1 - Optimization of multi-plant capacitated lot-sizing problems in an integrated supply chain network using calibrated metaheuristic algorithms

AU - Mohammadi, Maryam

AU - Musa, Siti Nurmaya

AU - Omar, Mohd Bin

PY - 2020/5

Y1 - 2020/5

N2 - In this paper, a mathematical model for a multi-item multi-period capacitated lot-sizing problem in an integrated supply chain network composed of multiple suppliers, plants and distribution centers is developed. The combinations of several functions such as purchasing, production, storage, backordering and transportation are considered. The objective is to simultaneously determine the optimal raw material order quantity, production and inventory levels, and the transportation amount, so that the demand can be satisfied with the lowest possible cost. Transfer decisions between plants are made when demand at a plant can be fulfilled by other production sites to cope with the under-capacity and stock-out problems of that plant. Since the proposed model is NP-hard, a genetic algorithm is used to solve the model. To validate the results, particle swarm optimization and imperialist competitive algorithm are applied to solve the model as well. The results show that genetic algorithm offers better solution compared to other algorithms.

AB - In this paper, a mathematical model for a multi-item multi-period capacitated lot-sizing problem in an integrated supply chain network composed of multiple suppliers, plants and distribution centers is developed. The combinations of several functions such as purchasing, production, storage, backordering and transportation are considered. The objective is to simultaneously determine the optimal raw material order quantity, production and inventory levels, and the transportation amount, so that the demand can be satisfied with the lowest possible cost. Transfer decisions between plants are made when demand at a plant can be fulfilled by other production sites to cope with the under-capacity and stock-out problems of that plant. Since the proposed model is NP-hard, a genetic algorithm is used to solve the model. To validate the results, particle swarm optimization and imperialist competitive algorithm are applied to solve the model as well. The results show that genetic algorithm offers better solution compared to other algorithms.

KW - capacitated lot-sizing

KW - multi-plant

KW - production and distribution planning

KW - integrated supply chain

KW - optimization

KW - metaheuristic algorithms

KW - genetic algorithm

KW - particle swarm optimization

KW - imperialist competitive algorithm

M3 - Article

VL - 39

SP - 325

EP - 363

JO - International Journal of Operational Research

JF - International Journal of Operational Research

SN - 1745-7645

IS - 3

M1 - 39(3)

ER -

ID: 27096318