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research-article

Hybrid adaptive simplified human learning optimization algorithms for supply chain network design problem with possibility of direct shipment

Authors:

S. Molla-Alizadeh-Zavardehi,

S. NiroomandAuthors Info & Claims

Volume 96, Issue C

https://doi.org/10.1016/j.asoc.2020.106594

Published: 01 November 2020 Publication History

Abstract

A two-stage supply chain network design problem with a minimization type cost-based objective function is focused in this study. Some important assumptions such as considering different transportation modes in the stages, fixed charge transportation cost, and possibility of direct shipment between plants and customers are respected in the problem. As the problem is of NP-hard class of optimization problems, some meta-heuristic algorithms are proposed as its solution approach. In this regard, the recently proposed adaptive simplified human learning optimization (ASHLO) algorithm is used to be hybridized by the genetic algorithm (GA) and Particle swarm optimization algorithm (PSO) separately. In addition, two more meta-heuristic algorithms of gravitational search algorithm (GSA) and cuckoo search (CS) are used for more comparisons. Therefore, totally thirteen classical and hybrid meta-heuristic algorithms are proposed to solve the problem. In the extensive computational experiments of the study, 40 test problems from various sizes are generated. A typical experimental study is done to tune the parameters of the proposed algorithms. Using the results of parameter tuning step, the final experiments on the test problems are performed. The obtained results prove the superiority of the ASHLO algorithm when hybridized by the PSO.

Highlights

•

A supply chain network design problem with different transportation modes and possibility of direct shipment is studied.

•

The problem is modeled as a MILP.

•

Some novel hybrid metaheuristics are proposed based on ASHLO, GA and PSO.

•

The proposed hybrid metaheuristics yield significantly better results.

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Cited By

Yin MHuang MQian XWang DWang XLee L(2023)Fourth-party logistics network design with service time constraint under stochastic demandJournal of Intelligent Manufacturing10.1007/s10845-021-01843-734:3(1203-1227)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s10845-021-01843-7

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Hybrid adaptive simplified human learning optimization algorithms for supply chain network design problem with possibility of direct shipment

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 96, Issue C

Nov 2020

1574 pages

ISSN:1568-4946

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 November 2020

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Cited By

Yin MHuang MQian XWang DWang XLee L(2023)Fourth-party logistics network design with service time constraint under stochastic demandJournal of Intelligent Manufacturing10.1007/s10845-021-01843-734:3(1203-1227)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s10845-021-01843-7

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