Optimization of the multi-objective green cyclical inventory routing problem using discrete multi-swarm PSO method

Hsin Rau; Syarif Daniel Budiman; Gede Agus Widyadana

doi:10.1016/j.tre.2018.10.006

Optimization of the multi-objective green cyclical inventory routing problem using discrete multi-swarm PSO method

Hsin Rau^*, Syarif Daniel Budiman, Gede Agus Widyadana

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

44 Citations (Scopus)

Abstract

This study proposes a multi-objective green cyclic inventory routing problem (MOGCIRP) model to capture the influence of both transportation and inventory management toward cost and environmental issues. The proposed IRP considers single and multiple cyclic tours, handling time, and capacitated fleet with weight dependent fuel consumption to model more comprehensive logistics activities. A discrete multi-swarm particle swarm optimization (PSO) and a heuristic optimization are proposed to yield the Pareto set of MOCGIRP. Results show that inventory management activities contribute considerably (17–22%) to total cost and emission rate. Additionally, multiple tours performance is consistently better than single tour method.

Original language	English
Pages (from-to)	51-75
Number of pages	25
Journal	Transportation Research Part E: Logistics and Transportation Review
Volume	120
DOIs	https://doi.org/10.1016/j.tre.2018.10.006
Publication status	Published - Dec 2018
Externally published	Yes

Keywords

Cyclic inventory routing problem
Green inventory routing problem
Logistics management
Multi-objective
Particle swarm optimization

Access to Document

10.1016/j.tre.2018.10.006

Cite this

@article{8007198aa2cf4ad8b7fe894f93507168,

title = "Optimization of the multi-objective green cyclical inventory routing problem using discrete multi-swarm PSO method",

abstract = "This study proposes a multi-objective green cyclic inventory routing problem (MOGCIRP) model to capture the influence of both transportation and inventory management toward cost and environmental issues. The proposed IRP considers single and multiple cyclic tours, handling time, and capacitated fleet with weight dependent fuel consumption to model more comprehensive logistics activities. A discrete multi-swarm particle swarm optimization (PSO) and a heuristic optimization are proposed to yield the Pareto set of MOCGIRP. Results show that inventory management activities contribute considerably (17–22%) to total cost and emission rate. Additionally, multiple tours performance is consistently better than single tour method.",

keywords = "Cyclic inventory routing problem, Green inventory routing problem, Logistics management, Multi-objective, Particle swarm optimization",

author = "Hsin Rau and Budiman, {Syarif Daniel} and Widyadana, {Gede Agus}",

note = "Publisher Copyright: {\textcopyright} 2018 Elsevier Ltd",

year = "2018",

month = dec,

doi = "10.1016/j.tre.2018.10.006",

language = "English",

volume = "120",

pages = "51--75",

journal = "Transportation Research Part E: Logistics and Transportation Review",

issn = "1366-5545",

publisher = "Elsevier Ltd.",

}

TY - JOUR

T1 - Optimization of the multi-objective green cyclical inventory routing problem using discrete multi-swarm PSO method

AU - Rau, Hsin

AU - Budiman, Syarif Daniel

AU - Widyadana, Gede Agus

PY - 2018/12

Y1 - 2018/12

N2 - This study proposes a multi-objective green cyclic inventory routing problem (MOGCIRP) model to capture the influence of both transportation and inventory management toward cost and environmental issues. The proposed IRP considers single and multiple cyclic tours, handling time, and capacitated fleet with weight dependent fuel consumption to model more comprehensive logistics activities. A discrete multi-swarm particle swarm optimization (PSO) and a heuristic optimization are proposed to yield the Pareto set of MOCGIRP. Results show that inventory management activities contribute considerably (17–22%) to total cost and emission rate. Additionally, multiple tours performance is consistently better than single tour method.

AB - This study proposes a multi-objective green cyclic inventory routing problem (MOGCIRP) model to capture the influence of both transportation and inventory management toward cost and environmental issues. The proposed IRP considers single and multiple cyclic tours, handling time, and capacitated fleet with weight dependent fuel consumption to model more comprehensive logistics activities. A discrete multi-swarm particle swarm optimization (PSO) and a heuristic optimization are proposed to yield the Pareto set of MOCGIRP. Results show that inventory management activities contribute considerably (17–22%) to total cost and emission rate. Additionally, multiple tours performance is consistently better than single tour method.

KW - Cyclic inventory routing problem

KW - Green inventory routing problem

KW - Logistics management

KW - Multi-objective

KW - Particle swarm optimization

UR - http://www.scopus.com/inward/record.url?scp=85055313419&partnerID=8YFLogxK

U2 - 10.1016/j.tre.2018.10.006

DO - 10.1016/j.tre.2018.10.006

M3 - Article

AN - SCOPUS:85055313419

SN - 1366-5545

VL - 120

SP - 51

EP - 75

JO - Transportation Research Part E: Logistics and Transportation Review

JF - Transportation Research Part E: Logistics and Transportation Review

ER -

Optimization of the multi-objective green cyclical inventory routing problem using discrete multi-swarm PSO method

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this