Evidence map›Paper›PMID 41298671›Full record

ArticleScientific reports2025

Optimizing sustainable hub depot locations for empty container logistics in Slovenia using particle swarm optimization.

Danijela Tuljak-Suban

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Danijela Tuljak-SubanFaculty of Maritime Studies and Transport, University of Ljubljana, Pot Pomorscakov 4, 6320, Portoroz, Europe, Slovenia. danijela.tuljak@fpp.uni-lj.si.ORCID http://orcid.org/0000-0001-7207-2976

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Containers account for 16% of all tonnage transported by sea. Therefore, the efficient repositioning of empty containers is a necessary, albeit often unprofitable, activity. This process supports both the local and global efficiency of container transport, especially given the significant imbalance in container flows from Asia to Europe. The transport sector is responsible for 21.2% of total CO2 emissions, making Empty Container Repositioning (ECR) crucial from an environmental perspective. Efficient repositioning and the identification of an optimal location for an empty container depot are crucial problems that can be effectively solved within a hub system. The aim is to find solutions that minimize the overall impact on the environment while reducing holding and travel costs. This paper proposes a robust and feasible method for determining the optimal location for an inland empty container depot, demonstrating its effectiveness on a large-scale logistics hub location problem in Slovenia. The proposed method takes into account both the economic costs and the emissions generated during the repositioning process and aims to select a hub location that minimizes these factors. The optimization is performed using a heuristic approach, Particle Swarm Optimization (PSO), which requires fewer constraints and assumptions compared to traditional linear optimization techniques. This method takes advantage of the behavior of swarms and their ability to find optimal solutions to complex problems. In contrast to classical approaches such as linear programming, which require considerable effort to formulate and solve, this method simplifies the process and efficiently identifies optimal solutions.

Indexed as

Empty container repositioningHub depotMinimizing CO2 emissionsOptimizationParticle swarm optimization

Identifiers

PMID41298671
PMCPMC12658254

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.