50 years of Operations Research for the tactical planning of consolidation-based freight transportation
Bibliographic record
Abstract
Consolidation-based freight transportation supports a large and valuable part of world trade, over short, medium, long, and intercontinental distances, which all societies depend on and thrive on. It is carried out by carriers that supply the resources and services that meet the demands expressed by shippers. Consolidation aims at increased operational and economic efficiency, by combining cargo of different shippers into the same loading units for their complete or partial journeys. The scope of this paper is consolidation-based freight transport and the tactical planning challenges faced by carriers, who need to design a set of scheduled services that profitably and efficiently align the allocation of resources with anticipated shipping needs over a medium to long-term horizon. The tactical plan then guides day-to-day operations and informs strategic decisions. Building it represents a very complex problem, however, that requires powerful decision-support tools. Operations Research, O.R., has a long and rich history of fruitful interactions with the decision-making and planning of freight transportation, leading to significant methodological developments and delivering high-quality solutions within the necessary decision-making time frames. The paper focuses on this history, emphasizing the developments in Service Network Design, SND, the primary O.R. methodology proposed to address the challenges of tactical planning problems pertinent to consolidation-based freight transportation systems. The paper is structured to explore key dimensions in the inherent complexity of carrier tactical planning and the SND innovations developed to address them. These dimensions include the explicit consideration of temporal aspects of system elements and dynamics; integrative decision making, particularly in managing the resources carriers require to support their services and operations; and accounting for the uncertainty that directly impacts the planning of consolidation-based freight transportation systems. The paper offers insights into the main developments over the last 50 years and identifies promising research avenues. • Paper focuses on consolidation-based freight and tactical planning for carriers. • Service Network Design (SND) is presented as the main tactical planning framework. • It reviews 50 years of SND innovation in complex freight transportation systems. • It highlights complexity in planning time dynamics, integration, and uncertainty. • It outlines future research to advance tactical freight planning tools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".