Consolidation centers in city logistics: A cooperative approach based on the location routing problem
Bibliographic record
Abstract
In the context of city logistics, freight transportation is one of the prominent causes of traffic congestion, high levels of pollution, and safety concerns. To decrease the negative impact of these issues, different methods have been traditionally implemented. On the one hand, the location of urban consolidation Centers (UCCs) near a city can be used to consolidate freight delivery services. Therefore, the number of trucks moving in urban areas can be reduced. On the other hand, Horizontal Cooperation can also help to reduce environmental impact while increasing service level. This paper combines both strategies, that is, we deal with the location of UCCs and, simultaneously, we analyze different scenarios where the players of different supply chain processes exhibit various levels of cooperation. Thus, different levels of cooperations regarding routing and UCCs-location decisions are considered in the following scenarios: (a) non-cooperative case, in which all decisions are decentralized (i.e., each enterprise solves its own vehicle routing problem); (b) low-cooperative case, where depot capacities are shared but the customers are still being served by each company's fleet of vehicles; (c) semicooperative case, based on centralized route planning decisions (i.e. facilities and fleets are shared among participating enterprises); and (d) fully cooperative scenario, where the routing plans and facility-location decisions are taken by consensus amongst all the participants. In order to estimate the benefits of both strategies, we propose a flexible metaheuristic algorithm to deal with the combined location and routing problem under the different cooperative scenarios. Our results show impressive benefits of the proposed approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".