Smart Delivery Mobile Lockers: Design, Models and Analytics
Bibliographic record
Abstract
This doctoral thesis represents pioneering research in integrating Smart Mobile Lockers with City Buses (SML-CBs) for e-commerce last-mile delivery, a novel concept rooted in the sharing economy. It explores the innovative use of underutilized urban bus capacities for parcel transportation while incorporating smart parcel lockers to facilitate self-pick-up by customers. Comprising six chapters, the thesis delineates its background, motivations, contributions, and organization in Chapter 1. Chapter 2 presents a comprehensive review of the recent literature on last-mile freight deliveries, including a bibliometric analysis, identifying gaps and opportunities for SML-CBs intervention. In Chapter 3, using survey data, we conduct empirical analytics to study Canadian consumers’ attitudes towards adopting SML-CBs, focusing on deterrents such as excessive walking distances to pick-up locations and incentives led by environmental concerns. This chapter also pinpoints demographic segments likely to be early adopters of this innovative delivery system. To address the concerns over walking distances identified in Chapter 3, Chapter 4 presents a prescriptive model and algorithms aimed at minimizing customer walking distance to self-pick-up points, considering the assignment of SML-CBs and customers. The case study results endorse the convenience of SML-CBs in terms of short walking distances. To systematically assess the sustainability benefits, a key motivator identified in Chapter 3, Chapter 5 includes analytical models for pricing and accessibility of SML-CBs. It also employs a hybrid life cycle assessment (LCA) methodology to analyze the sustainability performance of SML-CBs. It establishes system boundaries, develops pertinent LCA parameters, and illustrates substantial greenhouse gas (GHG) savings in both operational and life cycle phases when SML-CBs are utilized instead of traditional delivery trucks. The dissertation is concluded in Chapter 6, summarizing the principal contributions and suggesting avenues for future research. This comprehensive study not only provides empirical and analytical evidence supporting the feasibility and advantages of SML-CBs but also contributes to the literature on sustainable logistics and urban freight deliveries.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".