Los Movimientos, Part II: Solving Large Pickup-and-Delivery Problems with Adaptive Large Neighborhood Search
The article discusses using Adaptive Large Neighborhood Search (ALNS) to solve large-scale pickup-and-delivery routing problems that are impractical for exact MILP methods. It explains why MILP solutions become infeasible as problem size grows due to combinatorial complexity and the need for rapid, near‑optimal results. The author highlights the broader relevance of such routing challenges across industries as digital commerce expands.
- ▪The author previously solved a small version of the Los Movimientos problem with a MILP model using Pyomo and the HiGHS solver.
- ▪Scaling the problem introduces many more variables and constraints, making exact optimization too slow for practical use.
- ▪ALNS is presented as a metaheuristic that can produce high‑quality feasible solutions quickly for large instances.
- ▪The article notes that routing challenges are common in retail, transportation, food‑delivery, and e‑commerce sectors, and persist even with autonomous vehicles.
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Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/los-movimientos-part-ii-solving-large-pickup-and-delivery-problems-with-adaptive-large-neighborhood-search/ |
| Publication time | Wed, 29 Jul 2026 13:30:00 +0000 |
| Retrieval time | 2026-07-29T13:33:09.018Z |
| Last seen | 2026-07-29T13:33:09.018Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | q_WzhGRf0Pms · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Operations Research Los Movimientos, Part II: Solving Large Pickup-and-Delivery Problems with Adaptive Large Neighborhood Search Building an ALNS heuristic in Python for vehicle routing, time windows, capacity constraints, and mandatory driver breaks. Luis Fernando Pérez Armas Jul 29, 2026 28 min read Share Figure 1. The boys making a safety stop, after 2 hours of consecutive driving (Image generated by Gemini). In the first article of this series, I introduced Los Movimientos, the routing problem that I used to face in my previous job in the oil and gas industry. The operation involved two related but separable transportation problems. We had to move personnel using pickup trucks, and we also had to move tools and equipment using heavy trucks.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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