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Real operational data for the concrete delivery problem
Multiobjective Optimization REsearch Lab (MORE Lab), Department of Electrical Engineering & Computer Engineering, Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC, Canada.ORCID iD: 0000-0002-1319-513X
Multiobjective Optimization REsearch Lab (MORE Lab), Department of Electrical Engineering & Computer Engineering, Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC, Canada.
2023 (English)In: Data in Brief, E-ISSN 2352-3409, Vol. 48, article id 109189Article in journal (Refereed) Published
Abstract [en]

The data article describes a real operational dataset for the Concrete Delivery Problem (CDP). The dataset consists of 263 instances corresponding to daily orders of concrete from construction sites in Quebec, Canada. A concrete producer, i.e., a concrete-producing company that delivers concrete, provided the raw data. We cleaned the data by removing entries corresponding to non-complete orders. We processed these raw data to form instances useful for benchmarking optimization algorithms developed to solve the CDP. We also anonymized the published dataset by removing any client information and addresses corresponding to production or construction sites. The dataset is useful for researchers and practitioners studying the CDP. It can be processed to create artificial data for variations of the CDP. In its current form, the data contain information about intra-day orders. Thus, selected instances from the dataset are useful for CDP's dynamic aspect considering real-time orders.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 48, article id 109189
Keywords [en]
Combinatorial optimization, Concrete delivery problem, Operations research, Ready-mixed concrete delivery problem, Real data, Concrete mixers, Ready mixed concrete, Concrete delivery, Construction sites, Delivery problems, Operation research, Operational data
National Category
Computer Sciences Construction Management
Identifiers
URN: urn:nbn:se:hj:diva-63822DOI: 10.1016/j.dib.2023.109189ISI: 001001051000001PubMedID: 37206899Scopus ID: 2-s2.0-85156121347OAI: oai:DiVA.org:hj-63822DiVA, id: diva2:1846364
Available from: 2024-03-22 Created: 2024-03-22 Last updated: 2024-03-22Bibliographically approved

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Tzanetos, Alexandros

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