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Pumped Concrete

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Concrete in large quantities can be pumped across long distances for placing in inaccessible sites. This system comprises a hopper that receives concrete from a mixer, a pump to propel the concrete, and pipelines that facilitate its delivery.
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Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

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Real operational data for the concrete delivery problem.

Alexandros Tzanetos¹, Maude Blondin¹

¹Multiobjective Optimization REsearch Lab (MORE Lab), Department of Electrical Engineering & Computer Engineering, Faculty of Engineering, Université de Sherbrooke, 2500, boulevard de l'Université Sherbrooke, Sherbrooke, J1K 2R1, Quebec, Canada.

|May 19, 2023

Summary

This summary is machine-generated.

This study presents a real-world dataset for the Concrete Delivery Problem (CDP), featuring 263 instances of daily concrete orders. The anonymized data aids in benchmarking optimization algorithms and exploring the dynamic aspects of concrete delivery.

Keywords:

Combinatorial optimization Concrete delivery problem Operations research Ready-mixed concrete delivery problem Real data

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Area of Science:

Operations Research
Logistics Management
Data Science

Background:

The Concrete Delivery Problem (CDP) is a complex logistical challenge.
Real-world operational datasets are crucial for developing and validating optimization algorithms.
Existing datasets may not fully capture the dynamic nature of concrete delivery.

Purpose of the Study:

To introduce a novel, real-world operational dataset for the Concrete Delivery Problem (CDP).
To provide a benchmark dataset for evaluating optimization algorithms designed for CDP.
To facilitate research into the dynamic aspects of concrete delivery operations.

Main Methods:

Collected raw operational data from a concrete producer in Quebec, Canada.
Cleaned the dataset by removing incomplete orders.
Processed and anonymized the data to create 263 instances suitable for benchmarking.

Main Results:

A comprehensive dataset of 263 anonymized instances for the CDP is now available.
The dataset includes intra-day order information, capturing real-time aspects of concrete delivery.
The processed data is ready for use in benchmarking optimization algorithms.

Conclusions:

The presented dataset offers a valuable resource for researchers and practitioners in logistics and operations research.
This dataset enables the development and testing of more effective algorithms for solving the CDP.
The data's real-world nature and dynamic features can lead to improved concrete delivery logistics.