Cold Chain Route Planning With Real-Time Container Temperature Prediction
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Solution Overview
Problem
Existing computer-implemented systems for planning and monitoring temperature-controlled transports of temperature-sensitive goods fail to account for individual parameters such as transport times, ambient temperatures, and thermodynamic properties of cooling containers, leading to temperature deviations and inefficiencies, and lack real-time monitoring capabilities.
Innovation Solution
A method that plans and monitors cold chain transport by assigning route sections to specific transport means, using estimated and actual data to calculate and update the internal temperature progression, incorporating variance and probability distributions, and allowing real-time adjustments to maintain temperature ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing computer-implemented systems use mean value or worst case assumption for transport parameters, then the system complexity is reduced, but the temperature monitoring accuracy and reliability deteriorate
Solution Approach 1:
The transport route is divided into multiple route sections, each with its own set of parameters (ambient temperature, transport duration, means of transport). This segmentation allows the system to process and monitor temperature progression through discrete segments rather than treating the entire route as a single complex unit, improving accuracy while managing complexity through structured data organization.
Solution Approach 2:
The system dynamically updates the expected temperature progression by incorporating actual route section data (actual ambient temperature, actual transport duration) as the transport progresses. This dynamic adjustment allows the system to adapt to real conditions rather than relying solely on static mean values or worst-case scenarios, thereby improving reliability without requiring overly complex predictive models.
2Reliability
If real-time monitoring and prediction of temperature excursions is implemented, then the reliability of temperature control is improved, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary calculations of expected temperature progression for different route section combinations before the actual transport begins. By pre-calculating and storing these expected progressions, the system can quickly compare actual temperature data against pre-computed benchmarks during transport, reducing real-time processing requirements while maintaining reliable temperature control monitoring.
Solution Approach 2:
The system continuously compares actual temperature data from route sections with the expected temperature progression and updates the monitoring accordingly. This feedback mechanism allows the system to detect temperature excursions early by comparing actual measurements against predicted values, improving reliability through continuous verification without requiring excessive computational resources for complex real-time predictions.
3Measurement precision
If individual parameters such as transport times, ambient temperatures and thermodynamic properties are considered, then the temperature prediction accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The system processes individual parameters (transport time, ambient temperature, thermodynamic properties) separately for each route section rather than attempting to process all parameters simultaneously across the entire transport route. This segmentation approach allows the system to maintain high prediction accuracy by considering individual parameter variations while managing data processing complexity through structured, section-by-section analysis.
4Adaptability or versatility
If multiple route section combinations are evaluated for optimal transport planning, then the adaptability of the system is improved, but the computational time and processing requirements increase
Solution Approach 1:
The system performs preliminary evaluation of multiple route section combinations and pre-determines optimal transport plans before actual delivery operations. By pre-calculating and storing expected temperature progressions for different combinations, the system can quickly adapt to changing conditions during transport without requiring extensive real-time computational analysis, thus maintaining high adaptability while reducing operational computational time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate prediction and real-time monitoring of temperature excursions, optimizing transport routes and container choices to ensure temperature-sensitive goods remain within predefined ranges while considering cost and CO2-balance.
Implementation Method 1
Passive cooling containers usually work with latent heat storage devices. These are pre-cooled and undergo a phase change during transport, whereby the heat penetrating from the outside is absorbed.
Implementation Method 2
These are pre-cooled and undergo a phase change during transport, whereby the heat penetrating from the outside is absorbed.
Implementation Method 3
Active cooling containers work with a cooling unit that relies on a continuous power supply to maintain the temperature.
Data Source
AI summary
A computer-implemented method for planning and monitoring a cold chain when transporting temperature-sensitive goods in a temperature-controlled transport container from a starting location to a destination. The method includes creating at least one combination of route sections for at least one transport route from the starting location to the destination; and calculating and displaying an expected course of the internal temperature of the transport container for the at least one combination of route sections based on route section-specific estimated data and container-specific data, the expected course of the internal temperature being within a predefined temperature range. The method also includes recording route section-specific actual data during transport; and during transport, updating the calculation of the expected course of the internal temperature of the transport container for a remaining part of the transport route taking into account the route section-specific actual data.