SLA-to-JSON Mapping for Constrained Logistics Route Optimization
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Solution Overview
Problem
Optimizing logistics and service-level agreements across various use cases, such as e-commerce and mailing services, is costly and effort-intensive due to the need for customized algorithms, which can be cumbersome and reduce the return on resource investment.
Innovation Solution
A computerized method for automatically implementing Service-level agreement (SLA) to JavaScript Object Notation (JSON) mapping in an automated routing system, using parameterization with a heuristic evaluator to generate metrics and a JSON tree, which is then tuned for use in route planning algorithms, allowing for flexible and efficient route optimization across different use cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If customized algorithms are created for each use case, then the solution can address specific logistics problems, but the cost and effort increase significantly
Solution Approach 1:
The patent creates a universal routing system that can handle multiple use cases (e-commerce, mailing services, direct visits) through a single configurable algorithm. The system uses parameterization to adapt to different scenarios without requiring separate customized algorithms for each use case, thereby reducing complexity while maintaining versatility.
Solution Approach 2:
The system employs parameterization where different use cases are represented as sets of parameters (pickup/dropoff locations, constraints, objectives) that can be configured without changing the underlying algorithm. This allows the same routing algorithm to solve different logistics problems by simply changing its input parameters.
2Reliability
If customized algorithms are created for each use case, then the specific logistics problem can be solved, but the return on resource investment decreases
Solution Approach 1:
By creating a single universal routing system that can handle multiple use cases, the patent eliminates the need to invest resources in developing and maintaining separate customized algorithms for each scenario. This reduces resource investment while maintaining solution effectiveness through configurable parameters.
Solution Approach 2:
The system uses parameterized configurations that can be copied and adapted for different use cases rather than creating entirely new algorithms. This allows rapid deployment across multiple scenarios with minimal additional resource investment.
3Device complexity
If a single routing system is used for multiple use cases, then resource investment is optimized, but the system must handle variable constraints and objectives
Solution Approach 1:
The patent handles variable constraints and objectives by representing them as configurable parameters within the routing system. Different use cases (e-commerce with multiple dropoffs, mailing services with multiple pickups, direct visits) are modeled as different parameter configurations rather than requiring different system structures.
Solution Approach 2:
The system segments the routing problem into distinct components (pickup locations, dropoff locations, constraints, objectives) that can be independently configured. This segmentation allows the single system to handle diverse use cases by combining different parameter values while maintaining a unified structure.
Data Source
AI summary
In one aspect, a computerized method for automatically implementing Service-level agreement (SLA) to JavaScript Object Notation (JSON) mapping engine in an automated routing system includes the step of providing an SLA agreement in a digital format. The method includes the step of implementing a parameterization of the SLA agreement with a heuristic evaluator. The method includes the step of, based on the parameterization, generating a set of metrics. The method includes the step of from the set of metric, generating a JSON tree. The method includes the step of tuning the JSON tree with a weight of a relevant metric of the set of metrics, wherein the set of metrics sets an allowed range of parameters to take into consideration by a route planning algorithm. The method includes the step of providing the tuned configuration of the JSON tree to an automated routing engine during routing for use in a set of route decision making operations.