Routing Algorithm for Field Service Agents
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Solution Overview
Problem
Field service agents often inefficiently travel between service locations due to lack of comprehensive route planning, resulting in excessive travel times and costs for enterprises.
Innovation Solution
A routing algorithm that considers all tasks assigned to an agent over a longer interval, such as a workday, to minimize travel costs by determining the lowest cost route through permutation calculations and tracing back to construct a comprehensive route, while also accounting for time windows, task priorities, and dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If agents travel to the next-closest task for each task completion, then the immediate travel distance is minimized, but the overall travel time and distance for the complete route increases due to unnecessary cross-overs and double-backs
Solution Approach 1:
The routing algorithm performs preliminary comprehensive route planning at the beginning of the workday by calculating optimal sequences for all assigned tasks. This advance planning determines the complete route order considering all task locations, preventing the need for reactive next-closest task selection during execution, thereby minimizing overall travel time and eliminating unnecessary cross-overs and double-backs
Solution Approach 2:
The system dynamically adapts the routing approach based on the number of assigned tasks. When the number of tasks exceeds a threshold, it switches from comprehensive permutation calculation to approximation algorithms, allowing the routing strategy to flexibly adjust computational resources while maintaining acceptable route optimization levels
2Manufacturing precision
If comprehensive permutation calculations are performed for all task sequences, then the optimal route is identified, but the processing resources and computational time increase significantly
Solution Approach 1:
The system changes the parameter of computational approach based on the number of tasks. For small numbers of tasks below a threshold, it uses comprehensive permutation calculations with factorial complexity to ensure optimal routes. For larger numbers of tasks exceeding the threshold, it transitions to approximation algorithms with polynomial complexity, balancing route quality with processing efficiency and resource consumption
Solution Approach 2:
The routing problem is segmented into two distinct cases based on task quantity: small-scale problems solved with exhaustive permutation methods and large-scale problems solved with approximation methods. This segmentation allows the system to apply the most appropriate computational strategy for each scenario, avoiding unnecessary computational overhead while maintaining solution quality
3Loss of energy
If the routing algorithm considers all tasks over a longer interval comprehensively, then the overall travel cost is minimized, but the computational complexity and processing resources required increase
Solution Approach 1:
The algorithm dynamically adjusts its complexity based on the scale of the problem. For small numbers of tasks, it implements comprehensive permutation-based optimization to minimize travel cost. For large numbers of tasks, it employs approximation algorithms that provide satisfactory route optimization with significantly reduced computational complexity, allowing the system to handle varying workloads efficiently
4Productivity
If approximation algorithms are used for large numbers of tasks, then processing resources are conserved, but the route optimization precision may be reduced
Solution Approach 1:
The system changes the optimization parameter from exact permutation calculation to approximation based on the task quantity threshold. This parameter change allows the system to maintain high processing efficiency for large numbers of tasks while accepting a controlled reduction in route optimization precision, achieving a practical balance between computational resources and route quality
Data Source
AI summary
In accordance with the present approach, a routing algorithm may be implemented to enable an agent to receive an acceptable route between a number of assigned tasks on demand. Particularly, the algorithm may determine the acceptable route by determining a travel cost for each route between an end location and one of the assigned tasks. Then, the algorithm may add in another assigned task and determine a travel cost for each route from the end location, to a first assigned task, and then to a second assigned task. Continuing in this manner, the algorithm constructs routes from the end location and through each possible subset of the assigned tasks, while tracking a permutation of each subset having the lowest travel cost. The algorithm may therefore trace back through the lowest-cost permutations to construct a complete route that has the lowest cost.