Route Optimization Service for Field Agents

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Solution Overview

Problem

Inefficient task assignment in cloud computing environments leads to excessive travel times and unsuitable agent allocation due to limited routing criteria and oversight, resulting in suboptimal resource allocation and increased costs.

Innovation Solution

A client-configurable algorithm that considers various inputs to optimize travel routes for field service agents, including minimizing travel time, labor costs, and adhering to service-level agreements, while utilizing machine learning to adjust estimates based on actual task completion times for improved scheduling.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If tasks are assigned to agents using traditional cloud computing resource allocation methods, then resource allocation flexibility is improved, but travel time and operational efficiency deteriorate due to lack of routing optimization

Engineering Contradiction:

Improveresource allocation flexibilityVSAvoidtravel time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent introduces a route optimization service as an intermediary component between the cloud computing environment and field service agents. This service receives task assignments from the cloud environment, computes optimized travel routes considering multiple criteria (traffic conditions, agent locations, task priorities), and returns optimized schedules to agents. This intermediary layer resolves the contradiction by maintaining the flexibility of cloud-based allocation while adding routing optimization to reduce travel time.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Device complexity

If simple task assignment methods are used, then system complexity is reduced, but task assignment efficiency and cost optimization deteriorate

Engineering Contradiction:

Improvesystem complexityVSAvoidtask assignment efficiency

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The patent segments the task assignment system into distinct functional modules: a cloud computing environment for task generation and initial allocation, a route optimization service for computing optimized routes, and agent mobile devices for receiving and executing assignments. This segmentation allows each component to specialize in specific functions, improving overall task assignment efficiency while keeping individual component complexities manageable.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The route optimization service acts as an intermediary that bridges the simple cloud assignment system and the complex requirements for optimized routing. It receives basic task assignments, processes them through optimization algorithms considering multiple criteria (traffic, agent locations, task priorities), and returns optimized schedules. This intermediary adds intelligence and optimization capabilities without requiring complete system redesign.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Loss of energy

If travel route optimization is implemented, then travel time and labor costs are reduced, but system complexity and computational requirements increase

Engineering Contradiction:

Improvelabor costsVSAvoidsystem complexity

Core Design Contradiction:

Loss of energyVSDevice complexity

Solution Approach 1:

The route optimization service serves as a dedicated intermediary component that handles all computational complexity for route optimization. It receives task assignments from the cloud environment, performs sophisticated route computations considering multiple criteria (traffic conditions, agent locations, task priorities, service level agreements), and returns optimized schedules to agents. This segmentation isolates complexity within the optimization service while keeping the cloud environment and agent devices relatively simple.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Ease of operation

If traditional task assignment without routing optimization is used, then system simplicity is maintained, but agent utilization and customer satisfaction deteriorate

Engineering Contradiction:

Improvesystem simplicityVSAvoidagent utilization

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The route optimization service acts as an intermediary that enhances agent utilization and customer satisfaction while preserving system simplicity. It receives task assignments from the cloud environment, computes optimized routes that maximize agent utilization and meet service level agreements, and returns optimized schedules to agents. This intermediary adds value through optimization without requiring fundamental changes to the underlying cloud computing infrastructure or agent workflows.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS10817809B2Systems and methods for customizable route optimization

Publication Date: 2020.10.27 SERVICENOW INC

AI summary

Embodiments of the present disclosure are directed to providing, via a client instance hosted by an enterprise management data-center, an optimized travel route, including task assignment and scheduling, based at least on user configured criteria. Particularly, the client instance may execute an algorithm, trained via machine learning, to determine the optimized travel route in view of the user configured criteria.