Example- Schedule Optimization
Summarize
Summary of Schedule Optimization
This document outlines how administrators can configure the Schedule Optimization engine in ServiceNow to effectively manage task scheduling. Key configurations allow for batch scheduling overnight, real-time scheduling throughout the day, and on-demand optimization initiated by dispatchers.
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Key Features
- Batch Scheduling: Schedule Optimization can be set to run in batches overnight, allowing for the scheduling of a higher volume of tasks.
- On-Demand Optimization: Dispatchers can trigger optimization processes manually from the Dispatcher Workspace.
- Policy Configuration: Policies can be created to maximize task assignments while minimizing travel time for agents.
- Intraday Scheduling: Optimizations can also occur during work hours, ensuring tasks are efficiently assigned as conditions change.
Key Outcomes
- Increased efficiency for agents by reducing travel time and maximizing the number of tasks completed during their shifts.
- Flexibility in scheduling that allows organizations to adapt to real-time changes and demands.
- Enhanced control for dispatchers over task allocation and scheduling through on-demand features.
This example shows three different ways admins can configure the optimization engine to schedule tasks.
Admins can configure Schedule Optimization to run overnight in batches to schedule a larger number of tasks or throughout the day at selected intervals based on events. Admins can also enable dispatchers to initiate Schedule Optimization from Dispatcher Workspace by configuring on-demand optimization.
In this example, the organization is ensuring that agents complete as many tasks as they can during their shift without spending a lot of time traveling between tasks. A policy is configured to maximize assignments and minimize travel time. On-demand optimization is enabled for the dispatchers who are assigned to this group of agents.
Admin Core Configurations for Schedule Optimization
| Field | Value |
|---|---|
| Qualifier type for Schedule Optimization | Assignment Group |
| Number of seconds used for task scheduling resolution | 1 |
| Maximum number of location points allowed in a map provider call | 300 |
| Field | Value |
|---|---|
| Name | Maximum Assignments |
| Active | true |
| Constraints | Default values |
| Overall objectives | Maximize travel time (weight 1) Maximize task assignments (weight 1) Maximize assignments to earlier shifts (weight 1) |
| Field | Value |
|---|---|
| Name | West coast config |
| Active | True |
| Travel estimate provider | Beans.ai |
| Default policy | Maximum Assignments |
| Straight line estimate config | West Coast |
| Tasks | State is one of: Pending dispatch or Scheduled |
| On Demand applicable policy | West Coast Dispatcher |
Batch Optimization Configurations
| Field | Value |
|---|---|
| Name | West Coast weekly |
| Schedule start date | 2023-12-01 |
| Run frequency | Every 7 days |
| Batch start time | 22:00 |
| Batch end time | 3:00 |
| Field | Value |
|---|---|
| Name | West Coast-Next 7 days |
| Active | True |
| Scheduling attribute configuration | West Coast config |
| Rank | 1 |
| Assignment horizon offset | 00 |
| Assignment horizon range | Days 7 |
| Optimization Batch | West Coast weekly |
| Start date | 2023-12-01 |
| Batch start time | 22:00 |
| Batch end time | 3:00 |
| Assignment group | San Diego North |
Intraday Optimization Configurations
| Field | Value |
|---|---|
| Name | West Coast |
| Active | True |
| Default scheduling attribute configuration | West Coast config |
| Default | False |
| Flow | Schedule intraday jobs (default) |
| Default processing window | Workday 9:00-5:00 |
| Assignment group | San Diego South - Enable On Demand = True San Diego North - Enable On Demand = True |
On-demand Optimization configurations
| Field | Value |
|---|---|
| On Demand applicable policy | West Coast Dispatcher |
| Field | Value |
|---|---|
| Assignment group | San Diego South - Enable On Demand = True San Diego North - Enable On Demand = True |