© a.p.solve Limited 2003 DEPARTMENT PLACEHOLDER Dynamic Scheduling in Mobile Workforce Management Ralf Keuthen PLANET Information Day, Bilbao, 2003

© a.p.solve Limited 2003

DEPARTMENTPLACEHOLDER

Dynamic Scheduling in Mobile Workforce Management

Ralf KeuthenPLANET Information Day, Bilbao, 2003



Contents

Automated Mobile Workforce Management The Workforce Scheduling Problem TASKFORCE System Overview Issues



Mobile Workforce Management

a.p.solve -- A Short History Involved in mobile workforce management since

1987. Produced two major Work Management Systems

which have evolved into the TASKFORCE products we currently market.

a.p.solve (100+ employees) was spun out via the British Telecom’s Brightstar business incubator initiative in April 2003.

a.p.solve’s planning and scheduling products primarily support the management of mobile workers via Personal Digital Assistants and mobile telephony.



Scope

Large telecommunication, cable, utility and fix & repair companies typically maintain a fieldforce of 100s - 10,000s of technicians

The fieldforce supplies provision of service, repair and maintenance tasks on a daily basis (between 1000s - 100,000s of tasks/day)

Customers Residential, Business (provision, repair) Company itself (maintenance, repair)



Example: Mobile Workforce at British Telecom

BT Customer Access: a.p.solve’s TASKFORCE products currently schedule

BT’s workforce of Service Technicians. ~25,000 field technicians ~150,000 tasks every day across the United Kingdom. A high quality service at low operational cost needs to

be delivered.



Work Management Organisation

Domains: Geographical

partition of the work area

into autonomous domains

Domains controlled by automated work management system

Supervised by a human controller



Organisation: How it works

• Call Centre

• Network Service

TASKFORCE

• Handheld terminal

• Laptop

• Mobile

Customer Service

Work allocation and visualisation

Dispatch work to technicians



Organisation: How it works Customer Service:

take customer calls arrange appointments

TASKFORCE: provide customer service with a selection of

appointment slots Allocate work to technicians dispatch work to technicians

Technicians receive work details travel to and carry out work report back when work is finished



Workforce Management: From a Scheduling Point of View



Scheduling Model

Resources: Technicians Vehicles and other equipment

Activities: provision, repair and maintenance work

Constraints: time windows, access restrictions precedence constraints co-op, assists, etc.



Main Objectives

right man - right time - right place - right costs Maximise productivity

number of tasks scheduled most efficient resource for each task

Ensure a high quality of service compliance with agreed appointments & due dates work importance

- Minimise costs travel times waiting/idle times overtime



Other Objectives

Workforce satisfaction task preferences preferred working areas

Business rules every technician gets a job avoid the splitting of tasks over breaks (if possible)

Avoid disturbances robust schedules flexible schedule

Some of these contradict one another



Issues

Dynamics/Uncertainties/Complexities of problem

Scale

The need for a totally automated, online, system.



Dynamics

Tasks

The company and its customers can

request cancel amend tasks (at all time!!)

Resources

Availability subject to last minute changes personal absence, sick leave, etc changes to task completion times vehicle breakdown



Uncertainties Duration of tasks

Uncertain due to exact scale of work often unknown before a

technician arrives on site varying technician skill levels

Travel times

Uncertain due to weather traffic conditions

Business objectives

Resource manager can change business objectives



Complexities

Complex mixture of tasks: different execution target times (appointment/commitments) different task priorities: low - high priority tasks Wide range in the duration of tasks: 8 mins - several days

Inter task dependencies can be complex coops, assists tasks pre-installation tasks stock point visits, etc

Site access restrictions security access business opening times road closure, etc



Complexities

Geographical complexities diverse (London vs East Wales, etc) Preferred working areas

Skills heterogeneous workforce diverse skill set

Work type and work skill imbalances some geographical areas can be under resourced certain skills can be under resourced

Business Rules



Scale

Scale of individual problems domain dependent

Technicians: 10s to 100s of technicians

Tasks: 100s to 1000s of tasks

Scheduler needs to cope efficiently with all domains



Issues

No realistic forecasting possible! Assumed ‘static’ environment?

Optimised schedules quickly become sub optimal or even infeasible

What is optimality in an dynamic environment? First thing in the morning everything changes !!

(sick leave, new tasks, etc) Building robust/flexible schedules?

Limited applicability Radical changes to the current schedule are

possible and may even be desired



Scheduling Opportunities:

Impact of Personal Digital Assistants on Scheduling

Practice: Mobile phones, laptops, handheld terminals, the Internet, etc

allows to dispatch tasks to mobile workers in real time tasks are (usually) dispatched one by one Technician can drive to first task of the day straight from home

and drive back home after last job is finished. No need to visit a control site.

Scheduling impact: allows to adjust the schedule to the changed environment allows to correct (some) scheduling decisions made earlier

However, the time available to react to changes is very limited



TASKFORCE System Overview



Needed Automation

Automated data flow from order source systems to job dispatch.

Schedule revision must be automatic and robust. On line Dispatcher must be able to cope with corrupted

schedules. The real-line monitoring of the location of mobile

technicians and their expected completion times is important.



TASKFORCE

Developed by BT and employed since 1997.

TASKFORCE supports: Resource Management Operations Management Schedule/Jeopardy Management Progress Management Scheduling & Dispatching



System Overview



Schedule Manager

Planer that transforms customer orders (service requests) into scheduling variables with their associated constraints

Identifies tasks in jeopardy of becoming tardy Pre-processes tasks & resource information according

to business rules Calls scheduling tools to create/maintain executable

schedule



Scheduling Needs

Batch Scheduling Schedule revision Appointment booking support Interrupt Scheduling Route Optimisation Schedule Dispatcher What-If Scheduling Schedule Visualisation & Manual scheduling support



Architecture Overview

Visualiser

DS

Pre-scheduler

Optimiser

Interrupt Scheduler

Dispatcher

Intelligent Appointer

Schedule Manager



Batch Scheduler

Runs before start of working day to construct high quality schedule Build provisional start-of-day schedule Schedule construction & route optimisation

Techniques: Chronological backtracking based Constructive

Algorithm assign important and hard to schedule tasks

Local Search based optimisation supported by a constraint net to ensure feasibility

Stochastic Local Search: Simulated Annealing



Dynamic Scheduler

Run while schedule is being executed to maintain a high quality schedule

Responsibilities: Absorb schedule changes and most up to date

information Rebuild, repair, update & re-optimise provisional

schedule How it works:

Perform frequent short batch runs to rebuild a feasible schedule

Pass provisional schedule to Schedule Manager



Dynamic Scheduler

Pre-scheduling: Reload and try to rebuild old schedule

Tree Search assigning remaining important and hard to schedule tasks

Optimisation: Low Temperature Simulated Annealing.



Intelligent Appointer

Controller/call centre support tool Heuristic based

find a set of feasible appointment slots based on the current schedule

suggest feasible appointment slots to human controller

controller books appointment slot and associates time windows with the new task

task is sent to schedule manager



Interrupt Scheduler

Automatic Schedule Revision: Reallocation algorithm to support situations where

important work would otherwise fail. The system can’t find an available resource for high

priority work at specified times Interrupt scheduler identifies a sequence of

reallocations to free a technician to perform the work



Automatic Dispatcher Online and event triggered System Responds to requests for work from field force in real-

time, making alterations to the planned tour as required.

Supports the management of uncertainties & last second changes to the schedule associated with: travel time & task duration arrival of new high priority work and cancellation of

already scheduled work



Dispatching Algorithms

Rule based system. If Field Technician request work then the Dispatcher

identifies a task for the technician to service. Rule Examples:

Find work on site for technicians rather than travel to new locations

Give preference to tasks getting close to their deadlines

Keep technicians from travelling outside their preferred working area

etc, etcThis may result in the need to repair a damaged schedule



What-if and Manual Scheduling

Are off-line schedulers that support work controllers by providing the visual and analysis tools to try out different scheduling parameters and allocations to discover improvement opportunities without risk.

Changes to scheduling parameters and data can be applied to current or archived data and the resulting schedule can be examined in an off-line environment.

Changes can then be applied to live sites.



Schedule Visualisation Tools

Compress schedule information and represent it

in a way that can easily be captured by the user Provides the human controller with:

statistics tour task tables Gantt chart map tour representation



Schedule Visualisation: Gantt Chart



Schedule Visualisation: Map



Case Study: British Telecom

Work Stats: ~ 20,000 engineers ~ 150,000 tasks each day across the UK

Improvements through TASKFORCE Productivity was improved by 15 per cent Reduction in control sites from 100 (manual) to 15

(TASKFORCE) Greatly increased control-to-field employee ratio TASKFORCE enabled the introduction of several new, highly

competitive customer service initiatives: five-hour response time to business customers nine-hour commitment for residential customers.

Overall saving of £100 million per annum in service costs

Intelligent End-to-End Fieldforce Automation

www.apsolve.com

Documents

© a.p.solve Limited 2003 DEPARTMENT PLACEHOLDER Dynamic Scheduling in Mobile Workforce Management Ralf Keuthen PLANET Information Day, Bilbao, 2003