© British Telecommunications plc 2001 The Reactive, Real-Time Management of Mobile Workforces Jon E. Spragg Scheduling Software Designer and Research Coordinator

© British Telecommunications plc 2001

The Reactive, Real-Time Management of Mobile Workforces

Jon E. Spragg

Scheduling Software Designer and Research Coordinator


Scheduling Mobile Workforces 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) is being ‘spun out’ via the British Telecom’s Brightstar business incubator initiative next year.

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


Workforce Management at BT

a.p.solve’s TASKFORCE products currently schedule BT’s workforce of Service Technicians.

25,000 field technicians perform 150,000 tasks every day across the United Kingdom.

A high quality service at low operational cost needs to be delivered.


Issues

Complexity of problem

Scale

The need for a totally automated, online, system.


Complexity

Ever changing workload with a 1 hour response time. Complex mixture of tasks with different execution target times

and priorities. Wide range in the duration of tasks: 8 mins - several days. Work duration is uncertain, subject to environmental disturbances

and delays. Work type and work skill imbalances (some geographical areas

are seriously under resourced in certain skills). Task inter-dependencies can be complex (coops, assists, pre-

installation tasks) Travel times between tasks are subject to change.


Scale

20,000+ technicians, mostly mobile

Several hundred thousand tasks to be scheduled and dispatched every day.

Distinct workforces and scheduling environments.


Automation

Automated data flow from order source systems to job dispatch.

Schedule revision must be automatic and robust.

On line Dispatcher must handle corrupted schedules.

The real-line monitoring of the location of mobile technicians and their expected completion times is important.


Impact of Personal Digital Assistants on Scheduling Practice. Mobile phones, notebooks, laptops, the Internet, ...has

allowed a.p.solve to deliver scheduling solutions to mobile workers, and it has also forced us to rethink how we do scheduling. We are deeply interested in the latest technologies being explored by the scheduling community:

Dynamic scheduling Real-time scheduling On-line scheduling Adaptive scheduling Self-scheduling systems, Reactive scheduling systems


The Limitations of Traditional Scheduling Theory and Practice

Assumed ‘static’ environments: Obsession with optimisation under idealised

assumptions of environmental stability.

Limited support for tool sets to maintain the feasibility and quality of a schedule over time.


Theme: the case for reactive scheduling

On-line Scheduling is Reactive Scheduling -- for the most part.

First call for papers for AIPS 2002 Workshop on ‘On-line Planning and Scheduling’ didn’t mention reactive scheduling in the topics of interest!!


When I first realised this -- a personal account.

Scheduling Progressive Bundle Lines in clothing manufacture

Flow Line Manufacture

Line Balance Algorithms


Flow line theory

Work Station 4

Work Station 1

Work Station 2

Work Station 3

WIP

M3

Op3

WIP

M4

Op4

WIP

M2

Op2

M5

Op5

M1W

IP

Op1

SMV

Sum (Perfop)* 100 = pt


Algorithms for Solving Line Balancing

View it as a static optimisation problem:

Operations Research Branch and Bound Local Search

Genetic algorithms Tabu search


Flow line reality


In the Real World! Optimised balanced lines soon get out of

balance!! Machines breakdown Operators begin working below average

performance. Managers decide that jobs that were high priority

are no longer high priority and jobs that were low priority are now high priority, and …

New jobs need to be introduced onto an existing line with other jobs.

Operators go absent. Quality controllers decide re-work is necessary.


… and there is little you can do about it!

Build robust schedules Knowledge of the scheduling environment? Probabilistic models? Machine learning algorithms?

In a stochastic environment, such as human resource scheduling Reactive scheduling


On-line, Reactive Scheduling Maintain a schedule over time

Incremental Reactive

Mixed initiative approach (DITOPS/OZONE model) Automated Monitoring Automated Analysis Automated Revision Automated Optimisation Automated Execution


Automated On-line, Reactive Scheduling Agents Perform:

Identify processing bottlenecks Exploit scheduling opportunities Maintain schedule stability and existing process plans. Refine solutions. Repair constraint violations. Summarise solution states for human controllers and

software agents. Dispatch scheduling tasks to field technicians with

respect to current schedule state and customer demand.


Execution cycle

Monitor

Analysis

Revision

Optimise

Execute


Automatic Monitoring

Via dedicated HHT and laptop Cancelled jobs New jobs Delayed operations Resource absenteeism Re-visits ...


What can go wrong?

Inconsistency (constraint graph analysis) Resource capacity Temporal consistency

Quality (cost model) Unacceptable cost of late jobs Unacceptable cost of adding additional capacity (I.e.

pulling in a technician from outside the area).


Automatic Analysis

Perturbation metrics (texture measurement)

Optimisation in a dynamic environment Similar schedule metrics (identify neighbourhood and

extend of a perturbation)

Support revision/repair algorithms Support user’s ‘visualisation’ of schedule solutions.


Schedule revision metrics

Metrics that support schedule revision tools: Contention/reliance measures (estimate aggregate

demand for a resource)

Dem

and

Time


Automatic Conflict analysis

Conflict analysis Conflict duration

Conflict size

Resource idle time

Local downstream slack

Protected lateness

Variance in lateness


Automatic Schedule Revision

Reallocation algorithm to support appointment reservations.

A customer requests a technician to attend his premises between 9am and 12am.

The system can’t find an available resource between these hours but can identify a sequence of reallocations to free a technician to attend the customer.


Automatic Optimisation

The time between the construction of a feasible schedule and its execution is used to improve the quality of the schedule

Stochastic search

Simulated Annealing.

We are currently researching techniques for exploring large neighbourhoods based on an ejection chain model.


Automatic Dispatcher

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

identifies a task for the technician to service. This invariably results in the need to repair a

damaged schedule Schedule analysis will produce state summary reports that

support schedule repair after an unscheduled activity execution.

Focal point Neighbourhood of impact Conflict duration Conflict size


System Overview

Intelligent End-to-End Fieldforce Automation

www.apsolve.com

Documents

© British Telecommunications plc 2001 The Reactive, Real-Time Management of Mobile Workforces Jon E. Spragg Scheduling Software Designer and Research Coordinator