Raphael Dorne

iOpt: A Software Toolkit for Heuristic Search Methods

Heuristic Search techniques are known for their efficiency and effectiveness in solving NP-Hard problems. However, there has been limited success so far in constructing a software toolkit which is dedicated to these methods and can fully support all the stages and aspects of researching and developing a system based on these techniques. Some of the reasons for that include the lack of problem modelling facilities and domain specific frameworks which specifically suit the operations of heuristic search, tedious code optimisations which are often required to achieve efficient implementations of these methods, and the large number of available algorithms - both local search and population-based - which make it difficult to implement and evaluate a range of techniques to find the most efficient one for the problem at hand. The iOpt Toolkit, presented in this article, attempts to address these issues by providing problem modelling facilities well-matched to heuristic search operations, a generic framework for developing scheduling applications, and a logically structured heuristic search framework allowing the synthesis and evaluation of a variety of algorithms. In addition to these, the toolkit incorporates interactive graphical components for the visualisation of problem and scheduling models, and also for monitoring the run-time behaviour and configuring the parameters of heuristic search algorithms.

ARMS: An automated resource management system for British Telecommunications plc

Abstract Accurate demand forecasting combined with resource planning is critical to a company’s performance and profitability. This paper describes ARMS (automated resource management system), an integrated system developed for the customer service operations of British Telecommunications plc to help with the operational/tactical planning and deployment of the company’s 20,000-strong field engineer workforce. ARMS integrates a forecasting tool with a resource planning tool and a resource balancing tool providing an end-to-end automated resource management solution for the organisation. OR techniques are used throughout the system, including ARIMA for forecasting, constraint satisfaction for problem modelling, heuristic search for problem solving thus demonstrating the value and relevance of OR in solving today’s business problems.

On the partitioning of dynamic workforce scheduling problems

This problem is based on the British Telecom workforce scheduling problem, in which technicians (with different skills) are assigned to tasks (which require different skills) which arrive (partially) dynamically during the day. In order to manage their workforce, British Telecom divides the different regions into several areas. At the beginning of each day all the technicians in a region are assigned to one of these areas. During the day, each technician is limited to tasks within the assigned area.This effectively decomposes a large dynamic scheduling problem into smaller problems. On one hand, it makes the problem more manageable. On the other hand, it gives rise to, potentially, a mismatch between technicians and tasks within an area. Furthermore, it prevents technicians from being assigned a job which is just outside their area but happens to be close to where they are currently working.This paper studies the effect of the number of partitions on the expected objective (number of completed tasks) that a rule-based system (responsible for the dynamic assignment and reassignment of tasks to resources following dynamic events) can reach.

Integrating Heuristic Search and One-Way Constraints in the Iopt Toolkit

Heuristic search techniques are known for their efficiency and effectiveness in solving NP-hard problems. In this chapter, we present a heuristic search framework allowing the synthesis and evaluation of a variety of algorithms and also a library of oneway constraints for problem modelling. The integration of the two is explained in the context of the iOpt toolkit. iOpt is a large software system comprising several libraries and frameworks dedicated to the development of combinatorial optimization applications based on heuristic search.

On the partitioning of dynamic scheduling problems -: assigning technicians to areas

BT workforce scheduling problem considers technicians (with different skills) which are assigned to tasks which arrive (partially) dynamically during the day. In order to manage their workforce, BT divides the different regions into several areas. In the beginning of each day all the technicians in a region are assigned to one of these areas. During the day, tasks can only be allocated to technicians from the same area. In this paper we use a (1+1) EA in order to decide, once the area have been defined, which technicians to assign to which areas.

Solving Vehicle Routing Using IOPT

The objective of this paper is mainly to answer one question: “Why use a toolkit such as iOpt to solve a combinatorial optimization problem rather than hard-coding a solution from scratch?” To answer this question, we consider a well studied problem: the Vehicle Routing Problem. We explain in details how to make use of the modeling and solving facilities available in iOpt to tackle this problem. At each step of this building process, we discuss the benefits of using iOpt rather than starting building a solution from scratch. Then we exhibit some experiments comparing the results obtained using the best algorithm built using iOpt and the best known in the literature. The overall conclusion of this work is our toolkit allows the user to maximize reuse of his code, significantly reduce his development time, focus his attention on the design rather than the coding, and exchange problem models or algorithms in a very easy and simple way using XML files within his community. At last, algorithms built using iOpt appear to be very competitive compared to the best hard-wired algorithms found in the literature.

Dynamic Planner: A Decision Support Tool for Resource Planning

Accurate resource planning is critical to a company’s performance and profitability. This paper describes the motivation for and the realisation of an automated resource planning system (i.e. Dynamic Planner) within British Telecom. Dynamic Planner is a decision support tool to help resource managers decide how best to deploy their resources. It provides a feel for what resources are needed; how many are needed and how best they should be deployed. It also provides the framework to compare the impact of scenarios involving overtime, borrowed resources and skill move. Dynamic Planner is built atop iOpt, a Java-based optimisation toolkit for modelling and solving combinatorial problems using invariants (one-way constraints) and heuristic search methods respectively.

Hierarchical Type-2 Fuzzy Logic Based Real Time Dynamic Operational Planning System

Operational resource planning is critical for successful operations in service-based organizations as it underpins the process of utilizing resources to achieve a higher quality of service whilst lowering operational costs. The majority of service-based organizations use static operational planning. In recent years these, organizations have made attempts to switch to dynamic operational planners with the view of generating real-time operational plans. This paper proposes a hierarchical type-2 fuzzy logic based operational planner that can work in dynamic environments and can maintain operational plans in real-time. The proposed system outperformed ordinary heuristic-based systems and task dispatchers.

Service scheduling to minimise the risk of missing appointments

This paper introduces the risk minimisation objective in the Stochastic Vehicle Routing Problem (SVRP). In the studied variant of SVRP, technicians drive to customer sites to provide service. The service times and travel times are stochastic, and a time window is required for the start of the service for each customer. Most previous research uses a chance-constrained approach to the problem. Some consider the probability of journey duration exceeding the threshold of the drivers workload while others set restrictions on the probability of individual time window constraints being violated. Their objectives are related to traditional routing costs whilst a different approach was taken in this paper. The risk of missing a task is defined as the probability that the technician assigned to the task arrives at the customer site later than the time window. The problem studied in this paper is to generate a schedule that minimises the maximum risk and sum of risks of the tasks. The duration of each task may be considered as following a known normal distribution. However the distribution of the start time of the service at a customer site will not be normally distributed due to time window constraints. Therefore a multiple integral expression of the risk was derived, and this expression works whether task distribution is normal or not. Additionally a deterministic heuristic searching method was applied to solve the problem. Experiments are carried out to test the method. Results of this work have been applied to an industrial case of SVRP where field engineering individuals drive to customer sites to provide time-constrained services. This original approach allows organisations to pay more attention to increasing customer satisfaction and become more competitive in the market.