Christos Voudouris

Guided local search and its application to the traveling salesman problem

Abstract The Traveling Salesman Problem (TSP) is one of the most famous problems in combinatorial optimization. In this paper, we are going to examine how the techniques of Guided Local Search (GLS) and Fast Local Search (FLS) can be applied to the problem. GLS sits on top of local search heuristics and has as a main aim to guide these procedures in exploring efficiently and effectively the vast search spaces of combinatorial optimization problems. GLS can be combined with the neighborhood reduction scheme of FLS which significantly speeds up the operations of the algorithm. The combination of GLS and FLS with TSP local search heuristics of different eiciency and effectiveness is studied in an effort to determine the dependence of GLS on the underlying local search heuristic used. Comparisons are made with some of the best TSP heuristic algorithms and general optimization techniques which demonstrate the advantages of GLS over alternative heuristic approaches suggested for the problem.

Guided Local Search

Combinatorial explosion is a well-known phenomenon that prevents complete algorithms from solving many real-life combinatorial optimization problems. In many situations, heuristic search methods are needed. This chapter describes the principles of Guided Local Search (GLS) and Fast Local Search (FLS) and surveys their applications. GLS is a penalty-based metaheuristic algorithm that sits on top of other local search algorithms, with the aim to improve their efficiency and robustness. FLS is a way of reducing the size of the neighbourhood to improve the efficiency of local search. The chapter also provides guidance for implementing and using GLS and FLS. Four problems, representative of general application categories, are examined with detailed information provided on how to build a GLS-based method in each case.

Fast local search and guided local search and their application to British Telecom's workforce scheduling problem

This paper reports a fast local search (FLS) algorithm which helps to improve the efficiency of hill climbing and a guided local search (GLS) algorithm which was developed to help local search to escape local optima and distribute search effort. To illustrate how these algorithms work, this paper describes their application to British Telecoms workforce scheduling problem, which is a hard real life problem. The effectiveness of FLS and GLS are demonstrated by the fact that they both outperform all the methods applied to this problem so far, which include simulated annealing, genetic algorithms and constraint logic programming.

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.

On Optimising Resource Planning in BT plc with FOS

The need to move from reactive to proactive resource planning has been highlighted by industry analysts, academia and enterprises. Proactive resource planning provides business users with a view of future jobs, which in turn will help them to plan their workforce utilisation appropriately in order to reduce costs and improve customer satisfaction. This paper presents the application of FOS, an integrated service management system, for managing the resources of BT. FOS incorporates applications for reliable workload forecasting, optimised workforce planning, as well as advance tools for visualising and communicating the outputs to end users

Modular neural networks for recursive collaborative forecasting in the service chain

In order to honour customer demand and sustain quality of service in BTs service chain, accurate forecasting for customer demand is critical for optimal resource planning. In the more general context of service organisations, failure to allocate sufficient resources to meet anticipated customer demand will lead to delayed or disrupted service provision which in turn will result in degraded quality of service for customers and ill-balanced utilisation of available resources. In this paper, we present our ongoing research on a prototype collaborative forecasting application, whereas organisations involved in a supply and demand partnership aim to co-operate by sharing and jointly forming forecasts to aid in resource planning. We identify key theoretical and implementation specific issues related to the area of collaborative forecasting and discuss our initial modular artificial neural network approach to the problem.

FOS: An Advanced Planning and Scheduling Suite for Service Operations

Advanced planning and scheduling (APS) has been a well-known term in manufacturing being used to refer to supply chain management suites and, more specifically the forecasting, planning and scheduling applications within them. However, the applicability of that part of ERP/SCM systems has been so far limited to product-driven industries. Despite the increased importance of services, integrated APS systems for service operations are yet to emerge from the main enterprise software vendors. In this paper, we present Field Optimisation Suite or FOS for short. FOS is an integrated APS platform for service operations. The platform is underpinned by operations research techniques for tackling complex decision making problems

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.