The one-to-one pickup and delivery problem with time-windows (PDPTW) is one of the most important problems in Operations Research (OR). In this problem a set of goods need to be transported in a given time-window with a fleet of vehicles. The pickup and delivery problem is one of the most challenging and important combinatorial optimisation problems as it has many real-world applications. Selection hyper-heuristics that learn heuristic utility during optimisation have been successfully applied to a variety of different optimisation problems including those in OR. In this paper we investigate the application of a sequence-based selection hyperheuristic to the one-to-one, static and deterministic variant of the pickup and delivery problem with time-windows and will compare the results against two well known approaches in the Adaptive Large Neighbourhood Search and Grouping Genetic Algorithm.

This paper describes an on-going project on a full-day simulation of field engineering operations, enabling analysis of a company's capability to cope with changes in work demand and labour supply.This case study incorporates the operational dynamics in the scheduling process, illustrating a schedule of work for service delivery in BT Group plc (BT) over the course of an operational day.These jobs range from repairing existing network cables; installing new infrastructure; connecting cities to the internet and beyond.In this project, we model our operations using a discrete event simulation approach, adding a number of key in-day disturbances which are considered turbulent to BT's service delivery.Furthermore, we discuss the current development state and future directions for this project.

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 driver's 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.

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 chain management enables service organisations to improve customer satisfaction and reduce operational costs. In this book, Christos Voudouris and his BT colleagues together with experts from industry and academia present the latest innovations and technologies used to manage the operations of a service company. The viewpoints presented are based on the BT experience and on associated research and development. Service chain management is looked at both from the enterprise perspective and from the standpoints of the service professional and customer. The focus is on real-world challenges.

The fast moving and competitive global economy requires that organisations manage their human resources in an efficient and flexible manner in order to tap their full potentials. The management of human resources has crucial role in success or failure of an organisation. Today’s services organisations strive to tailor their human re-source management policies that improve their performance by empowering employees. Employee empowerment is a management style that helps organisations to utilise their employee’s potential to maximum and be able to react and adapt quickly and efficiently to changes in service provision. This paper explores the possibility of empowering British Telecommunications (BT) field workforce and its potential benefits by using simulator. We investigate the application of a ‘pull-based strategy’ for empowering BT field workforce and its relation with motivation and productivity of BT workforce. Currently jobs are allocated to BT field engineers by a central scheduler; we refer to it as a ‘push strategy’. In this simulation we attempt to assess the impact of empowerment on BT FE motivation and towards overall increase in productivity.

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.

Most of real-world scheduling problems which include optimisation and feasibility aspects, are NP-hard. To tackle large scale optimization problems, meta-heuristic algorithms are often proposed to quickly obtain near-optimal solutions. However, they have difficulty to find feasible solutions on strongly constrained problems, whereas constraint programming can help as they efficiently handle feasibility problems. Moreover, despite their success, these methods often require complete re-implementation to solve new problems or variations of the same problem. To address these issues, the Framework approach of the intelligent Optimization Toolkit iOpt [5] provides three components for problem modelling, solving and visualizing, based on constraints and meta-heuristic algorithms. The problem representation is well-matched to heuristic search operations, thanks to the use of an invariants processing engine. As a consequence it reduces the time necessary to develop efficient solutions. Extensions of iOpt have been developed for specific domains. Such an extension is the iSchedule Toolkit which is dedicated to assist non-expert users in modelling scheduling applications. This paper outlines iSchedule capabilities and illustrates their use on a particular scheduling problem, the mobile Workforce Scheduling problem, a Vehicle Routing-type problem with time windows, additional tasks and resource constraints, multiple concurrent objectives and subject to disturbances. This problem is of interest to BT in its field service operations.

An important part of Business Performance Management (BPM) is identifying strategic targets for the organisation, cascading them down into tactical and operational targets and finally optimising operational levers or actions as to achieve the given targets. In order to do this, performance metrics have to be defined at all levels and relationships between levers and metrics have to be mathematically modelled. Thereby, traditional BPM cannot deal with imprecise information although imprecision is a commonplace in real world applications. We discuss the most important functions of BPM and show how fuzzy and probabilistic techniques can be applied to process imprecise information. A few of the techniques have already been implemented in a research prototype.

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.

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.

In this paper, we focus on improving service operations by presenting BT's Field Optimisation Suite (FOS). FOS is an integrated advanced planning and scheduling (APS) platform incorporating applications for forecasting demand, planning resources, scheduling work, reserving capacity and managing employee, customer, supplier links across a service chain. FOS is intended for field service operations with high volumes of low-to-medium complexity work as the name implies although several modules are suitable for office environments too.

Advanced PlanEing andScheduling (APS)hasbeenawell-known terminmanufacturing being usedtorefer toSupply Chain Management suites and, morespecifically, theforecasting, planEing andscheduling applications within them. However, theapplicability ofthat partofERP/SCMsystems hasbeensofarlimited toproduct-driven industries. Despite theincreased importance ofservices, integrated APSsystems forservice operations areyettoemerge fromthe mainenterprise software vendors. Inthis paper, wepresent Field Optimisation Suite orFOSforshort. FOSisan integrated APSplatform forservice operations. Theplatform isunderpinned byOperations Research techniques for tackling complex decision making problems.

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.

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

This paper presents a new genetic local search algorithm for the graph coloring problem. The algorithm combines an original crossover based on the notion of union of independent sets and a powerful local search operator (tabu search). This new hybrid algorithm allows us to improve ou the best known results of some large instances of the famous Dimacs benchmarks.

The goal of this paper is twofold. First, we introduce a class of local search procedures for solving optimization and constraint problems. These procedures are based on various heuristics for choosing variables and values in order to examine a general neighborhood. Second, four combinations of heuristics are empirically evaluated by using the graph-coloring problem and a real world application — the frequency assignment problem. The results are also compared with those obtained with other approaches including simulated annealing, Tabu search, constraint programming and heuristic graph coloring algorithms. Empirical evidence shows the benefits of this class of local search procedures for solving large and hard instances.

The goal of this paper is twofold. First, we present an evolutionary approach to a real world application: the Frequency Assignment Problem (FAP) in Cellular Radio Networks. Second, we present an empirical study on the effectiveness of crossover for solving this problem. Experiments carried out on a set of real-size FAP instances (up to 300 cells, 30 frequencies and 30,000 interference constraints) show the interest of EAs. At the same time, empirical evidence suggests that the contribution of crossover is marginal for this application.