Metaheuristics For Solving Real World Employee Rostering and Shift Scheduling Problems

Optimising resources and making considerate decisions are central concerns in any responsible organisation aiming to succeed in efficiently achieving their goals. Careful use of resources can have positive outcomes in the form of fiscal savings, improved service levels, better quality products, improved awareness of diminishing returns and general output efficiency, regardless of field. Operational research techniques are advanced analytical tools used to improve managerial decision-making. There have been a variety of case studies where operational research techniques have been successfully applied to save millions of pounds. Operational research techniques have been successfully applied to a multitude of fields, including agriculture, policing, defence, conservation, air traffic control, and many more. In particular, management of resources in the form of employees is a challenging problem --- but one with the potential for huge improvements in efficiency. The problem this thesis tackles can be divided into two sub-problems; the personalised shift scheduling & employee rostering problem, and the roster pattern problem. The personalised shift scheduling & employee rostering problem involves the direct scheduling of employees to hours and days of week. This allows the creation of schedules which are tailored to individuals and allows a fine level over control over the results, but with at the cost of a large and challenging search space. The roster pattern problem instead takes existing patterns employees currently work, and uses these as a pool of potential schedules to be used. This reduces the search space but minimises the number of changes to existing employee schedules, which is preferable for personnel satisfaction. Existing research has shown that a variety of algorithms suit different problems and hybrid methods are found to typically outperform standalone ones in real-world contexts. Several algorithmic approaches for solving variations of the employee scheduling problem are considered in this thesis. Initially a VNS approach was used with a Metropolis-Hastings acceptance criterion. The second approach utilises ER&SR controlled by the EMCAC, which has only been used in the field of exam timetabling, and has not before been used within the domain of employee scheduling and rostering. ER&SR was then hybridised with our initial approach, producing ER&SR with VNS. Finally, ER&SR was hybridised into a matheuristic with Integer Programming and compared to the hybrid's individual components. A contribution of this thesis is evidence that the algorithm ER&SR has merit outside of the original sub-field of exam scheduling, and can be applied to shift scheduling and employee rostering. Further, ER&SR was hybridised and schedules produced by the hybridisations were found to be of higher quality than the standalone algorithm. In the literature review it was found that hybrid algorithms have become more popular in real-world problems in recent years, and this body of work has explored and continued this trend. Problem formulations in this thesis provide insight into creating constraints which satisfy the need for minimising employee dissatisfaction, particularly in regards to abrupt change. The research presented in this thesis has positively impacted a multinational and multibillion dollar field service operations company. This has been achieved by implementing a variety of techniques, including metaheuristics and a matheuristic, to schedule shifts and roster employees over a period of several months. This thesis showcases the research outputs by this project, and highlights the real-world impact of this research

Optimisation for Large-scale Maintenance, Scheduling and Vehicle Routing Problems

Solving real-world combinatorial problems is involved in many industry fields to minimise operational cost or to maximise profit, or both. Along with continuous growth in computing power, many asset management decision-making processes that were originally solved by hand now tend to be based on big data analysis. Larger scale problem can be solved and more detailed operation instructions can be delivered. In this thesis, we investigate models and algorithms to solve large scale Geographically Distributed asset Maintenance Problems (GDMP). Our study of the problem was motivated by our business partner, Gaist solutions Ltd., to optimise scheduling of maintenance actions for a drainage system in an urban area. The models and solution methods proposed in the thesis can be applied to many similar issues arising in other industry fields. The thesis contains three parts. We firstly built a risk driven model considering vehicle routing problems and the asset degradation information. A hyperheuristic method embedded with customised low-level heuristics is employed to solve our real-world drainage maintenance problem in Blackpool. Computational results show that our hyperheuristic approach can, within reasonable CPU time, produce much higher quality solutions than the scheduling strategy currently implemented by Blackpool council. We then attempt to develop more efficient solution approaches to tackle our GDMP. We study various hyperheuristics and propose efficient local search strategies in part II. We present computational results on standard periodic vehicle routing problem instances and our GDMP instances. Based on manifold experimental evidences, we summarise the principles of designing heuristic based solution approaches to solve combinatorial problems. Last bu not least, we investigate a related decision making problem from highway maintenance, that is again of interest to Gaist solutions Ltd. We aim to make a strategical decision to choose a cost effective method of delivering the road inspection at a national scale. We build the analysis based on the Chinese Postman Problem and theoretically proof the modelling feasibility in real-world road inspection situations. We also propose a novel graph reduction process to allow effective computation over very large data sets

Optimisation heuristics for solving technician and task scheduling problems

Motivated by an underlying industrial demand, solving intractable technician and task scheduling problems through the use of heuristic and metaheuristic approaches have long been an active research area within the academic community. Many solution methodologies, proposed in the literature, have either been developed to solve a particular variant of the technician and task scheduling problem or are only appropriate for a specific scale of the problem. The motivation of this research is to find general-purpose heuristic approaches that can solve variants of technician and task scheduling problems, at scale, balancing time efficiency and solution quality. The unique challenges include finding heuristics that are robust, easily adapted to deal with extra constraints, and scalable, to solve problems that are indicative of the real world. The research presented in this thesis describes three heuristic methodologies that have been designed and implemented: (1) the intelligent decision heuristic (which considers multiple team configuration scenarios and job allocations simultaneously), (2) the look ahead heuristic (characterised by its ability to consider the impact of allocation decisions on subsequent stages of the scheduling process), and (3) the greedy randomized heuristic (which has a flexible allocation approach and is computationally efficient). Datasets used to test the three heuristic methodologies include real world problem instances, instances from the literature, problem instances extended from the literature to include extra constraints, and, finally, instances created using a data generator. The datasets used include a broad array of real world constraints (skill requirements, teaming, priority, precedence, unavailable days, outsourcing, time windows, and location) on a range of problem sizes (5-2500 jobs) to thoroughly investigate the scalability and robustness of the heuristics. The key findings presented are that the constraints a problem features and the size of the problem heavily influence the design and behaviour of the solution approach used. The contributions of this research are; benchmark datasets indicative of the real world in terms of both constraints included and problem size, the data generators developed which enable the creation of data to investigate certain problem aspects, mathematical formulation of the multi period technician routing and scheduling problem, and, finally, the heuristics developed which have proved to be robust and scalable solution methodologies

Operational Research IO2017, Valença, Portugal, June 28-30

This proceedings book presents selected contributions from the XVIII Congress of APDIO (the Portuguese Association of Operational Research) held in Valença on June 28–30, 2017. Prepared by leading Portuguese and international researchers in the field of operations research, it covers a wide range of complex real-world applications of operations research methods using recent theoretical techniques, in order to narrow the gap between academic research and practical applications. Of particular interest are the applications of, nonlinear and mixed-integer programming, data envelopment analysis, clustering techniques, hybrid heuristics, supply chain management, and lot sizing and job scheduling problems. In most chapters, the problems, methods and methodologies described are complemented by supporting figures, tables and algorithms. The XVIII Congress of APDIO marked the 18th installment of the regular biannual meetings of APDIO – the Portuguese Association of Operational Research. The meetings bring together researchers, scholars and practitioners, as well as MSc and PhD students, working in the field of operations research to present and discuss their latest works. The main theme of the latest meeting was Operational Research Pro Bono. Given the breadth of topics covered, the book offers a valuable resource for all researchers, students and practitioners interested in the latest trends in this field.info:eu-repo/semantics/publishedVersio

Problèmes de tournées de véhicules et application industrielle pour la réduction de l'empreinte écologique

Dans cette thèse, nous nous sommes intéressés à la résolution approchée de problèmes de tournées de véhicules. Nous avons exploité des travaux menés sur les graphes d'intervalles et des propriétés de dominance relatives aux tournées saturées pour traiter les problèmes de tournées sélectives plus efficacement. Des approches basées sur un algorithme d'optimisation par essaim particulaire et un algorithme mémétique ont été proposées. Les métaheuristiques développées font appel à un ensemble de techniques particulièrement efficaces telles que le découpage optimal, les opérateurs de croisement génétiques ainsi que des méthodes de recherches locales. Nous nous sommes intéressés également aux problèmes de tournées classiques avec fenêtres de temps. Différents prétraitements ont été introduits pour obtenir des bornes inférieures sur le nombre de véhicules. Ces prétraitements s'inspirent de méthodes issues de modèles de graphes, de problème d'ordonnancement et de problèmes de bin packing avec conflits. Nous avons montré également l'utilité des méthodes développées dans un contexte industriel à travers la réalisation d'un portail de services mobilité.In this thesis, we focused on the development of heuristic approaches for solvingvehicle routing problems. We exploited researches conducted on interval graphsand dominance properties of saturated tours to deal more efficiently with selectivevehicle routing problems. An adaptation of a particle swarm optimization algorithmand a memetic algorithm is proposed. The metaheuristics that we developed arebased on effective techniques such as optimal split, genetic crossover operatorsand local searches. We are also interested in classical vehicle problems with timewindows. Various pre-processing methods are introduced to obtain lower boundson the number of vehicles. These methods are based on many approaches usinggraph models, scheduling problems and bin packing problems with conflicts. Wealso showed the effectiveness of the developed methods with an industrial applicationby implementing a portal of mobility services.COMPIEGNE-BU (601592101) / SudocSudocFranceF

Constraint Programming-Based Heuristics for the Multi-Depot Vehicle Routing Problem with a Rolling Planning Horizon

Der Transportmarkt ist sowohl durch einem intensiven Kostenwettbewerb als auch durch hohe Erwartungen der Kunden an den Service geprägt. Die vorliegende Dissertation stellt zwei auf Constraint Programming basierende heuristische Frameworks vor, die eine Reoptimierung bereits geplanter Touren zu festgelegten Zeitpunkten erlauben und so eine Reaktion auf die gesteigerte Wettbewerbsdynamik und den Kostendruck ermöglichen.Actors on the transportation market currently face two contrary trends: Cost pressure caused by intense competition and a need for prompt service. We introduce two heuristic solution frameworks to enable freight carriers to deal with this situation by reoptimizing tours at predefined points in time. Both heuristics are based on Constraint Programming techniques

Enhancing Decision Support Systems for Airport Slot Allocation

Due to the growing imbalance between air traffic demand and airport capacity at congested airports, airlines must secure slots to operate flights at capacity-constrained airports. In practice, slot allocation is performed by independent slot coordinators at each airport according to a set of principles and regulations. As a result, the current decision-making system is considered inefficient and does not take adequate account of the complexity of real-world problems. Therefore, optimisation techniques are needed to improve airport capacity management and slot allocation. This thesis aims to contribute to single airport slot allocation research by providing an in-depth analysis of the slot request data and developing new models and solution algorithms to deal with large-scale slot allocation problems. First, we propose a new model considering slot rejections (SASA-R) based on the maximum acceptable displacement of slots to support the decision-making of rejecting slots. In addition, we analyse the impact of changing the current slot allocation rules on slot allocation results. Second, we propose a two-stage approach that aims to solve large-scale slot allocation problems. A greedy constructive heuristic is developed to generate feasible solutions in a short time. This initial feasible solution is then improved by an adaptive large neighbourhood search heuristic (ALNS). A novel related destroy operator is designed specifically for this problem. The results show high-quality solutions can be obtained within a few hours for the problem instance tested, while a commercial optimisation solver does not return a feasible solution after several days of computation. Third, we propose a flexible slot allocation model to allocate slots individually on different days of the week. This model enhances existing models by enabling coordinators to explore the trade-off between schedule regularity and flexibility. The results show that the flexible scheduler can simultaneously reduce the number of rejected slots and schedule displacement