Optimising Training for Service Delivery

We study the problem of training a roster of engineers, who are scheduled to respond to service calls that require a set of skills, and where engineers and calls have different locations. Both training an engineer in a skill and sending an engineer to respond a non-local service call incur a cost. Alternatively, a local contractor can be hired. The problem consists in training engineers in skills so that the quality of service (i.e. response time) is maximised and costs are minimised. The problem is hard to solve in practice partly because (1) the value of training an engineer in one skill depends on other training decisions, (2) evaluating training decisions means evaluating the schedules that are now made possible by the new skills, and (3) these schedules must be computed over a long time horizon, otherwise training may not pay off. We show that a monolithic approach to this problem is not practical. Instead, we decompose it into three subproblems, modelled with MiniZinc. This allows us to pick the approach that works best for each subproblem (MIP or CP) and provide good solutions to the problem. Data is provided by a multinational company

Operational Squadron Scheduling

The 28th Operational Weather Squadron (28th OWS) is responsible for producing and disseminating mission planning and execution weather analyses and forecasts. The squadron must prepare schedules that meet the needs of their mission while dealing with real-world constraints such as time windows, task priorities, and intermittent recurring missions. The 28th OWS\u27s manning consists of active duty, deployed in-place, reserve, civilian and contract personnel. In this research, a scheduling model and algorithm are provided as an approach to crew scheduling for the 28th Operational Weather Squadron. Scheduling in the 28th OWS is complex and can be time consuming. This model will reduce the time and burden of scheduling the squadron

FLIGHT RISK MANAGEMENT AND CREW RESERVE OPTIMIZATION

There are two key concerns in the development process of aviation. One is safety, and the other is cost. An airline running with high safety and low cost must be the most competitive one in the market. This work investigates two research efforts respectively relevant to these two concerns. When building support of a real time Flight Risk Assessment and Mitigation System (FRAMS), a sequential multi-stage approach is developed. The whole risk management process is considered in order to improve the safety of each flight by integrating AHP and FTA technique to describe the framework of all levels of risks through risk score. Unlike traditional fault tree analysis, severity level, time level and synergy effect are taken into account when calculating the risk score for each flight. A risk tree is designed for risk data with flat shape structure and a time sensitive optimization model is developed to support decision making of how to mitigate risk with as little cost as possible. A case study is solved in reasonable time to approve that the model is practical for the real time system. On the other hand, an intense competitive environment makes cost controlling more and more important for airlines. An integrated approach is developed for improving the efficiency of reserve crew scheduling which can contribute to decrease cost. Unlike the other technique, this approach integrates the demand forecasting, reserve pattern generation and optimization. A reserve forecasting tool is developed based on a large data base. The expected value of each type of dropped trip is the output of this tool based on the predicted dropping rate and the total scheduled trips. The rounding step in current applied methods is avoided to keep as much information as possible. The forecasting stage is extended to the optimization stage through the input of these expected values. A novel optimization model with column generation algorithm is developed to generate patterns to cover these expected level reserve demands with minimization to the total cost. The many-to-many covering mode makes the model avoid the influence of forecasting errors caused by high uncertainty as much as possible

An Approach for Optimizing the On-Orbit Servicing Architecture for the Space-Based Radar Constellation

Satellite systems, once operational, are essentially a consumable item with no capacity to maintain, repair, or upgrade them while on-orbit. In order to avoid having to replace costly space assets, the Defense Advanced Research Projects Agency (DARPA) and Air Force Space Command (AFSPC) are looking to developing programs to provide an on-orbit servicing capability for future satellite systems under development, such as the Space-Based Radar (SBR) system. DARPA and AFSPC are studying on-orbit servicing using the Orbital Express platform as part of an Analysis of Alternatives for the SBR program. Like their satellite clients, on-orbit servicing assets are expected to be resource intensive, and so proper management of these space logistics assets is essential. This research provides a flexible planning tool to determine the optimal on-orbit servicing architecture for a given client satellite constellation and applies it to the proposed SBR constellation. The model uses a generalized network structure with side constraints to efficiently solve this large combinatorial optimization problem. The optimal number and type of servicing vehicles to use is found, along with the associated most efficient routing to meet client satellite demand for two commodities within multiple time windows

Scheduling challenges within maintenance repair and overhaul operations in the civil aviation sector

Aircraft Maintenance Repair and Overhaul (MRO) corporations provide two types of service: light and heavy maintenance. Typical MRO problems are related to scheduled and unscheduled aircraft maintenance because of the large number of components and parts that need a lead-in time for delivery and the consequent scheduling of work. This paper focuses on the significant causes of such problems affecting MRO operations. It addresses three major factors as follows: OEMs, Maintenance schedule and manpower and turnaround time. By a systematic review and analysis of scientific literature sources, it is shown that aviation industry standards do not permit aircraft to be scheduled unless they are maintained according to and comply with the stringent standards related to airworthiness. What seems to be important is effective maintenance schedule planning since this reduces time and cost, and enables aircraft to be maintained in a short time. Unfortunately, light maintenance remains beyond the control of airlines because no time allowance is programmed into flight schedules for such events vs increase the number of flight schedule per da

Development of ship maintenance performance measurement framework to assess the decision making process to optimise in ship maintenance planning

Effective maintenance planning is essential and important in any organisation that is responsible for procuring and managing complex assets. In the marine shipping industry maintenance planning is very significant due to its complexity and the obligations on shipping organisations to comply with certain regulations and requirements. Moreover, improper planning can reduce the ship's availability, which may in turn, be reflected in the revenue of the company. Another issue that requires attention in this field is the cost of maintenance, since improper or inadequate planning could result in breakdowns that could increase the cost of maintenance.This research aims to identify the key factors that affect ship maintenance planning and to provide a framework that can help the decision maker to identify and choose optimum decisions regarding ship maintenance. The research is divided into four stages in order to achieve its objectives and to address the research problem.The first stage is the review of the literature to identify the need for maintenance and to select the key factors that affect maintenance planning. The findings indicate that: maintenance scheduling, selection of maintenance strategy, ship construction, crew compensation, and shipyard selection are the most important factors.The second stage is to evaluate maintenance performance measurements for the marine shipping industry by conducting case study and interviews with professionals involved in the mercantile industry. Semi-structured interviews were conducted with six senior staff experts from three different organisations. The results show that: dry docking scheduling, maintenance costs and budgets, customer satisfaction, employees' satisfaction, classification requirements, and the ship's maintenance requirements are the main factors that have great influence on maintenance planning.The third stage is to develop new methodology to measure the maintenance performance in the marine shipping organisation which is the ship maintenance performance measurement (SMPM) framework. The developed method was validated to assist managers in making the right decisions in ship maintenance planning. The framework was developed based on ten thematic criteria that can be used as indicators for potential organisation growth, i.e., maintenance strategy; dry docking scheduling; budget and costs; the ship's equipment; customer satisfaction; employees; health, safety and environment; learning and growth; classification requirements; and the ship's operation and demands requirements. Interviews were conducted with key personnel from the Kuwait Oil Tanker Company (KOTC) to validate the framework.The fourth stage demonstrates that an optimised schedule for the dry docking of ships for routine maintenance has been constructed. This is accomplished on the basis of one measured criterion, dry docking scheduling, by using an integer programming model to maximise the ship's availability within the company fleet. The model is defined by three constraints: the maintenance window, maintenance completion, and the ship's limit. The model was validated using data from KOTC, and the results depict an optimum solution for maintenance scheduling, maximising the ship's availability to 100% and not less than 92%.EThOS - Electronic Theses Online ServiceCollege of Technological Studies at Public Authority for Applied Education and Training, KuwaitGBUnited Kingdo

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