The Integration of Maintenance Decisions and Flow Shop Scheduling

In the conventional production and service scheduling problems, it is assumed that the machines can continuously process the jobs and the information is complete and certain. However, in practice the machines must stop for preventive or corrective maintenance, and the information available to the planners can be both incomplete and uncertain. In this dissertation, the integration of maintenance decisions and production scheduling is studied in a permutation flow shop setting. Several variations of the problem are modeled as (stochastic) mixed-integer programs. In these models, some technical nuances are considered that increase the practicality of the models: having various types of maintenance, combining maintenance activities, and the impact of maintenance on the processing times of the production jobs. The solution methodologies involve studying the solution space of the problems, genetic algorithms, stochastic optimization, multi-objective optimization, and extensive computational experiments. The application of the problems and managerial implications are demonstrated through a case study in the earthmoving operations in construction projects

Medium-term optimization-based approach for the integration of production planning, scheduling and maintenance

A medium-term optimization-based approach is proposed for the integration of production planning, scheduling and maintenance. The problem presented in this work considers a multiproduct single-stage batch process plant with parallel units and limited resources. An MILP continuous-time formulation is developed based on the main ideas of travelling salesman problem and precedence-based constraints to deal with, sequence-dependent unit performance decay, flexible recovery operations, resource availability and product lifetime. Small scheduling examples have been solved and compared with adapted formulations from the literature, based on discrete-time and global-time events, demonstrating the effectiveness of the proposed solution approach. Additional planning and scheduling problems have been proposed by considering several time periods. Multi-period examples have been efficiently solved by the model showing the applicability of the solution approach for medium-size problems

Some contributions to maintenance and accelerated degradation test under complex failure process

Ph.DDOCTOR OF PHILOSOPH

Fault Diagnosis of Centrifugal Pumps based on the Intrinsic Time-scale Decomposition of Motor Current Signals

Centrifugal pumps are widely used in various manufacturing processes, such as power plants, and chemistry. However, pump problems are responsible for large amount of the maintenance budget. An early detection of such problems would provide timely information to take appropriate preventive actions. This paper investigates the application of Machine Learning Techniques (MLT) in monitoring and diagnosing fault in centrifugal pump. In particular, the focus is on utilising motor current signals since they can be measured remotely for easy and low-cost deployment. Moreover, because the signals are usually produced by a nonlinear process and contaminated by various noises, it is difficult to obtain accurate diagnostic features with conventional signal processing methods such as Fourier spectrum and wavelet transforms as they rely heavily on standard basis functions and often capture limited nonlinear weak fault signatures. Therefore, a data-driven method: Intrinsic Time-scale Decomposition (ITD) is adopted in this study to process motor current signals from different pump fault cases. The results indicate that the proposed ITD technique is an effective method for extracting useful diagnostic information, leading to accurate diagnosis by combining the RMS values of the first Proper Rotation Component (PRC) with the raw signal RMS values

Using Decision Analysis to Select Facility Maintenance Management Information Systems

Maintenance organizations, charged with preserving the built environment, are receiving a shrinking portion of an organization’s operational budget to do its job. It has been demonstrated through various studies that efficiencies can be gained by implementing a maintenance management information system (MMIS). However, with so many choices available, maintenance organizations often select the wrong system. This research effort used value-focused thinking decision analysis to create a model based on values from the Air Force Civil Engineer career field. Data for values and weights were collected from official documents and interviews. The resulting model is highly flexible, allowing the ultimate decision-maker to easily modify weights and value functions related to MMISs. The values and evaluation measures were used to score systems that were selected as alternatives. Sensitivity analyses were conducted to study the influence of evaluation measure weights on the final alternative rankings. The sensitivity analyses displayed alterations in rankings for each alternative based on changes in value weighing. Results indicate that commercially available systems may not be appropriate for Air Force use. The resulting model provides a readily modifiable decision model for the Air Force, as well as other maintenance organizations, to use when selecting a MMIS

Condition monitoring of wind turbine pitch controller: A maintenance approach

With the increase of wind power capacity worldwide, researchers are focusing their attention on the operation and maintenance of wind turbines. A proper pitch controller must be designed to extend the life cycle of a wind turbine’s blades and tower. The pitch control system has two primaries, but conflicting, objectives: to maximize the wind energy captured and converted into electrical energy and to minimize fatigue and mechanical load. Four metrics have been proposed to balance these two objectives. Also, diverse pitch controller strategies are proposed in this paper to evaluate these objectives. This paper proposes a novel metrics approach to achieve the conflicting objectives with a maintenance focus. It uses a 100 kW wind turbine as a case study to simulate the proposed pitch control strategies and evaluate with the metrics proposed. The results are shown in two tables due to two different wind models are used

Improving decision making for incentivised and weather-sensitive projects

The field of project management has originated from the domain of operational research, which focuses on the mathematical optimization of operational problems. However, in recent decades an increasingly broad perspective has been applied to the field of project management. As such, project management has spawned a number of very active sub- domains, which focus not solely on the scheduling of the project’s baseline, but also on the analysis of risk, as well as the controlling of project execution. This dissertation focuses on two areas where existing literature is still lacking. The first area is the use of incentivised contractual agreements between the owner of a project, and the contractor who is hired to execute the project. Whereas this area has received growing attention in recent years, the majority of studies remained strongly descriptive. Hence, the aim of the first part of this dissertation is to develop a more prescriptive approach from both the owner’s and the contractor’s perspective. The second part of this dissertation investigates the use of dedicated weather models to improve operational performance of weather-sensitive projects. During recent decades, significant effort has been made to improve the quality of weather simulation models. Moreover, the amount of available weather data has been steadily increasing. This opens up a lot of new possibilities for using more precise weather models in order to support operational decision making. In spite of this, the number of applications of these weather models in operational research has remained rather limited. As such, the aim of the second part of this dissertation is to leverage these weather models to improve the scheduling of offshore construction projects, as well as preventive maintenance of offshore wind turbines