This MS thesis studies the effect of uncertainty in the demand of finished products, supply of raw materials, and maintenance resources availability on the maintenance schedule of a manufacturing facility. A simulation model is formulated in order to realistically model manufacturing systems of various complexities, consisting of multiple interacting machines that degrade and fail over time, and are repaired using imperfectly available maintenance crews and resources. A design of experiments (DOE) based sensitivity study is conducted to find the system parameters that mostly affected the maintenance decisions and corresponding profits. / text

The reliability of supply in a bulk electricity system is directly related to the availability of the generation and transmission facilities. In a conventional vertically integrated system these facilities are usually owned and operated by a single company. In the new deregulated utility environment, these facilities could be owned and operated by a number of independent organizations. In this case, the overall system reliability is the responsibility of an independent system operator (ISO). The load point and system reliabilities are a function of the capacities and availabilities of the generation and transmission facilities and the system topology. This research examines the effect of equipment unavailability on the load point and system reliability of two test systems. The unavailabilities of specific generation and transmission facilities have major impacts on the load point and system reliabilities. These impacts are not uniform throughout the system and are highly dependent on the overall system topology and the operational philosophy of the system. Contingency evaluation is a basic planning and operating procedure and different contingencies can have quite different system and load point impacts. The risk levels associated with a given contingency cannot be estimated using deterministic criteria. The studies presented in this thesis estimate the risk associated with each case using probability techniques and rank the cases based on the predicted risk levels. This information should assist power system managers and planners to make objective decisions regarding reliability and cost. Composite system preventive maintenance scheduling is a challenging task. The functional separation of generation and transmission in the new market environment creates operational and scheduling problems related to maintenance. Maintenance schedules must be coordinated through an independent entity (ISO) to assure reliable and economical service. The methods adopted by an ISO to coordinate planned outages are normally based on traditional load flow and stability analysis and deterministic operating criteria. A new method designated as the maintenance coordination technique (MCT) is proposed in this thesis to coordinate maintenance scheduling. The research work illustrated in this thesis indicates that probabilistic criteria and techniques for composite power system analysis can be effectively utilized in both vertically integrated and deregulated utility systems. The conclusions and the techniques presented in this thesis should prove valuable to those responsible for system planning and maintenance coordination.

The reliability of supply in a bulk electricity system is directly related to the availability of the generation and transmission facilities. In a conventional vertically integrated system these facilities are usually owned and operated by a single company. In the new deregulated utility environment, these facilities could be owned and operated by a number of independent organizations. In this case, the overall system reliability is the responsibility of an independent system operator (ISO). The load point and system reliabilities are a function of the capacities and availabilities of the generation and transmission facilities and the system topology. This research examines the effect of equipment unavailability on the load point and system reliability of two test systems. The unavailabilities of specific generation and transmission facilities have major impacts on the load point and system reliabilities. These impacts are not uniform throughout the system and are highly dependent on the overall system topology and the operational philosophy of the system. Contingency evaluation is a basic planning and operating procedure and different contingencies can have quite different system and load point impacts. The risk levels associated with a given contingency cannot be estimated using deterministic criteria. The studies presented in this thesis estimate the risk associated with each case using probability techniques and rank the cases based on the predicted risk levels. This information should assist power system managers and planners to make objective decisions regarding reliability and cost. Composite system preventive maintenance scheduling is a challenging task. The functional separation of generation and transmission in the new market environment creates operational and scheduling problems related to maintenance. Maintenance schedules must be coordinated through an independent entity (ISO) to assure reliable and economical service. The methods adopted by an ISO to coordinate planned outages are normally based on traditional load flow and stability analysis and deterministic operating criteria. A new method designated as the maintenance coordination technique (MCT) is proposed in this thesis to coordinate maintenance scheduling. The research work illustrated in this thesis indicates that probabilistic criteria and techniques for composite power system analysis can be effectively utilized in both vertically integrated and deregulated utility systems. The conclusions and the techniques presented in this thesis should prove valuable to those responsible for system planning and maintenance coordination.

New Artificial Intelligence (AI) approaches such as simulated annealing, genetic algorithms, simulated evolution, neural networks, tabu search, fuzzy logic and their hybrid techniques have been applied in recent years to solving Generator Maintenance Scheduling (GMS) problems. This paper presents a review of these AI approaches for the GMS problem. The formulation of problems and the methodologies of solution are discussed and analysed. A case study is also included which presents the application of a genetic algorithm to a test system based on a practical power system scenario.

The aim of generator maintenance scheduling (GMS) in an electric power system is to allocate a proper maintenance timetable for generators while maintaining a high system reliability, reducing total production cost, extending generator life time etc. In order to solve this complex problem a genetic algorithm technique is proposed here. The paper discusses the implementation of GAs to GMS problems with two approaches: generational and steady state. The results of applying these GAs to a test GMS problem based on a practical power system scenario are presented and analysed. The effect of different GA parameters is also studied

Traditionally, the electricity industry is fully regulated with a centrally controlled structure. The power system operator has full technical and costing information as well as a full control over the operation and maintenance of power system equipment. Recently, many countries have gone through privatization of their electricity industries unbundling the integrated power system into a number of separate deregulated business entities. The preventive maintenance of power system equipment in the restructured electricity industries is no longer controlled centrally, and none of these entities currently have explicit accountability for maintenance activities. The approaches used to schedule the maintenance activities in the centralized system are not ideal for addressing the new deregulated environments. In recent years a few research publications has been reported in this area. This paper presents a review and analysis of these reported maintenance scheduling approaches for power system equipment in the changed environment.

No / The effective maintenance scheduling of power system generators is very important for the economical and reliable operation of a power system. This represents a tough scheduling problem which continues to present a challenge for efficient optimization solution techniques. This paper presents the application of metaheuristic approaches, such as a genetic algorithm (GA), simulated annealing (SA) and their hybrid for generator maintenance scheduling (GMS) in power systems using an integer representation. This paper mainly focuses on the application of GA/SA and GA/SA/heuristic hybrid approaches. GA/SA hybrid uses the probabilistic acceptance criterion of SA within the GA framework. GA/SA/heuristic hybrid combines heuristic approaches within the GA/SA hybrid to seed the initial population. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the metaheuristic approaches and their hybrid for the test case study are discussed. The results obtained are promising and show that the hybrid approaches are less sensitive to the variations of technique parameters and offer an effective alternative for solving the generator maintenance scheduling problem.

Rail track operators have traditionally used manual methods to construct train timetables. Creating a timetable can take several weeks, and so the process usually stops once the first feasible timetable has been found. It is suspected that this timetable is often far from optimal. Existing methods schedule track maintenance once the best train timetable has been determined and allow little or no adjustments to the timetable. This approach almost certainly produces suboptimal integrated solutions since the track maintenance schedule is developed with the imposition of the previously constructed train timetable. The research in this thesis considers operationally feasible methods to produce integrated train timetables and track maintenance schedules so that, when evaluated according to key performance criteria, the overall schedule is the best possible. This research was carried out as part of the Cooperative Research Centre for Railway Engineering and Technologies. We developed a method that uses a local search meta-heuristic called 'problem space search'. A fast dispatch heuristic repeatedly selects and moves a track possessor (train or maintenance task) through the network; this results in a single integrated schedule. This technique generates a collection of alternative feasible schedules by applying the dispatch heuristic to different sets of randomly perturbed data. The quality of the schedules is then evaluated. Thousands of feasible solutions can be found within minutes. We also formulated an integer programming model that selects a path for each train and maintenance task from a set of alternatives. If all possible paths are considered, then the best schedule found is guaranteed to be optimal. To reduce the size of the model, we explored a reduction technique called 'branch and price'. The method works on small example problems where paths are selected from a predetermined set, but the computation time and memory requirements mean that the method is not suitable for realistic problems. The main advantages of the problem space search method are generality and speed. We are able to model the operations of a variety of rail networks due to the representation of the problem. The generated schedules can be ranked with a user-defined objective measure. The speed at which we produce a range of feasible integrated schedules allows the method to be used in an operational setting, both to create schedules and to test different scenarios. A comparison with simulated current practice on a range of test data sets reveals improvements in total delay of up to 22%.

Rail track operators have traditionally used manual methods to construct train timetables. Creating a timetable can take several weeks, and so the process usually stops once the first feasible timetable has been found. It is suspected that this timetable is often far from optimal. Existing methods schedule track maintenance once the best train timetable has been determined and allow little or no adjustments to the timetable. This approach almost certainly produces suboptimal integrated solutions since the track maintenance schedule is developed with the imposition of the previously constructed train timetable. The research in this thesis considers operationally feasible methods to produce integrated train timetables and track maintenance schedules so that, when evaluated according to key performance criteria, the overall schedule is the best possible. This research was carried out as part of the Cooperative Research Centre for Railway Engineering and Technologies. We developed a method that uses a local search meta-heuristic called 'problem space search'. A fast dispatch heuristic repeatedly selects and moves a track possessor (train or maintenance task) through the network; this results in a single integrated schedule. This technique generates a collection of alternative feasible schedules by applying the dispatch heuristic to different sets of randomly perturbed data. The quality of the schedules is then evaluated. Thousands of feasible solutions can be found within minutes. We also formulated an integer programming model that selects a path for each train and maintenance task from a set of alternatives. If all possible paths are considered, then the best schedule found is guaranteed to be optimal. To reduce the size of the model, we explored a reduction technique called 'branch and price'. The method works on small example problems where paths are selected from a predetermined set, but the computation time and memory requirements mean that the method is not suitable for realistic problems. The main advantages of the problem space search method are generality and speed. We are able to model the operations of a variety of rail networks due to the representation of the problem. The generated schedules can be ranked with a user-defined objective measure. The speed at which we produce a range of feasible integrated schedules allows the method to be used in an operational setting, both to create schedules and to test different scenarios. A comparison with simulated current practice on a range of test data sets reveals improvements in total delay of up to 22%.

abstract: This research develops heuristics to manage both mandatory and optional network capacity reductions to better serve the network flows. The main application discussed relates to transportation networks, and flow cost relates to travel cost of users of the network. Temporary mandatory capacity reductions are required by maintenance activities. The objective of managing maintenance activities and the attendant temporary network capacity reductions is to schedule the required segment closures so that all maintenance work can be completed on time, and the total flow cost over the maintenance period is minimized for different types of flows. The goal of optional network capacity reduction is to selectively reduce the capacity of some links to improve the overall efficiency of user-optimized flows, where each traveler takes the route that minimizes the traveler’s trip cost. In this dissertation, both managing mandatory and optional network capacity reductions are addressed with the consideration of network-wide flow diversions due to changed link capacities. This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule. Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2017