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.

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

This thesis focuses on the challenges that arise from the scheduling of heat exchanger network maintenance problems which undergo fouling and run continuously over time. The original contributions of the current research consist of the development of novel optimisation methodologies for the scheduling of cleaning actions in heat exchanger network problems, the application of the novel solution methodology developed to other general maintenance scheduling problems, the development of a stochastic programming formulation using this optimisation technique and its application to these scheduling problems with parametric uncertainty. The work presented in this thesis can be divided into three areas. To efficiently solve this non-convex heat exchanger network maintenance scheduling problem, new optimisation strategies are developed. The resulting contributions are outlined below. In the first area, a novel methodology is developed for the solution of the heat exchanger network maintenance scheduling problems, which is attributed towards a key discovery in which it is observed that these problems exhibit bang-bang behaviour. This indicates that when integrality on the binary decision variables is relaxed, the solution will tend to either the lower or the upper bound specified, obviating the need for integer programming solution techniques. Therefore, these problems are in ac- tuality optimal control problems. To suitably solve these problems, a feasible path sequential mixed integer optimal control approach is proposed. This methodology is coupled with a simple heuristic approach and applied to a range of heat exchanger network case studies from crude oil refinery preheat trains. The demonstrated meth- odology is shown to be robust, reliable and efficient. In the second area of this thesis, the aforementioned novel technique is applied to the scheduling of the regeneration of membranes in reverse osmosis networks which undergo fouling and are located in desalination plants. The results show that the developed solution methodology can be generalised to other maintenance scheduling problems with decaying performance characteristics. In the third and final area of this thesis, a stochastic programming version of the feasible path mixed integer optimal control problem technique is established. This is based upon a multiple scenario approach and is applied to two heat exchanger network case studies of varying size and complexity. Results show that this methodology runs automatically with ease without any failures in convergence. More importantly due to the significant impact on economics, it is vital that uncertainty in data is taken into account in the heat exchanger network maintenance scheduling problem, as well as other general maintenance scheduling problems when there is a level of uncertainty in parameter values.

The operation of a large and complex electric power system requires meticulous and rigorous study and incessant planning. All the players involved, must plan ahead to account for the uncertainties that can affect the hour-to-hour, day-to-day, medium-term and long-term supply of electricity. Medium-term operations and planning provides the players with guidelines and strategies for short-term operating decisions vis-à-vis the market. Adequate planning helps the players to mitigate or be prepared for unforeseen circumstances encountered during scheduling of electricity generation at any stage. This thesis focuses on some aspects of the least explored medium-term operations and planning issues in power systems in the deregulated electricity market environment. The issues addressed in the thesis are diverse but inter-linked as medium-term problems, which have surfaced due to deregulation or are outcomes of unique thought-processes emerging from the restructuring phenomenon. The thesis presents a novel approach to security coordinated maintenance scheduling in deregulation wherein the ISO does not generate a maintenance schedule by itself, but assesses the maintenance schedules from individual gencos by incorporating them in a medium-term security constrained production scheduling model, and verifying whether they result in unserved energy at one or more buses. Based on the information on bus-wise unserved energy, the ISO generates corrective signals for the genco(s), and directs them to alter their maintenance schedules in specific periods and re-submit. The proposed scheme exploits the concept of commons and domains to derive a novel factor to allocate the unserved energy at a bus to a set of generators responsible. The coordination scheme is based on individual genco’s accountability to unserved energy at a bus. Another important question addressed in the thesis is whether there is a need to consider customer’s locations in the power system when the utility provides service to them. In other words, whether the reliability of the load service provided by the utility varies across the system, from bus to bus, and if so, how are the Locational Marginal Prices (LMPs), which are determined from market auctions, affected by such variations. The thesis also answers the important question of how the LMPs can be differentiated by the Load Service Probability (LSP) at a particular location, so that it is fair to all customers. A new approach to determining the bus-wise LSP indices in power systems is proposed in the thesis. These LSP indices are arrived at by defining and computing bus-wise Loss of Load Probability (LOLP) indices. The discrepancy in LMPs with respect to the bus-wise LSP is then investigated and the bus-wise LSP indices are thereafter utilized to formulate a novel proposition for LSP-differentiated LMPs for electricity markets. The thesis furthermore addresses the medium-term Transmission Reinforcement Planning (TRP) problem and proposes a practical approach to TRP by making use of standard design practices, engineering judgement, experience and thumb-rules to construct a Feasibility Set. The Feasibility Set helps in limiting the type and number of reinforcement options available to the transmission planner in selected existing corridors. Mathematical optimization procedure is then applied considering the Feasibility Set, to attain an optimal set of reinforcement decisions that are economical and meets the system demand in the medium-term, without overloading the transmission system. Two different solution approaches- the Decomposition Approach and the Unified Approach are proposed to solve the TRP optimization problem.

The operation of a large and complex electric power system requires meticulous and rigorous study and incessant planning. All the players involved, must plan ahead to account for the uncertainties that can affect the hour-to-hour, day-to-day, medium-term and long-term supply of electricity. Medium-term operations and planning provides the players with guidelines and strategies for short-term operating decisions vis-à-vis the market. Adequate planning helps the players to mitigate or be prepared for unforeseen circumstances encountered during scheduling of electricity generation at any stage. This thesis focuses on some aspects of the least explored medium-term operations and planning issues in power systems in the deregulated electricity market environment. The issues addressed in the thesis are diverse but inter-linked as medium-term problems, which have surfaced due to deregulation or are outcomes of unique thought-processes emerging from the restructuring phenomenon. The thesis presents a novel approach to security coordinated maintenance scheduling in deregulation wherein the ISO does not generate a maintenance schedule by itself, but assesses the maintenance schedules from individual gencos by incorporating them in a medium-term security constrained production scheduling model, and verifying whether they result in unserved energy at one or more buses. Based on the information on bus-wise unserved energy, the ISO generates corrective signals for the genco(s), and directs them to alter their maintenance schedules in specific periods and re-submit. The proposed scheme exploits the concept of commons and domains to derive a novel factor to allocate the unserved energy at a bus to a set of generators responsible. The coordination scheme is based on individual genco’s accountability to unserved energy at a bus. Another important question addressed in the thesis is whether there is a need to consider customer’s locations in the power system when the utility provides service to them. In other words, whether the reliability of the load service provided by the utility varies across the system, from bus to bus, and if so, how are the Locational Marginal Prices (LMPs), which are determined from market auctions, affected by such variations. The thesis also answers the important question of how the LMPs can be differentiated by the Load Service Probability (LSP) at a particular location, so that it is fair to all customers. A new approach to determining the bus-wise LSP indices in power systems is proposed in the thesis. These LSP indices are arrived at by defining and computing bus-wise Loss of Load Probability (LOLP) indices. The discrepancy in LMPs with respect to the bus-wise LSP is then investigated and the bus-wise LSP indices are thereafter utilized to formulate a novel proposition for LSP-differentiated LMPs for electricity markets. The thesis furthermore addresses the medium-term Transmission Reinforcement Planning (TRP) problem and proposes a practical approach to TRP by making use of standard design practices, engineering judgement, experience and thumb-rules to construct a Feasibility Set. The Feasibility Set helps in limiting the type and number of reinforcement options available to the transmission planner in selected existing corridors. Mathematical optimization procedure is then applied considering the Feasibility Set, to attain an optimal set of reinforcement decisions that are economical and meets the system demand in the medium-term, without overloading the transmission system. Two different solution approaches- the Decomposition Approach and the Unified Approach are proposed to solve the TRP optimization problem.