Risk-Based Optimal Scheduling for the Predictive Maintenance of Railway Infrastructure

In this thesis a risk-based decision support system to schedule the predictive maintenance activities, is proposed. The model deals with the maintenance planning of a railway infrastructure in which the due-dates are defined via failure risk analysis.The novelty of the approach consists of the risk concept introduction in railway maintenance scheduling, according to ISO 55000 guidelines, thus implying that the maintenance priorities are based on asset criticality, determined taking into account the relevant failure probability, related to asset degradation conditions, and the consequent damages

Optimization Under Uncertainty in Building Energy Management

The introduction of decentralized energy resources as well as energy storage systems to the energy system calls for new control and coordination mechanisms and systems. This is also true for buildings. An optimized operation of buildings comprising decentralized generation and energy storage systems can be achieved by a building energy management system. It controls and coordinates the operation of individual devices in a building\u27s energy system to achieve given goals, such as the increase of energy efficiency, the decrease of carbon emissions, the minimization of operating costs or the provision of demand response measures. This thesis picks up on this idea and extends the ongoing research by presenting an approach to the optimized operation of building energy systems that includes the uncertainties in the predictions of the future energy generation and consumption into the control scheme of a building energy management system. To do so, this thesis identified the use of a scenario-based consideration of the uncertainties to be best suited. The presented approach uses a rolling horizon optimization approach with a stochastic two-stage optimization problem, which considers several forecast scenarios in the optimization

Advanced Unit Commitment Strategies in the United States Eastern Interconnection

This project sought to evaluate the impacts of high wind penetrations on the U.S. Eastern Interconnection and analyze how different unit commitment strategies may affect these impacts

Disruption Management of Rolling Stock in Passenger Railway Transportation

This paper deals with real-time disruption management of rolling stock in passenger railway transportation. We present a generic framework for modeling disruptions in railway rolling stock schedules. The framework is presented as an online combinatorial decision problem where the uncertainty of a disruption is modeled by a sequence of information updates. To decompose the problem we propose a rolling horizon approach where only rolling stock decisions within a certain time horizon from the time of rescheduling are taken into account. The schedules are then revised as the situation progresses and more accurate information becomes available. We extend an existing model for rolling stock scheduling to the specific requirements of the real-time case and apply it in the rolling horizon framework. We perform computational tests on instances constructed from real life cases and explore the consequences of different settings of the approach for the trade-off between solution quality and computation time

Train planning in a fragmented railway: a British perspective

Train Planning (also known as railway scheduling) is an area of substantial importance to the success of any railway. Through train planning, railway managers aim to meet the needs of customers whilst using as low a level of resources (infrastructure, rolling stock and staff) as possible. Efficient and effective train planning is essential to get the best possible performance out of a railway network. The author of this thesis aims, firstly, to analyse the processes which are used to develop train plans and the extent to which they meet the objectives that they might be expected to meet and, secondly, to investigate selected new and innovative software approaches that might make a material difference to the effectiveness and/or efficiency of train planning processes. These aims are delivered using a range of primarily qualitative research methods, including literature reviews, interviews, participant observation and case studies, to understand these processes and software. Conclusions regarding train planning processes include how the complexity of these processes hinders their effectiveness, the negative impact of the privatisation of British Rail on these processes and the conflicting nature of objectives for train planning in the privatised railway. Train planning software is found not to adequately support train planners in meeting the objectives they are set. The potential for timetable generation using heuristics and for timetable performance simulation to improve the effectiveness of train planning are discussed and recommendations made for further research and development to address the limitations of the software currently available

Modeling Industrial Lot Sizing Problems: A Review

In this paper we give an overview of recent developments in the field of modeling single-level dynamic lot sizing problems. The focus of this paper is on the modeling various industrial extensions and not on the solution approaches. The timeliness of such a review stems from the growing industry need to solve more realistic and comprehensive production planning problems. First, several different basic lot sizing problems are defined. Many extensions of these problems have been proposed and the research basically expands in two opposite directions. The first line of research focuses on modeling the operational aspects in more detail. The discussion is organized around five aspects: the set ups, the characteristics of the production process, the inventory, demand side and rolling horizon. The second direction is towards more tactical and strategic models in which the lot sizing problem is a core substructure, such as integrated production-distribution planning or supplier selection. Recent advances in both directions are discussed. Finally, we give some concluding remarks and point out interesting areas for future research

A Hybrid Tabu/Scatter Search Algorithm for Simulation-Based Optimization of Multi-Objective Runway Operations Scheduling

As air traffic continues to increase, air traffic flow management is becoming more challenging to effectively and efficiently utilize airport capacity without compromising safety, environmental and economic requirements. Since runways are often the primary limiting factor in airport capacity, runway operations scheduling emerge as an important problem to be solved to alleviate flight delays and air traffic congestion while reducing unnecessary fuel consumption and negative environmental impacts. However, even a moderately sized real-life runway operations scheduling problem tends to be too complex to be solved by analytical methods, where all mathematical models for this problem belong to the complexity class of NP-Hard in a strong sense due to combinatorial nature of the problem. Therefore, it is only possible to solve practical runway operations scheduling problem by making a large number of simplifications and assumptions in a deterministic context. As a result, most analytical models proposed in the literature suffer from too much abstraction, avoid uncertainties and, in turn, have little applicability in practice. On the other hand, simulation-based methods have the capability to characterize complex and stochastic real-life runway operations in detail, and to cope with several constraints and stakeholders’ preferences, which are commonly considered as important factors in practice. This dissertation proposes a simulation-based optimization (SbO) approach for multi-objective runway operations scheduling problem. The SbO approach utilizes a discrete-event simulation model for accounting for uncertain conditions, and an optimization component for finding the best known Pareto set of solutions. This approach explicitly considers uncertainty to decrease the real operational cost of the runway operations as well as fairness among aircraft as part of the optimization process. Due to the problem’s large, complex and unstructured search space, a hybrid Tabu/Scatter Search algorithm is developed to find solutions by using an elitist strategy to preserve non-dominated solutions, a dynamic update mechanism to produce high-quality solutions and a rebuilding strategy to promote solution diversity. The proposed algorithm is applied to bi-objective (i.e., maximizing runway utilization and fairness) runway operations schedule optimization as the optimization component of the SbO framework, where the developed simulation model acts as an external function evaluator. To the best of our knowledge, this is the first SbO approach that explicitly considers uncertainties in the development of schedules for runway operations as well as considers fairness as a secondary objective. In addition, computational experiments are conducted using real-life datasets for a major US airport to demonstrate that the proposed approach is effective and computationally tractable in a practical sense. In the experimental design, statistical design of experiments method is employed to analyze the impacts of parameters on the simulation as well as on the optimization component’s performance, and to identify the appropriate parameter levels. The results show that the implementation of the proposed SbO approach provides operational benefits when compared to First-Come-First-Served (FCFS) and deterministic approaches without compromising schedule fairness. It is also shown that proposed algorithm is capable of generating a set of solutions that represent the inherent trade-offs between the objectives that are considered. The proposed decision-making algorithm might be used as part of decision support tools to aid air traffic controllers in solving the real-life runway operations scheduling problem

Dynamic scheduling in a multi-product manufacturing system

To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation