In activity based modelling the concept of rescheduling is very important in order to gain dynamic scheduling of activities and to adjust the effect of unexpected incidents in individual agendas to keep them realistic and valid. This report describes a new framework to investigate algorithms for rescheduling on a large scale. This framework models the information of traffic demand and results of micro simulation of traffic on a loaded network; it enables agents to adapt their schedules by providing them with information about the traffic flow. A perception filter for each agent is included in this framework. It models the concept that some agents can notice the broadcast traffic information about the incident and get their own prediction of the expected delay, while other agents who do not notice the information can become aware only by experiencing traffic jam. Initial agendas are created by means of the FEATHERS activity based schedule generator for mutually independent agents. FEATHERS has no knowledge about the actual transportation network but makes use of an impedance matrix that specifies the minimal travel time between traffic analysis zones. The matrix specifies a free-flow value for the uncongested case and correction values for the loaded network. In this new framework the network state can be changed by agent behaviour and external incidents; the effect of this change in network state is perceived differently by each agent through a perception filter, and according to the perceived value individual adaption is calculated by a ReScheduler. The modified behaviour again creates new traffic demand hence creating a new traffic state; this phenomenon continues for the complete day. Each activity in the agenda is assumed to generate some utility. Each individual is assumed to maximize the total utility over the day. The ReScheduler is implemented using a marginal utility function that monotonically decreases with activity duration. This results in a monotonically converging relaxation algorithm to efficiently determine the new activity timing when less time is available for activities due to increased travel time caused by the incident.

Public dependence on air transportation has grown to its largest point in history. Along with this increased dependence is a heightened awareness of safety concerns and the need for pilots to cover all scheduled flights. All commercial pilots are certified for the particular aircraft they are flying by satisfactorily completing a Federal Aviation Administration (FAA) approved course. During the training course, a number of training devices are used including a Full Flight Simulator (FFS) and a Flight Training Device (FTD). The procurement, installation, operation, and maintenance costs of these devices are expensive. In addition, the amount of time pilots spend in training is costly because they continue to be paid their salary rate although they are unable to fly revenue-generating flights for the airline. Due to these high costs, training is scheduled very tightly, with the goals of maximizing training device utilization and minimizing the pilot's training footprint (time spent in training). Any disruption to the tight schedule, such as a simulator breakdown, or pilot illness, renders the original schedule obsolete and demands rescheduling of activities. In this research, the rescheduling problem is investigated through the development and application of several different rescheduling approaches. The problem is decomposed by first investigating a single resource model and insights gained from this experimentation are transferred to the multiple resource model. The solution approaches developed for experimentation include: right-shift rescheduling (RSR), rescheduling of affected activities (RAFF), and rescheduling of all activities (RALL). Performance measures used to compare the various approaches include the minimization of the pilot footprint, the minimization of pilot tardiness, and the minimization of the deviation that a revised schedule has from the original schedule. A case study and a series of experiments involving random disruptions to original schedules were used to analyze the solution approaches. For the data sets analyzed, the RAFF algorithm outperformed other methods with respect to the majority of the measure collected. Analysis performed on the amount of slack in the original schedule revealed that diminishing returns were observed beyond a certain level of slack. Further analysis on the impact of the location of this slack showed that the majority of the slack should be placed at the end of the schedule, or the slack should be dispersed almost evenly over the entire schedule. / Master of Science

The purpose of this thesis is to develop a heuristic solution for the static problem of resequencing unblocked vehicles as a part of an ongoing research project at Daimler AG. The target client of this project is Mercedes-Benz Cars. An unblocked vehicle is defined as a vehicle that for some reason could not be processed in its given time slot but at a later point in time needs to be inserted into the production sequence. Work overload is defined as work that the worker is unable to finish prior to reaching the station border. The resequencing problem can be described as finding new positions for a set of unblocked vehicles in a sequence of previously not blocked vehicles, such that the new sequence containing the previously not blocked vehicles and the additional unblocked vehicles causes as little work overload as possible. A decision has to be made in real-time, forcing the solution method to return a solution within a cycle time. Today, Mercedes-Benz Cars uses the sequencing approach “car sequencing”. This approach relies on so called spacing constraints, which basically means, trying to distribute work intensive vehicles as evenly as possible over the planning horizon and thereby enabling a hopefully smooth production. The car sequencing approach needs limited information. The difficulty is to find spacing constraints that fits the high level of product customization characterizing a modern car manufacturer. To overcome these difficulties, a new approach is being considered, namely the mixed-model sequencing, which takes more detailed data into account than the car sequencing approach but on the other hand is more costly in terms of computation. To this end, a simple but promising tabu search scheme was developed, that for many instances was able to find the optimal solution in less than 30 seconds of computing time and that also clearly outperformed all benchmark heuristics.

Train dispatchers faces lots of challenges due to conflicts which causes delays of trains as a result of solving possible dispatching problems the network faces. The major challenge is for the train dispatchers to make the right decision and have reliable, cost effective and much more faster approaches needed to solve dispatching problems. This thesis work provides detail information on the implementation of different heuristic algorithms for train dispatchers in solving train dispatching problems. The library data files used are in xml file format and deals with both single and double tracks between main stations. The main objective of this work is to build different heuristic algorithms to solve unexpected delays faced by train dispatchers and to help in making right decisions on steps to take to have reliable and cost effective solution to the problems. These heuristics algorithms proposed were able to help dispatchers in making right decisions when solving train dispatching problems.

The development of wide-area networks and the availability of powerful computers as low-cost commodity components are changing the face of computation. These progresses in technology make it possible to utilize geographically distributed resources in multiple owner domains to solve large-scale problems in science, engineering and commerce. Research on this topic has led to the emergence of Grid computing. To achieve the promising potentials of tremendous distributed resources in the Grid, effective and efficient scheduling algorithms are fundamentally important. However, scheduling problems are well known for their intractability, and many of instances are in fact NP-Complete. The situation becomes even more challenging in the Grid circumstances due to some unique characteristics of the Grid. Scheduling algorithms in traditional parallel and distributed systems, which usually run on homogeneous and dedicated resources, cannot work well in the new environments. This work focuses on workflow scheduling algorithms in the Grid scenario. New challenges are discussed, previous research in this realm is surveyed, and novel heuristic algorithms addressing the challenges are proposed and tested. The proposed algorithms contribute to the literature by taking the following factors into account when a schedule for a DAG-based workflow is produced: predictable performance fluctuation and non-deterministic performance model of Grid resources, the computation and data staging co-scheduling, the clustering characteristic of Grid resource distribution, and the ability to reschedule according to performance change after the initial schedule is made. The performance of proposed algorithms are tested and analyzed by simulation under different workflow and resource configurations. / Thesis (Ph.D, Computing) -- Queen's University, 2009-04-23 22:30:09.646

No / In many production processes real time information may be obtained from process control computers and other monitoring systems, but most existing scheduling models are unable to use this information to effectively influence scheduling decisions in real time. In this paper we develop a general framework for using real time information to improve scheduling decisions, which allows us to trade off the quality of the revised schedule against the production disturbance which results from changing the planned schedule. We illustrate how our framework can be used to select a strategy for using real time information for a single machine scheduling model and discuss how it may be used to incorporate real time information into scheduling the complex production processes of steel continuous caster planning.

The goal of this thesis was to design and implement a graphical tool for visualization and editing of schedules which would provide a function for automatic repairing of violated constraints in the schedule. The resulting application called a Gantt Viewer is integrated to the FlowOpt project that represents a complex solution for modeling workflows, creation of schedules from them and analysis of these schedules. The application has been developed with the focus on intuitiveness of the user interface and performance during the management of large schedules. It enables the user to visualize extended manufacturing schedules thanks to the cooperation with other modules of the FlowOpt project. Moreover, the Gantt Viewer incorporates a repair tool exploiting a new Repair-DTP algorithm which is both introduced and demonstrated in this work.

Optimized Routing and Scheduling (RS) for mobile caregivers is essential for the efficient management of Home Health Care services. Unexpected events, such as traffic jams and visits lasting longer or shorter than expected, may affect the initial caregiver’s schedule by delaying or accelerating visits. Therefore, the RS should be continuously updated to deliver services that respect the problem constraints, e.g., patients’ and caregivers’ availability, caregivers’ breaks, etc., while minimizing the total costs of services. The services costs include travel, overtime, time exceeding patient time windows, and working time differences among caregivers. In this research, we formulate and solve a mixed-integer linear programming RS model that considers delays and earliness throughout the day. Once delays or earliness arise, we propose a rescheduling approach capable of updating the current schedule to consider the time difference and instantly provide a new optimal outcome. Results show a decrease in total costs in 48% of the cases, with an average saving of 349$ per day when rescheduling patients. 15% of the cases present an increase in total costs by an average of 143$ per day. No change is observed in 37% of the cases. Finally, when applying the rescheduling approach, results show that larger time windows provide more significant savings when delays are observed throughout the day.

En zone dense, la croissance actuelle du trafic sur les lignes ferroviaires suburbaines conduit les exploitants à déployer des systèmes de contrôle-commande avancés des trains, tels que les systèmes dits « CBTC » (Communication Based Train Control) jusque-là réservés aux systèmes de métro. Les systèmes CBTC mettent en œuvre un pilotage automatique des trains et permettent une amélioration significative des performances. Par ailleurs, ils peuvent inclure un module de supervision de la ligne en charge de réguler la marche des trains en cas d’aléa, améliorant ainsi la robustesse du trafic. Face au problème de régulation, la recherche opérationnelle a produit un certain nombre de méthodes permettant de répondre efficacement aux perturbations, d’une part dans le secteur métro et d’autre part dans le secteur ferroviaire lourd. En tirant profit de l’état de l’art et des avancées faites dans les deux secteurs, les travaux présentés dans ce manuscrit cherchent à contribuer à l’adaptation des fonctions de régulation des systèmes CBTC pour l’exploitation de lignes ferroviaires suburbaines. L’approche du problème débute par la construction de l’architecture fonctionnelle d’un module de supervision pour un système CBTC standard. Nous proposons ensuite une méthode de régulation basée sur une stratégie de commande prédictive et sur une optimisation multi-objectif des consignes des trains automatiques. Afin d’être en mesure d’évaluer précisément les performances d’une ligne ferroviaire suburbaine équipée d’un automatisme CBTC, il est nécessaire de s’équiper d’un outil de simulation microscopique adapté. Nous présentons dans ce manuscrit l’outil SNCF nommé SIMONE qui permet une simulation réaliste du point de vue fonctionnel et dynamique d’un système ferroviaire incluant un système CBTC. Les objectifs des travaux de thèse nous ont naturellement conduits à prendre part, avec l’équipe SNCF, à la spécification, à la conception et à l’implémentation de cet outil. Finalement, grâce à l’outil SIMONE, nous avons pu tester la méthode de régulation proposée sur des scénarios impliquant des perturbations. Afin d’évaluer la qualité des solutions, la méthode multi-objectif proposée a été comparée à une méthode de régulation individuelle basée sur une heuristique simple. La méthode de régulation multi-objectif propose de bonnes solutions au problème, dans la majorité des cas plus satisfaisantes que celles proposées par la régulation individuelle, et avec un temps de calcul jugé acceptable. Le manuscrit se termine par des perspectives de recherche intéressantes. / In high-density area, the demand for railway transportation is continuously increasing. Operating companies turn to new intelligent signaling and control systems, such as Communication Based Train Control (CBTC) systems previously deployed on underground systems only. CBTC systems operate trains in automatic pilot and lead to increase the line capacity without expensive modification of infrastructures. They can also include a supervision module in charge of adapting train behavior according to operating objectives and to disturbances, increasing line robustness. In the literature of real-time traffic management, various methods have been proposed to supervise and reschedule trains, on the one hand for underground systems, on the other hand for railway systems. Making the most of the state-of-the-art in both fields, the presented work intend to contribute to the design of supervision and rescheduling functions of CBTC systems operating suburban railway systems. Our approach starts by designing a supervision module for a standard CBTC system. Then, we propose a rescheduling method based on a model predictive control approach and a multi-objective optimization of automatic train commands. In order to evaluate the performances of a railway system, it is necessary to use a microscopic simulation tool including a CBTC model. In this thesis, we present the tool developed by SNCF and named SIMONE. It allows realistic simulation of a railway system and a CBTC system, in terms of functional architecture and dynamics. The presented work has been directly involved in the design and implementation of the tool. Eventually, the proposed rescheduling method was tested with the tool SIMONE on disturbed scenarios. The proposed method was compared to a simple heuristic strategy intending to recover delays. The proposed multi-objective method is able to provide good solutions to the rescheduling problem and over-performs the simple strategy in most cases, with an acceptable process time. We conclude with interesting perspectives for future work.

As computational grids have become popular and ubiquitous, users have access to large number and different types of geographically distributed grid resources. Many computational grid frameworks are composed of multiple distributed sites with each site consisting of one or more dedicated or non-dedicated clusters. Jobs submitted to a grid are handled by a matascheduler which interacts with the local schedulers of the clusters for scheduling jobs to the individual clusters. Computational grids have been found to be powerful research-beds for execution of various kinds of parallel applications. When a parallel application is submitted to a grid, the metascheduler has to choose a set of resources from a cluster for application execution. To select the best set of resources for application execution, it is important to determine the performance of the application. Accurate performance estimates of an application is essential in assisting a grid meta scheduler to efficiently schedule user jobs. Thus models that predict execution times of parallel applications on a set of resources and a search procedure (scheduling strategy) which selects the best set of machines within a cluster for application execution are of importance for enabling the parallel applications on grids. For efficient execution of large scientific parallel applications consisting of multiple phases, performance models of the individual phases should be obtained. Efficient rescheduling strategies that can use the per-phase models to adapt the parallel applications to application and resource dynamics are necessary for maintaining high performance of the applications on grids. A practical and robust grid computing infrastructure that integrates components related to application and resource monitoring, performance modeling, scheduling and rescheduling techniques, is highly essential for large-scale deployment and high performance of scientific applications on grid systems and hence for fostering high performance computing. This thesis focuses on developing performance models for predicting execution times of parallel problems/subproblems on dedicated and non-dedicated grid resources. The thesis also constructs robust scheduling and rescheduling strategies in a grid metascheduler that can use the performance models for efficient execution of large scientific parallel applications on dynamic grids. Finally, the thesis builds a practical and robust grid middleware infrastructure which integrates components related to performance modeling, scheduling and rescheduling, monitoring and migration frameworks for large-scale deployment and use of high performance applications on grids. The thesis consists of four main components. In the first part of the thesis, we have developed a comprehensive set of performance modeling strategies to predict the execution times of tightly-coupled parallel applications on a set of resources in a dedicated or non-dedicated cluster. The main purpose of our prediction strategies is to aid grid metaschedulers in making scheduling decisions. Our performance modeling strategies, based on linear regression, can deal with non-dedicated systems where the loads can change during application executions. Our models do not require detailed knowledge and instrumentation of the applications and can be constructed without the involvement of application developers. The strategies are intended for rapid and large scale deployment of parallel applications on non-dedicated grid systems. We have evaluated our strategies on 8, 16, 24 and 32-node clusters with random loads and load traces from a grid system. Our performance modeling strategies gave less than 30% average percentage prediction errors in all cases, which is reasonable for non-dedicated systems. We also found that scheduling based on the predictions by our strategies will result in perfect scheduling in many cases. For modeling large-scale scientific applications, we use execution profiles and automatic program analysis, and manual analysis of significant portions of the application’s code to identify the different phases of applications. We then adopt our performance modeling strategies to predict execution times for the different phases of the tightly-coupled parallel applications on a set of resources in a dedicated or non-dedicated cluster. Our experiments show that using combinations of performance models of the phases give 18% – 70% more accurate predictions than using single performance models for the applications. In the second part of the thesis, we have devised, evaluated and compared algorithms for scheduling tightly-coupled parallel applications on multi-cluster grids. Our algorithms use performance models that predict the execution times of parallel applications, for evaluations of candidate schedules. In this work, we propose a novel algorithm called Box Elimination (BE) that searches a space of performance model parameters to determine efficient schedules. By eliminating large search space regions containing poorer solutions at each step and searching high quality solutions, our algorithm is able to generate efficient schedules within few seconds for even clusters of 512 processors. By means of large number of real and simulation experiment, we compared our algorithm with popular optimization techniques. We show that our algorithm generates up to 80% more efficient schedules than other algorithms and the resulting execution times are more robust against performance modeling errors. The third part of the thesis deals with policies for rescheduling long-running multi-phase parallel applications in response to application and resource dynamics. In this work, we use our performance modeling and scheduling strategies to derive rescheduling plans for executing multi-phase parallel applications on grids. A rescheduling plan consists of potential points in application execution for rescheduling and schedules of resources for application execution between two consecutive rescheduling points. We have developed three algorithms, namely an incremental algorithm, a divide-and-conquer algorithm and a genetic algorithm, for deriving a rescheduling plan for a parallel application execution. We have also developed an algorithm that uses rescheduling plans derived on different clusters to form a single coherent rescheduling plan for application execution on a grid consisting of multiple clusters. The rescheduling plans generated by our algorithms are highly efficient leading to application execution times that are higher than the execution times corresponding to brute force method by less than 10%. We also find that rescheduling in response to changing application and resource dynamics, using the rescheduling plans for multi-cluster grids generated by our algorithms, give much lesser execution times when compared to executions of the applications on a single schedule throughout application execution. In the final part of the thesis, we have developed a practical grid middleware framework called MerITA (Middleware for Performance Improvement of Tightly Coupled Parallel Applications on Grids), a system for effective execution of tightly-coupled parallel applications on multi-cluster grids consisting of dedicated or non-dedicated, interactive or batch systems. The framework brings together performance modeling for automatically determining the characteristics of parallel applications, scheduling strategies that use the performance models for efficient mapping of applications to resources, rescheduling policies for determining the points in application execution when executing applications can be rescheduled to different sets of resources to obtain performance improvement and a check-pointing library for enabling rescheduling.