Scalable dimensioning of resilient Lambda Grids

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A parallel simulated annealing (PSA) for solving project scheduling problem with discounted cash flow policy in pricing strategy of the project suppliers

Problem programiranja projekta s ograničenim sredstvima u literaturi je poznat kao NP-Hard problem. U ovom istraživanju prvi se puta predlaže politika diskontne solventnosti za rješavanje problema programiranja projekta s ograničenim sredstvima. U klasičnim modelima pretpostavlja se da je cijena potrebnih resursa za izvršenje aktivnosti fiksna i resursi se mogu pripremiti samo po jednoj cijeni na tržištu. Problem je usmjeren na određivanje optimalnog polaznog vremena aktivnosti projekta uzimajući u obzir ograničenja prioriteta i dostupne resurse u svrhu skraćenja vremena završetka projekta. Kako bi se riješio predloženi model predlaže se hibridni algoritam zasnovan na dva algoritma, genetskom i simuliranog žarenja. U toj metodi genetski algoritam služi kao glavni okvir predložene metode a metoda simuliranog žarenja kao novi operater i u svrhu poboljšanja lokalnog pretraživanja glavnog algoritma. Budući da vrijednosti parametara znatno utječu na učinkovitost tih algoritama, daje se novi statistički pristup temeljen na stepenastoj regresiji (stepwise regression) za postavljanje parametara predloženih algoritama. Rezultati proračuna pokazuju visoku učinkovitost predloženog algoritma u odnosu na vrijeme donošenja rješenja i optimalnih rješenja.Resource-constrained project scheduling problem is known as a NP-Hard problem in literature. In this research, discounted cash flow policy is suggested for the resources-constrained project scheduling problem for the first time while in classic models, it has been assumed that price of the required resources is fixed for performing the activities and resources can be prepared only with one price rate in the market. Goal of this problem is to determine optimal starting time of the project activities considering precedence constraints and the available resources such that the project completion time can be minimized. In order to solve the proposed model, a hybrid algorithm based on two algorithms i.e. genetic and simulated annealing has been suggested. In this method, genetic algorithm has been designed as the main framework of the proposed method and simulated annealing method as a new operator and in order to improve local search of the main algorithm. Since values of the parameters have considerable effect on efficiency of these algorithms, therefore, a new statistical approach based on the stepwise regression has been presented to set the proposed algorithms parameters. Results of the calculations show high efficiency of proposed algorithm in terms of solution time and optimal solutions

Large-scale mixed integer optimization approaches for scheduling airline operations under irregularity

Perhaps no single industry has benefited more from advancements in computation, analytics, and optimization than the airline industry. Operations Research (OR) is now ubiquitous in the way airlines develop their schedules, price their itineraries, manage their fleet, route their aircraft, and schedule their crew. These problems, among others, are well-known to industry practitioners and academics alike and arise within the context of the planning environment which takes place well in advance of the date of departure. One salient feature of the planning environment is that decisions are made in a frictionless environment that do not consider perturbations to an existing schedule. Airline operations are rife with disruptions caused by factors such as convective weather, aircraft failure, air traffic control restrictions, network effects, among other irregularities. Substantially less work in the OR community has been examined within the context of the real-time operational environment. While problems in the planning and operational environments are similar from a mathematical perspective, the complexity of the operational environment is exacerbated by two factors. First, decisions need to be made in as close to real-time as possible. Unlike the planning phase, decision-makers do not have hours of time to return a decision. Secondly, there are a host of operational considerations in which complex rules mandated by regulatory agencies like the Federal Administration Association (FAA), airline requirements, or union rules. Such restrictions often make finding even a feasible set of re-scheduling decisions an arduous task, let alone the global optimum. The goals and objectives of this thesis are found in Chapter 1. Chapter 2 provides an overview airline operations and the current practices of disruption management employed at most airlines. Both the causes and the costs associated with irregular operations are surveyed. The role of airline Operations Control Center (OCC) is discussed in which serves as the real-time decision making environment that is important to understand for the body of this work. Chapter 3 introduces an optimization-based approach to solve the Airline Integrated Recovery (AIR) problem that simultaneously solves re-scheduling decisions for the operating schedule, aircraft routings, crew assignments, and passenger itineraries. The methodology is validated by using real-world industrial data from a U.S. hub-and-spoke regional carrier and we show how the incumbent approach can dominate the incumbent sequential approach in way that is amenable to the operational constraints imposed by a decision-making environment. Computational effort is central to the efficacy of any algorithm present in a real-time decision making environment such as an OCC. The latter two chapters illustrate various methods that are shown to expedite more traditional large-scale optimization methods that are applicable a wide family of optimization problems, including the AIR problem. Chapter 4 shows how delayed constraint generation and column generation may be used simultaneously through use of alternate polyhedra that verify whether or not a given cut that has been generated from a subset of variables remains globally valid. While Benders' decomposition is a well-known algorithm to solve problems exhibiting a block structure, one possible drawback is slow convergence. Expediting Benders' decomposition has been explored in the literature through model reformulation, improving bounds, and cut selection strategies, but little has been studied how to strengthen a standard cut. Chapter 5 examines four methods for the convergence may be accelerated through an affine transformation into the interior of the feasible set, generating a split cut induced by a standard Benders' inequality, sequential lifting, and superadditive lifting over a relaxation of a multi-row system. It is shown that the first two methods yield the most promising results within the context of an AIR model.PhDCommittee Co-Chair: Clarke, John-Paul; Committee Co-Chair: Johnson, Ellis; Committee Member: Ahmed, Shabbir; Committee Member: Clarke, Michael; Committee Member: Nemhauser, Georg

Loosely Coupled Formulations for Automated Planning: An Integer Programming Perspective

We represent planning as a set of loosely coupled network flow problems, where each network corresponds to one of the state variables in the planning domain. The network nodes correspond to the state variable values and the network arcs correspond to the value transitions. The planning problem is to find a path (a sequence of actions) in each network such that, when merged, they constitute a feasible plan. In this paper we present a number of integer programming formulations that model these loosely coupled networks with varying degrees of flexibility. Since merging may introduce exponentially many ordering constraints we implement a so-called branch-and-cut algorithm, in which these constraints are dynamically generated and added to the formulation when needed. Our results are very promising, they improve upon previous planning as integer programming approaches and lay the foundation for integer programming approaches for cost optimal planning

Comparing the Performance of Expert User Heuristics and an Integer Linear Program in Aircraft Carrier Deck Operations

Planning operations across a number of domains can be considered as resource allocation problems with timing constraints. An unexplored instance of such a problem domain is the aircraft carrier flight deck, where, in current operations, replanning is done without the aid of any computerized decision support. Rather, veteran operators employ a set of experience based heuristics to quickly generate new operating schedules. These expert user heuristics are neither codified nor evaluated by the United States Navy; they have grown solely from the convergent experiences of supervisory staff. As unmanned aerial vehicles (UAVs) are introduced in the aircraft carrier domain, these heuristics may require alterations due to differing capabilities. The inclusion of UAVs also allows for new opportunities for on-line planning and control, providing an alternative to the current heuristic-based replanning methodology. To investigate these issues formally, we have developed a decision support system for flight deck operations that utilizes a conventional integer linear program-based planning algorithm. In this system, a human operator sets both the goals and constraints for the algorithm, which then returns a proposed schedule for operator approval. As a part of validating this system, the performance of this collaborative human–automation planner was compared with that of the expert user heuristics over a set of test scenarios. The resulting analysis shows that human heuristics often outperform the plans produced by an optimization algorithm, but are also often more conservative

Intermodal Transfer Coordination in Logistic Networks

Increasing awareness that globalization and information technology affect the patterns of transport and logistic activities has increased interest in the integration of intermodal transport resources. There are many significant advantages provided by integration of multiple transport schedules, such as: (1) Eliminating direct routes connecting all origin-destinations pairs and concentrating cargos on major routes; (2) improving the utilization of existing transportation infrastructure; (3) reducing the requirements for warehouses and storage areas due to poor connections, and (4) reducing other impacts including traffic congestion, fuel consumption and emissions. This dissertation examines a series of optimization problems for transfer coordination in intermodal and intra-modal logistic networks. The first optimization model is developed for coordinating vehicle schedules and cargo transfers at freight terminals, in order to improve system operational efficiency. A mixed integer nonlinear programming problem (MINLP) within the studied multi-mode, multi-hub, and multi-commodity network is formulated and solved by using sequential quadratic programming (SQP), genetic algorithms (GA) and a hybrid GA-SQP heuristic algorithm. This is done primarily by optimizing service frequencies and slack times for system coordination, while also considering loading and unloading, storage and cargo processing operations at the transfer terminals. Through a series of case studies, the model has shown its ability to optimize service frequencies (or headways) and slack times based on given input information. The second model is developed for countering schedule disruptions within intermodal freight systems operating in time-dependent, stochastic and dynamic environments. When routine disruptions occur (e.g. traffic congestion, vehicle failures or demand fluctuations) in pre-planned intermodal timed-transfer systems, the proposed dispatching control method determines through an optimization process whether each ready outbound vehicle should be dispatched immediately or held waiting for some late incoming vehicles with connecting freight. An additional sub-model is developed to deal with the freight left over due to missed transfers. During the phases of disruption responses, alleviations and management, the proposed real-time control model may also consider the propagation of delays at further downstream terminals. For attenuating delay propagations, an integrated dispatching control model and an analysis of sensitivity to slack times are presented

MODELS AND SOLUTION ALGORITHMS FOR EQUITABLE RESOURCE ALLOCATION IN AIR TRAFFIC FLOW MANAGEMENT

Population growth and economic development lead to increasing demand for travel and pose mobility challenges on capacity-limited air traffic networks. The U.S. National Airspace System (NAS) has been operated near the capacity, and air traffic congestion is expected to remain as a top concern for the related system operators, passengers and airlines. This dissertation develops a number of model reformulations and efficient solution algorithms to address resource allocation problems in air traffic flow management, while explicitly accounting for equitable objectives in order to encourage further collaborations by different stakeholders. This dissertation first develops a bi-criteria optimization model to offload excess demand from different competing airlines in the congested airspace when the predicted traffic demand is higher than available capacity. Computationally efficient network flow models with side constraints are developed and extensively tested using datasets obtained from the Enhanced Traffic Management System (ETMS) database (now known as the Traffic Flow Management System). Representative Pareto-optimal tradeoff frontiers are consequently generated to allow decision-makers to identify best-compromising solutions based on relative weights and systematical considerations of both efficiency and equity. This dissertation further models and solves an integrated flight re-routing problem on an airspace network. Given a network of airspace sectors with a set of waypoint entries and a set of flights belonging to different air carriers, the optimization model aims to minimize the total flight travel time subject to a set of flight routing equity, operational and safety requirements. A time-dependent network flow programming formulation is proposed with stochastic sector capacities and rerouting equity for each air carrier as side constraints. A Lagrangian relaxation based method is used to dualize these constraints and decompose the original complex problem into a sequence of single flight rerouting/scheduling problems. Finally, within a multi-objective utility maximization framework, the dissertation proposes several practically useful heuristic algorithms for the long-term airport slot assignment problem. Alternative models are constructed to decompose the complex model into a series of hourly assignment sub-problems. A new paired assignment heuristic algorithm is developed to adapt the round robin scheduling principle for improving fairness measures across different airlines. Computational results are presented to show the strength of each proposed modeling approach

Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System

Practical design of optimal wireless metropolitan area networks: model and algorithms for OFDMA networks

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Ph.D.This thesis contributes to the study of the planning and optimisation of wireless metropolitan area networks, in particular to the access network design of OFDMAbased systems, where different parameters like base station position, antenna tilt and azimuth need to be configured during the early stages of the network life. A practical view for the solution of this problem is presented by means of the development of a novel design framework and the use of multicriteria optimisation. A further consideration of relaying and cooperative communications in the context of the design of this kind of networks is done, an area little researched. With the emergence of new technologies and services, it is very important to accurately identify the factors that affect the design of the wireless access network and define how to take them into account to achieve optimally performing and cost-efficient networks. The new features and flexibility of OFDMA networks seem particularly suited to the provision of different broadband services to metropolitan areas. However, until now, most existing efforts have been focused on the basic access capability networks. This thesis presents a way to deal with the trade-offs generated during the OFDMA access network design, and presents a service-oriented optimization framework that offers a new perspective for this process with consideration of the technical and economic factors. The introduction of relay stations in wireless metropolitan area networks will bring numerous advantages such as coverage extension and capacity enhancement due to the deployment of new cells and the reduction of distance between transmitter and receiver. However, the network designers will also face new challenges with the use of relay stations, since they involve a new source of interference and a complicated air interface; and this need to be carefully evaluated during the network design process. Contrary to the well known procedure of cellular network design over regular or hexagonal scenarios, the wireless network planning and optimization process aims to deal with the non-uniform characteristics of realistic scenarios, where the existence of hotspots, different channel characteristics for the users, or different service requirements will determine the final design of the wireless network. This thesis is structured in three main blocks covering important gaps in the existing literature in planning (efficient simulation) and optimisation. The formulation and ideas proposed in the former case can still be evaluated over regular scenarios, for the sake of simplicity, while the study of latter case needs to be done over specific scenarios that will be described when appropriate. Nevertheless, comments and conclusions are extrapolated to more general cases throughout this work. After an introduction and a description of the related work, this thesis first focuses on the study of models and algorithms for classical point-to-multipoint networks on Chapter 3, where the optimisation framework is proposed. Based on the framework, this work: - Identifies the technology-specific physical factors that affect most importantly the network system level simulation, planning and optimization process. - It demonstrates how to simplify the problem and translate it into a formal optimization routine with consideration of economic factors. - It provides the network provider, a detailed and clear description of different scenarios during the design process so that the most suitable solution can be found. Existing works on this area do not provide such a comprehensive framework. In Chapter 4: - The impact of the relay configuration on the network planning process is analysed. - A new simple and flexible scheme to integrate multihop communications in the Mobile WiMAX frame structure is proposed and evaluated. - Efficient capacity calculations that allow intensive system level simulations in a multihop environment are introduced. In Chapter 5: - An analysis of the optimisation procedure with the addition of relay stations and the derived higher complexity of the process is done. - A frequency plan procedure not found in the existing literature is proposed, which combines it with the use of the necessary frame fragmentation of in-band relay communications and cooperative procedures. - A novel joint two-step process for network planning and optimisation is proposed. Finally, conclusions and open issues are exposed