Mathematical Optimization for the Train Timetabling Problem

AMS Subj. Classification: 90C57; 90C10;

Solving Maximum Clique Problem for Protein Structure Similarity

Computing the similarity between two protein structures is a crucial task in molecular biology, and has been extensively investigated. Many protein structure comparison methods can be modeled as maximum weighted clique problems in specific k-partite graphs, referred here as alignment graphs. In this paper we present both a new integer programming formulation for solving such clique problems and a dedicated branch and bound algorithm for solving the maximum cardinality clique problem. Both approaches have been integrated in VAST, a software for aligning protein 3D structures largely used in the National Center for Biotechnology Information, an original clique solver which uses the well known Bron and Kerbosch algorithm (BK). Our computational results on real protein alignment instances show that our branch and bound algorithm is up to 116 times faster than BK.

A Fast Algorithm for Weber Problem on a Grid

AMS subject classification: 90B80.

Cost-effective node monitoring for online hot event detection in sina weibo microblogging

We propose a cost-effective hot event detection system over Sina Weibo platform, currently the dominant microblogging service provider in China. The problem of finding a proper subset of microbloggers under resource constraints is formulated as a mixed-integer problem for which heuristic algorithms are developed to compute approximate solution. Preliminary results show that by tracking about 500 out of 1.6 million candidate microbloggers and processing 15,000 microposts daily, 62% of the hot events can be detected five hours on average earlier than they are published by Weibo.

Calculus of Variations with Classical and Fractional Derivatives

MSC 2010: 49K05, 26A33

CVaR minimization by the SRA algorithm

Using the risk measure CV aR in �nancial analysis has become more and more popular recently. In this paper we apply CV aR for portfolio optimization. The problem is formulated as a two-stage stochastic programming model, and the SRA algorithm, a recently developed heuristic algorithm, is applied for minimizing CV aR.

CVaR számítás SRA algoritmussal (CVaR minimization by the SRA algorithm)

A CV aR kockázati mérték egyre nagyobb jelentőségre tesz szert portfóliók kockázatának megítélésekor. A portfolió egészére a CVaR kockázati mérték minimalizálását meg lehet fogalmazni kétlépcsős sztochasztikus feladatként. Az SRA algoritmus egy mostanában kifejlesztett megoldó algoritmus sztochasztikus programozási feladatok optimalizálására. Ebben a cikkben az SRA algoritmussal oldottam meg CV aR kockázati mérték minimalizálást. ___________ The risk measure CVaR is becoming more and more popular in recent years. In this paper we use CVaR for portfolio optimization. We formulate the problem as a two-stage stochastic programming model. We apply the SRA algorithm, which is a recently developed heuristic algorithm, to minimizing CVaR.

Algorithms for scheduling parallel batch processing machines with non-identical job ready times

This research is motivated by a practical application observed at a printed circuit board (PCB) manufacturing facility. After assembly, the PCBs (or jobs) are tested in environmental stress screening (ESS) chambers (or batch processing machines) to detect early failures. Several PCBs can be simultaneously tested as long as the total size of all the PCBs in the batch does not violate the chamber capacity. PCBs from different production lines arrive dynamically to a queue in front of a set of identical ESS chambers, where they are grouped into batches for testing. Each line delivers PCBs that vary in size and require different testing (or processing) times. Once a batch is formed, its processing time is the longest processing time among the PCBs in the batch, and its ready time is given by the PCB arriving last to the batch. ESS chambers are expensive and a bottleneck. Consequently, its makespan has to be minimized. ^ A mixed-integer formulation is proposed for the problem under study and compared to a formulation recently published. The proposed formulation is better in terms of the number of decision variables, linear constraints and run time. A procedure to compute the lower bound is proposed. For sparse problems (i.e. when job ready times are dispersed widely), the lower bounds are close to optimum. ^ The problem under study is NP-hard. Consequently, five heuristics, two metaheuristics (i.e. simulated annealing (SA) and greedy randomized adaptive search procedure (GRASP)), and a decomposition approach (i.e. column generation) are proposed—especially to solve problem instances which require prohibitively long run times when a commercial solver is used. Extensive experimental study was conducted to evaluate the different solution approaches based on the solution quality and run time. ^ The decomposition approach improved the lower bounds (or linear relaxation solution) of the mixed-integer formulation. At least one of the proposed heuristic outperforms the Modified Delay heuristic from the literature. For sparse problems, almost all the heuristics report a solution close to optimum. GRASP outperforms SA at a higher computational cost. The proposed approaches are viable to implement as the run time is very short. ^

Realization of Differentiated Quality of Service for Wideband Code Division Multiple Access Core Network

The development of 3G (the 3rd generation telecommunication) value-added services brings higher requirements of Quality of Service (QoS). Wideband Code Division Multiple Access (WCDMA) is one of three 3G standards, and enhancement of QoS for WCDMA Core Network (CN) becomes more and more important for users and carriers. The dissertation focuses on enhancement of QoS for WCDMA CN. The purpose is to realize the DiffServ (Differentiated Services) model of QoS for WCDMA CN. Based on the parallelism characteristic of Network Processors (NPs), the NP programming model is classified as Pool of Threads (POTs) and Hyper Task Chaining (HTC). In this study, an integrated programming model that combines both of the two models was designed. This model has highly efficient and flexible features, and also solves the problems of sharing conflicts and packet ordering. We used this model as the programming model to realize DiffServ QoS for WCDMA CN. ^ The realization mechanism of the DiffServ model mainly consists of buffer management, packet scheduling and packet classification algorithms based on NPs. First, we proposed an adaptive buffer management algorithm called Packet Adaptive Fair Dropping (PAFD), which takes into consideration of both fairness and throughput, and has smooth service curves. Then, an improved packet scheduling algorithm called Priority-based Weighted Fair Queuing (PWFQ) was introduced to ensure the fairness of packet scheduling and reduce queue time of data packets. At the same time, the delay and jitter are also maintained in a small range. Thirdly, a multi-dimensional packet classification algorithm called Classification Based on Network Processors (CBNPs) was designed. It effectively reduces the memory access and storage space, and provides less time and space complexity. ^ Lastly, an integrated hardware and software system of the DiffServ model of QoS for WCDMA CN was proposed. It was implemented on the NP IXP2400. According to the corresponding experiment results, the proposed system significantly enhanced QoS for WCDMA CN. It extensively improves consistent response time, display distortion and sound image synchronization, and thus increases network efficiency and saves network resource.^

Minimizing makespan for hybrid flowshops with batch, discrete processing machines and arbitrary job sizes

This research aims at a study of the hybrid flow shop problem which has parallel batch-processing machines in one stage and discrete-processing machines in other stages to process jobs of arbitrary sizes. The objective is to minimize the makespan for a set of jobs. The problem is denoted as: FF: batch1,sj:Cmax. The problem is formulated as a mixed-integer linear program. The commercial solver, AMPL/CPLEX, is used to solve problem instances to their optimality. Experimental results show that AMPL/CPLEX requires considerable time to find the optimal solution for even a small size problem, i.e., a 6-job instance requires 2 hours in average. A bottleneck-first-decomposition heuristic (BFD) is proposed in this study to overcome the computational (time) problem encountered while using the commercial solver. The proposed BFD heuristic is inspired by the shifting bottleneck heuristic. It decomposes the entire problem into three sub-problems, and schedules the sub-problems one by one. The proposed BFD heuristic consists of four major steps: formulating sub-problems, prioritizing sub-problems, solving sub-problems and re-scheduling. For solving the sub-problems, two heuristic algorithms are proposed; one for scheduling a hybrid flow shop with discrete processing machines, and the other for scheduling parallel batching machines (single stage). Both consider job arrival and delivery times. An experiment design is conducted to evaluate the effectiveness of the proposed BFD, which is further evaluated against a set of common heuristics including a randomized greedy heuristic and five dispatching rules. The results show that the proposed BFD heuristic outperforms all these algorithms. To evaluate the quality of the heuristic solution, a procedure is developed to calculate a lower bound of makespan for the problem under study. The lower bound obtained is tighter than other bounds developed for related problems in literature. A meta-search approach based on the Genetic Algorithm concept is developed to evaluate the significance of further improving the solution obtained from the proposed BFD heuristic. The experiment indicates that it reduces the makespan by 1.93 % in average within a negligible time when problem size is less than 50 jobs.

Scaling geospatial searches in large spatial databases

Modern geographical databases, which are at the core of geographic information systems (GIS), store a rich set of aspatial attributes in addition to geographic data. Typically, aspatial information comes in textual and numeric format. Retrieving information constrained on spatial and aspatial data from geodatabases provides GIS users the ability to perform more interesting spatial analyses, and for applications to support composite location-aware searches; for example, in a real estate database: “Find the nearest homes for sale to my current location that have backyard and whose prices are between $50,000 and $80,000”. Efficient processing of such queries require combined indexing strategies of multiple types of data. Existing spatial query engines commonly apply a two-filter approach (spatial filter followed by nonspatial filter, or viceversa), which can incur large performance overheads. On the other hand, more recently, the amount of geolocation data has grown rapidly in databases due in part to advances in geolocation technologies (e.g., GPS-enabled smartphones) that allow users to associate location data to objects or events. The latter poses potential data ingestion challenges of large data volumes for practical GIS databases. In this dissertation, we first show how indexing spatial data with R-trees (a typical data pre-processing task) can be scaled in MapReduce—a widely-adopted parallel programming model for data intensive problems. The evaluation of our algorithms in a Hadoop cluster showed close to linear scalability in building R-tree indexes. Subsequently, we develop efficient algorithms for processing spatial queries with aspatial conditions. Novel techniques for simultaneously indexing spatial with textual and numeric data are developed to that end. Experimental evaluations with real-world, large spatial datasets measured query response times within the sub-second range for most cases, and up to a few seconds for a small number of cases, which is reasonable for interactive applications. Overall, the previous results show that the MapReduce parallel model is suitable for indexing tasks in spatial databases, and the adequate combination of spatial and aspatial attribute indexes can attain acceptable response times for interactive spatial queries with constraints on aspatial data.

A decision support framework for infrastructure maintenance investment decision-making

Infrastructure management agencies are facing multiple challenges, including aging infrastructure, reduction in capacity of existing infrastructure, and availability of limited funds. Therefore, decision makers are required to think innovatively and develop inventive ways of using available funds. Maintenance investment decisions are generally made based on physical condition only. It is important to understand that spending money on public infrastructure is synonymous with spending money on people themselves. This also requires consideration of decision parameters, in addition to physical condition, such as strategic importance, socioeconomic contribution and infrastructure utilization. Consideration of multiple decision parameters for infrastructure maintenance investments can be beneficial in case of limited funding. Given this motivation, this dissertation presents a prototype decision support framework to evaluate trade-off, among competing infrastructures, that are candidates for infrastructure maintenance, repair and rehabilitation investments. Decision parameters' performances measured through various factors are combined to determine the integrated state of an infrastructure using Multi-Attribute Utility Theory (MAUT). The integrated state, cost and benefit estimates of probable maintenance actions are utilized alongside expert opinion to develop transition probability and reward matrices for each probable maintenance action for a particular candidate infrastructure. These matrices are then used as an input to the Markov Decision Process (MDP) for the finite-stage dynamic programming model to perform project (candidate)-level analysis to determine optimized maintenance strategies based on reward maximization. The outcomes of project (candidate)-level analysis are then utilized to perform network-level analysis taking the portfolio management approach to determine a suitable portfolio under budgetary constraints. The major decision support outcomes of the prototype framework include performance trend curves, decision logic maps, and a network-level maintenance investment plan for the upcoming years. The framework has been implemented with a set of bridges considered as a network with the assistance of the Pima County DOT, AZ. It is expected that the concept of this prototype framework can help infrastructure management agencies better manage their available funds for maintenance.

A simulation-based heuristic for fleet assignment

Integer programming, simulation, and rules of thumb have been integrated to develop a simulation-based heuristic for short-term assignment of fleet in the car rental industry. It generates a plan for car movements, and a set of booking limits to produce high revenue for a given planning horizon. Three different scenarios were used to validate the heuristic. The heuristic's mean revenue was significant higher than the historical ones, in all three scenarios. Time to run the heuristic for each experiment was within the time limits of three hours set for the decision making process even though it is not fully automated. These findings demonstrated that the heuristic provides better plans (plans that yield higher profit) for the dynamic allocation of fleet than the historical decision processes. Another contribution of this effort is the integration of IP and rules of thumb to search for better performance under stochastic conditions.

High-performance parallel systems for data-intensive computing

Thesis (Ph.D.)--University of Washington, 2016-08

Parallelisation of a 3-D electromagnetic scattering code

A high-frequency time domain finite element scattering code using a combination of edge and piecewise constant elements on unstructured tetrahedral meshes is described. A comparison of computation with theory is given for scattering from a sphere. A parallel implementation making use of the bulk synchronous parallel (BSP) programming model is described in detail; a BSP performance model of the parallelized field calculation is derived and compared to timing measurements on up to 128 processors on a Cray T3D.