492,909 research outputs found
Editorial for the Special Issue: "Novel Solutions and Novel Approaches in Operational Research"
This special issue of Business Systems Research (SI of the BSR) is being co-published by the Slovenian Society INFORMATIKA – Section for Operational Research (SSI -SOR). It focuses on recent advances in Operations Research and Management Science (OR / MS), with a particular emphasis on linking OR / MS with other areas of quantitative and qualitative methods in the context of a multidisciplinary framework. The ten papers that were chosen for this Special Issue of the BSR present advancements and new techniques (methodology) in the field of Operations Research (OR), as well as their application in a variety of fields, including risk management, mathematical programming, game theory, gravity, spatial analysis, logistics, circular economy, continuous improvement, sustainability, e-commerce, forecasting, Gaussian processes, linear regression, multi-layer perceptron, and machine learning
Methods for Improving Robustness and Recovery in Aviation Planning.
In this dissertation, we develop new methods for improving robustness and recovery in aviation planning. In addition to these methods, the contributions of this dissertation include an in-depth analysis of several mathematical modeling approaches and proof of their structural equivalence. Furthermore, we analyze several decomposition approaches, the difference in their complexity and the required computation time to provide insight into selecting the most appropriate formulation for a particular problem structure. To begin, we provide an overview of the airline planning process, including the major components such as schedule planning, fleet assignment and crew planning approaches. Then, in the first part of our research, we use a recursive simulation-based approach to evaluate a flight schedule's overall robustness, i.e. its ability to withstand propagation delays. We then use this analysis as the groundwork for a new approach to improve the robustness of an airline's maintenance plan. Specifically, we improve robustness by allocating maintenance rotations to those aircraft that will most likely benefit from the assignment. To assess the effectiveness of our approach, we introduce a new metric, maintenance reachability, which measures the robustness of the rotations assigned to aircraft. Subsequently, we develop a mathematical programming approach to improve the maintenance reachability of this assignment. In the latter part of this dissertation, we transition from the planning to the recovery phase. On the day-of-operations, disruptions often take place and change aircraft rotations and their respective maintenance assignments. In recovery, we focus on creating feasible plans after such disruptions have occurred. We divide our recovery approach into two phases. In the first phase, we solve the Maintenance Recovery Problem (MRP), a computationally complex, short-term, non-recurrent recovery problem. This research lays the foundation for the second phase, in which we incorporate recurrence, i.e. the property that scheduling one maintenance event has a direct implication on the deadlines for subsequent maintenance events, into the recovery process. We recognize that scheduling the next maintenance event provides implications for all subsequent events, which further increases the problem complexity. We illustrate the effectiveness of our methods under various objective functions and mathematical programming approaches.Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91539/1/mlapp_1.pd
Software development for forest growth models and management. CORKFITS: web based growth simulator
New methods of forest management and the study of their impact on sustainability are strongly dependent on realistic mathematical modelling. The complexity of the models however, makes the use of computational power, and thus the incorporation of knowledge from computer science and research, indispensable. In this paper we wish to demonstrate the development of a simulator for the growth and production of cork oak woodlands – montados. The software is divided into three sub-modules, sharing a common core, with functions and mathematical operations. The desktop client allows for repeated operations for more intense calculations, and statistical operations for modelling purposes. The web version is intended to be used by final users in forest practice. It permits simulation of inventory data based on individual tree measurements, and inventory data based on plot description with a reduced amount of detail (number of trees per ha, diameter structure, etc.) The last module allows the incorporation of the cork model into other software by means of SOAP protocol, via web services. It conforms to the WS-I Basic Profile 1.1, to ensure interoperability among the largest number of clients. This module allows other developers to use the cork oak growth-model in their software, and the developers from other areas of expertise (management optimisation, decision support...) have the opportunity to test their techniques on real stands, with the most recently-updated model versions
Variant-oriented Planning Models for Parts/Products Grouping, Sequencing and Operations
This research aims at developing novel methods for utilizing the commonality between part/product variants to make modern manufacturing systems more flexible, adaptable, and agile for dealing with less volume per variant and minimizing total changes in the setup between variants. Four models are developed for use in four important domains of manufacturing systems: production sequencing, product family formation, production flow, and products operations sequences retrieval. In all these domains, capitalizing on commonality between the part/product variants has a pivotal role. For production sequencing; a new policy based on setup similarity between product variants is proposed and its results are compared with a developed mathematical model in a permutation flow shop. The results show the proposed algorithm is capable of finding solutions in less than 0.02 seconds with an average error of 1.2%. For product family formation; a novel operation flow based similarity coefficient is developed for variants having networked structures and integrated with two other similarity coefficients, operation and volume similarity, to provide a more comprehensive similarity coefficient. Grouping variants based on the proposed integrated similarity coefficient improves changeover time and utilization of the system. A sequencing method, as a secondary application of this approach, is also developed. For production flow; a new mixed integer programing (MIP) model is developed to assign operations of a family of product variants to candidate machines and also to select the best place for each machine among the candidate locations. The final sequence of performing operations for each variant having networked structures is also determined. The objective is to minimize the total backtracking distance leading to an improvement in total throughput of the system (7.79% in the case study of three engine blocks). For operations sequences retrieval; two mathematical models and an algorithm are developed to construct a master operation sequence from the information of the existing variants belonging to a family of parts/products. This master operation sequence is used to develop the operation sequences for new variants which are sufficiently similar to existing variants. Using the proposed algorithm decreases time of developing the operations sequences of new variants to the seconds
Improving the Performance of the Distributed File System through Anticipated Parallel Processing
In the emerging Big Data scenario, distributed File systems (DFSs) are used for storing and accessing information in a scalable manner. Many cloud computing systems use DFS as the main storage component. The Big Data applications de-ployed in cloud computing systems more frequently perform read operations and less frequently the write operations. So, improving the performance of read access has become an im-portant research issue in DFS. In the literature, many client side caching with appropriate pre fetching techniques are proposed for improving the performance read access in the DFS. A speculation-based approach which uses client side caching is also proposed in the literature for improving the performance of read access in the DFS. In this paper, we have proposed a new read algorithm for the DFS based on anticipated parallel processing. We have evaluated the per- formance of the proposed algorithm using mathematical and simulation methods and the results indicate that the pro-posed algorithm performs better than the speculation-based algorithm proposed in the literature
New Sequential and Parallel Division Free Methods for Determinant of Matrices
A determinant plays an important role in many applications of linear algebra. Finding determinants using non division free methods will encounter problems if entries of matrices
are represented in rational or polynomial expressions, and also when floating point errors arise. To overcome this problem, division free methods are used instead. The
two commonly used division free methods for finding determinant are cross multiplication and cofactor expansion. However, cross multiplication which uses the Sarrus Rule only works for matrices of order less or equal to three, whereas cofactor expansion requires lengthy and tedious computation when dealing with large matrices. This research, therefore, attempts to develop new sequential and parallel methods for finding determinants of matrices. The research also aims to generalise the Sarrus Rule for any order of square matrices based on permutations which are derived using starter sets. Two strategies were introduced to generate distinct starter sets namely the circular and the exchanging of two elements operations. Some theoretical works and mathematical properties for generating permutation and determining determinants were also constructed to support the research. Numerical results indicated that the new proposed methods performed better
than the existing methods in term of computation times. The computation times in the newly developed sequential methods were dominated by generating starter sets. Therefore, two parallel strategies were developed to parallelise this algorithm so as to reduce the computation times. Numerical results showed that the parallel methods were able to
compute determinants faster than the sequential counterparts, particularly when the tasks were equally allocated. In conclusion, the newly developed methods can be used as viable alternatives for finding determinants of matrices
Innovative modeling and management of infrastructure systems, engineering and construction operations, and offsite construction technology using computational data analytics
“The construction industry has been facing considerable challenges due to the inadequacy of the traditional methods in executing, managing, and modeling infrastructure and construction projects. While many techniques have been developed to improve the decision-making process in the industry, there is no evidence of sufficient and continuous improvements in the industry’s adoption and implementation of innovative techniques such as new management approaches, modern modeling methods, and emerging computational data analytics. To this end, the goal of this research is to address some of the recent challenges faced in the industry with a focus on infrastructure asset management, construction engineering and management operations, and offsite construction technology. The research goals and objectives were achieved through multiple management, modeling, and computational analytical methods; including artificial intelligence and supervised machine learning algorithms, mathematical and risk modeling, statistical and multivariate time series analysis, clustering techniques and unsupervised data mining algorithms, and surveys and industry panel meetings. The research has numerous intellectual merits, methodological contributions, and practical implications as it addresses critical research areas that have not been investigated before and strengthens areas which needed in-depth examination and further advancements. The findings, outcomes, and conclusions of this research will contribute in further improving the cost, time, productivity, and safety considerations in the industry; leveraging innovative management, modeling, and computational analytics in infrastructure and construction projects; devising data-driven decision-making processes; and administrating and preparing the workforce of the future”--Abstract, page iii
Decision support system for form verification of manufactured parts.
The form verification of manufactured parts is a process composed of a set of operations that are expensive and yet add no value to the product. Yet, the resources used to inspect the parts add a small but significant amount of noise that can affect the outcome of the process. For this reason, this research provides guidelines to effectively perform the inspection process by suggesting new mathematical models and approaches that can be used for the creation of a decision support system that can assist in the verification of the accuracy of machined parts.This research proposes two approaches to improve the robustness of the mathematical models from the noise induced by the inspection process. The Dynamic Angle Approach (DAA) and the Free Form Orientation approach (FFO) presented here focus on finding the parameters of the axes and origin of the form that counteract the inaccuracies of the inspection equipment.In summary, this research suggests formalized methods for feature extraction, sampling, path planning, and form fitting, although the last mentioned received the most attention. It is believed that this comprehensive, integrated analysis will lead to the development of a decision support system.The proposed approaches and mathematical models were verified using measurements from features that were perfectly aligned with the coordinate system of the inspection equipment and from features that were intentionally misaligned. The results showed that the models were accurate and robust enough to estimate the parameters and zone of error of the form features and they performed better than existing models.The main goal of this research is to develop procedures that are simple to implement but at the same time are robust enough to provide reliable information that help the metrologist to make accurate decisions about the inspected parts. Form features such as spheres, cylinders, cones, frustums, and torus forms are commonly used to design complex parts. However, the procedures to verify most of these form features have not been developed yet by the national standards. Therefore, this research proposes new mathematical models that combine the concepts of analytic geometry and optimization to provide optimal solutions
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