An integer programming model

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  • The selection of subjects (such as waste stream, process, apparatus, ect.) for improvement and development their alternatives when implementing cleaner production (CP) programs at the company in order to achieve the highest efficiency is a complex and timeconsuming process, especially in case when there are many subjects to be improved, and many alternatives for each subject.

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  • The ability to compress sentences while preserving their grammaticality and most of their meaning has recently received much attention. Our work views sentence compression as an optimisation problem. We develop an integer programming formulation and infer globally optimal compressions in the face of linguistically motivated constraints. We show that such a formulation allows for relatively simple and knowledge-lean compression models that do not require parallel corpora or largescale resources. The proposed approach yields results comparable and in some cases superior to state-of-the-art.

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  • In particular, almost all feasible instances of the problem are solvable in O(n) time using the new algorithm, where n is the number of vertices. The developed approach also helps in fast enumeration of a neighborhood in the local search and yields an integer programming model with O(n) binary variables for the problem.

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  • In this paper, the flow shop with blocking and sequence and machine dependent setup time problem aiming to minimize the makespan is studied. Two mixed-integer programming models are proposed (TNZBS1 and TNZBS2) and two other mixed-integer programming models, originally proposed for the no setup problem, are adapted to the problem. Furthermore, an Iterated Greedy algorithm is proposed for the problem.

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  • Department of Linguistics The University of Texas at Austin Austin, Texas 78712 or trigram Hidden Markov Model (HMM). Ravi and Knight (2009) achieved the best results thus far (92.3% word token accuracy) via a Minimum Description Length approach using an integer program (IP) that finds a minimal bigram grammar that obeys the tag dictionary constraints and covers the observed data.

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  • The Maximal Profit Model for reaching an optimal design of the production line undergoing the limitations of obtainable resources is presented in this paper. This model is treated as an integer programming problem, and an efficient step-by-step algorithm to solve this problem is also constructed. In addition, it is discussed that the operation cost of a machine does not include idle and breakdown situations while the maintenance cost for a broken machine should be considered.

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  • RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of RNA secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution, often missing the real structure.

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  • We present a novel computational formulation of speaker authority in discourse. This notion, which focuses on how speakers position themselves relative to each other in discourse, is first developed into a reliable coding scheme (0.71 agreement between human annotators). We also provide a computational model for automatically annotating text using this coding scheme, using supervised learning enhanced by constraints implemented with Integer Linear Programming.

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  • The major changes to this Fourth Edition include an earlier introduction to UML, a new section on inter-file communication in Chapter 13, and a revised approach to software development in Chapter 16. Introducing the UML at the beginning allows the use of UML diagrams where they fit naturally with topics in the text, so there are many new UML diagrams throughout the book. The section on inter-file communication gathers together many concepts that were previously scattered throughout the book.

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  • (bq) part 1 book "an introduction to management science - quantitative approaches to decision making" has contents: an introduction to linear programming; linear programming applications in marketing, finance, and operations management; advanced linear programming applications; distribution and network models; integer linear programming; nonlinear optimization models,...and other contents.

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  • Dency parsing. We believe that our formulations can pave the way for efficient exploitation of global features and constraints in parsing applications, leading to more powerful models. Riedel and Clarke (2006) cast dependency parsing as an ILP, but efficient formulations remain an open problem.

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  • Energy storage systems (ESSs) have experienced a very rapid growth in recent years and are expected to be a promising tool in order to improving power system reliability and being economically efficient. The ESSs possess many potential benefits in various areas in the electric power systems. One of the main benefits of an ESS, especially a bulk unit, relies on smoothing the load pattern by decreasing on-peak and increasing off-peak loads, known as load leveling. These devices require new methods and tools in order to model and optimize their effects in the power system studies.

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  • In this paper, we propose a multi−period mixed integer nonlinear programming (MINLP) model for an optimal planning and scheduling of the production and transportation of multiple petroleum products from a refinery plant connected to several depots through a single pipeline system.

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  • The model is formulated as a multi-objective mixed-integer nonlinear programming in order to minimize the expected total cost of such a supply chain network comprising location, procurement, transportation, holding, ordering, and shortage costs. Moreover, we develop an effective solution approach on the basis of multi-objective particle swarm optimization for solving the proposed model.

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  • This paper considers the capacity determination in a closed-loop supply chain network when a queueing system is established in the reverse flow. Since the queueing system imposes costs on the model, the decision maker faces the challenge of determining the capacity of facilities in such a way that a compromise between the queueing costs and the fixed costs of opening new facilities could be obtained.

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  • The purpose of this paper is to carry out the abelianization program proposed by Atiyah [1] and Hitchin [9] for the geometric quantization of SU(2) Wess-Zumino-Witten model. Let C be a Riemann surface of genus g. Let Mg be the moduli space of semi-stable rank 2 holomorphic vector bundles on C with trivial determinant. For a positive integer k, let Γ(Mg , Lk ) be the space of holomorphic sections of the k-th tensor product of the determinant line bundle L on Mg . An element of Γ(Mg , Lk ) is called a rank 2 theta function...

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  • In this paper we present a joint content selection and compression model for single-document summarization. The model operates over a phrase-based representation of the source document which we obtain by merging information from PCFG parse trees and dependency graphs. Using an integer linear programming formulation, the model learns to select and combine phrases subject to length, coverage and grammar constraints.

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  • Many phrase alignment models operate over the combinatorial space of bijective phrase alignments. We prove that finding an optimal alignment in this space is NP-hard, while computing alignment expectations is #P-hard. On the other hand, we show that the problem of finding an optimal alignment can be cast as an integer linear program, which provides a simple, declarative approach to Viterbi inference for phrase alignment models that is empirically quite efficient.

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  • We show how global constraints such as transitivity can be treated intensionally in a Zero-One Integer Linear Programming (ILP) framework which is geared to find the optimal and coherent partition of coreference sets given a number of candidate pairs and their weights delivered by a pairwise classifier (used as reliable clustering seed pairs). In order to find out whether ILP optimization, which is NPcomplete, actually is the best we can do, we compared the first consistent solution generated by our adaptation of an efficient Zero-One algorithm with the optimal solution. ...

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  • Flux Balance Analysis (FBA) is a genome-scale computational technique for modeling the steady-state fluxes of an organism’s reaction network. When the organism’s reaction network needs to be completed to obtain growth using FBA, without relying on the genome, the completion process is called reaction gap-filling.

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