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In paper production, a jumbo reel is cut into multiple intermediate rolls, and each intermediate roll is then sheeted as finished goods. This problem is called a cutting stock problem and is proven to be NP-hard. The objective is to minimize material waste or trim loss from all the cuttings. In the case that any intermediate roll is not entirely used for its associated order, the intermediate roll itself could turn to be a dead stock. We use the concept of universal sizes of intermediate rolls to eliminate the dead stock. A pre-defined number of universal sizes of intermediate rolls is to be used to serve all the orders. The problem is solved using Reinforcement Artificial Bee Colony algorithm with Integer Linear Programming subroutine. This proposed approach is then tested with a set of 1,055 orders and 127 different sizes of sheet papers from a paper manufacturer. The results reveal that our method outperforms other algorithms. Our method offers the total trim loss of 3.51%, compared to the trim loss reported by the industry of at least 5%. This approach not only reduces the number of partially cut rolls, but also decreases the number of the jumbo reels needed to serve all the orders. Therefore, both the inventory cost and material cost can be saved.

In this work, some counterexamples are given to refute some results in the paper by Kohli [RAIRO:OR 53 (2019) 1617–1632]. We correct the fault in some of his results.

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

Environmental concerns and soaring energy prices have brought huge pressure of energy saving and emission reduction to tissue paper mills. Electricity is one of the main energy sources of tissue paper mills. The production characteristics of tissue paper mills make it easy to decrease energy cost by using time-of-use (TOU) electricity tariffs. This study investigates the bi-objective energy-efficiency scheduling of tissue paper mills under time-of-use electricity tariffs, the objectives of which are makespan and energy cost. First, considering the processing energy cost, setup energy cost, and transportation energy cost, an energy cost model of a tissue paper mill under TOU electricity tariffs is established. Second, the energy-efficiency scheduling model under TOU electricity tariffs is built based on the energy cost model. Finally, on the basis of decomposition and teaching&ndash, learning optimization, this study proposes a novel multi-objective evolutionary algorithm and further combined with the variable neighborhood search to solve the problem. The case study results demonstrate that our study of tissue paper mill energy saving is feasible, and the proposed method has better performance than the existing methods.

In this paper, we study the sum rate maximization for successive zero-forcing dirty-paper coding (SZFDPC) with per-antenna power constraint (PAPC). Although SZFDPC is a low-complexity alternative to the optimal dirty paper coding (DPC), efficient algorithms to compute its sum rate are still open problems especially under practical PAPC. The existing solution to the considered problem is computationally inefficient due to employing high-complexity interior-point method. In this study, we propose two new low-complexity approaches to this important problem. More specifically, the first algorithm achieves the optimal solution by transforming the original problem in the broadcast channel into an equivalent problem in the multiple access channel, then the resulting problem is solved by alternating optimization together with successive convex approximation. We also derive a suboptimal solution based on machine learning to which simple linear regressions are applicable. The approaches are analyzed and validated extensively to demonstrate their superiors over the existing approach., Comment: 5 pages, 4 figures

Objective: The present study is to formulate and development of efavirenz tablets by paper technique using the co-solvency method, the drug is antiviral drug used for the treatment of HIV. Methods: In this 7 formulation (F1-F7) were prepared by using different tissue papers like kitchen roll paper, hand kercheif paper, facial tissue paper, with different weights. The prepared tablets were evaluated for hardness, friability, thickness, content uniformity, disintegration time and in vitro dissolution study. Results: Among all the formulations, F2 (kicthen roll paper with weight 250 mg) was consired to be the best formulation, which release up to 98.02% drug in 3 h. The results of stability studies of formulation F2 after a period of 2 mo indicated that the formulation was stable. Conclusion: It was concluted that a paper tablet of efavirenz shows better results and it does not contain any excipient and increase the dissolution rate.

The design of a supply chain network helps companies in dealing with variability and uncertain evolution of demand over time. An efficient supply chain network may contribute to fulfill the customers’ demands in a quick and least cost manner. Therefore, it is important to solve the problem dealt with in this article concerning the design of the distribution network under demand uncertainty. The problem is to determine which warehouses to open and how much space to rent (outsource) in warehouses owned by third-party logistics providers. This paper presents the development and application of the robust optimization methodology to distribution network design problem under demand uncertainty. The proposed method allows the designer to find a network configuration having a total cost that is robust to typical changes in the geographical distribution of the demand. The Algorithm is an iterative process based on Benders decomposition. At each iteration, the following two steps are performed. In the first step, the global design problem (master problem) is solved to decide on the best use of warehouses according to the information provided by the previous iterations. For a given warehouse configuration and under some restrictions on demand variations, the second step determines the demand that incurred the largest transportation cost, granted that the transportation cost is optimal. These steps are repeated until finding the warehouses configuration that gives the smallest worst-case transportation cost. At each iteration the worst-case transportation cost sub-problem provides new information to the global design problem, such that the latter can improve its robustness. We report numerical results for real size network problems. The main results show that a high level of robustness of the distribution network can be achieved at a relatively low cost.

In this article, we propose a novel hybridization of regression trees (RT) and radial basis function networks (RBFN), namely, radial basis neural tree (RBNT) model,for waste recovery process improvement in the paper industry. As a by-product of the paper manufacturing process, a lot of waste along with valuable fibers and fillerscome out from the paper machine. The waste recovery process (WRP) involves separating the unwanted materials from the valuable ones so that the recovered fibersand fillers can be further reused in the production process. This job is done by fiber-filler recovery equipment (FFRE). The efficiency of FFRE depends on severalcrucial process parameters and monitoring them is a difficult proposition. The proposed model can be useful to find the essential parameters from the set of availabledata and perform prediction task to improve waste recovery process efficiency. An idea of parameter optimization along with regularity conditions for the universal consistency of the proposed model are given. The proposed model has the advantages of easy interpretability and excellent performance when applied to the FFREefficiency improvement problem. Improved waste recovery will help the industry to become environmentally friendly with less ecological damage apart from being cost-effective.

A large part of the paper industry involves supplying customers with reels of specified width in specified quantities. These 'customer reels' must be cut from a set of wider 'jumbo reels' as economically as possible. The first priority is to satisfy the customer demands using as few jumbo reels as possible. This is an example of the well-known one-dimensional cutting stock problem, which can be solved by creating a series of patterns, each corresponding to a different set of reels that can be cut from a single jumbo. A secondary problem is to minimize the number of patterns needed in order to reduce frequency of cutting-knife resets. Existing methods account for 90% of cases. The problem is to provide methods for the remaining 10%. This is shown to be an NP-hard problem, and so efficient design heuristics are proposed and tested for reducing further the frequency of resets.

The design of a high-volume printer for professional use is rather complex. The design of the paper path and the timing of sheets is frequently reengineered as the design of the printer components progresses. This paper presents an optimization model for the combined paper path and timing design problem. The paper path is an optimal physical dimensioning problem, while the timing is an open-loop optimal control problem. The coupled optimization problem is formulated as a simultaneous analysis and design (SAND) problem using a direct transcription of the optimal control problem. Benefits of the chosen formulation for industrial application are the ease of setting up the optimization model for arbitrary printer configurations, and the short computation times. Results of an industrial case are presented.

Made available in DSpace on 2021-07-14T10:36:19Z (GMT). No. of bitstreams: 0 Previous issue date: 2016. Added 1 bitstream(s) on 2021-07-14T11:34:04Z : No. of bitstreams: 1 S2179-84512016000300305.pdf: 151242 bytes, checksum: 76a9182b1eaa42a52072ff191dcf3917 (MD5) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Dois importantes problemas de otimização combinatória ocorrem no planejamento da produção em indústrias papeleiras: o problema de dimensionamento de lotes e o problema de corte de estoque multiperíodo. O problema de dimensionamento de lotes deve determinar a quantidade de bobinas jumbos de diferentes tipos de papel (gramaturas) a serem produzidos em cada máquina, ao longo de um horizonte de planejamento finito. Estes jumbos são então cortados para atender a demanda de itens para cada período. Neste trabalho, tratamos da integração desses dois problemas, procurando minimizar custos com produção e estoque dos jumbos, como também a perda de papel durante o processo de corte. Duas modelagens matemáticas para o problema integrado foram consideradas, e os modelos foram resolvidos heuristicamente usando um pacote de otimização. Procurando obter limitantes inferiores para o problema, foram resolvidas versões relaxadas dos modelos. Finalmente, experimentos computacionais são apresentados e discutidos. Two important optimization problems occur in the planning and production scheduling inpaper industries: the lot sizing problem and the cutting stock problem. The lot sizing problem must determine the quantity of jumbos of different types of paper to be produced in each machine over a finite planning horizon. These jumbos are then cut in order to meet the demand of items for each period. In this paper, we deal with the integration of these two problems, aiming to minimize costs of production and inventory of jumbos, as well as the trim loss of paper generated during the cutting process. Two mathematical models for the integrated problem are considered, and these models are solved both heuristically and using an optimization package. Attempting to get lower bounds for the problem, relaxed versions of the models also have been solved. Finally, computational experiments are presented and discussed. Universidade Estadual Paulista Júlio de Mesquita Filho, Faculdade de Ciências Universidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Matemática Aplicada Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica Universidade Estadual Paulista Júlio de Mesquita Filho, Faculdade de Ciências Universidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Matemática Aplicada CNPq: 2010/10133-0; 2013/07375-0 FAPESP: 2010/10133-0; 2013/07375-0

Data envelopment analysis (DEA) as a nonparametric programming approach has been widely extended and applied in many areas. Conventional DEA models can well measure the efficiency of inefficient decision-making units (DMUs) but cannot further discriminate the efficient DMUs. A lot of methods are proposed to address this problem. One of the most important methods is the slacks-based measure of super-efficiency model (S-SBM model) developed by Tone in 2002. However, the projection for a DMU on the efficient frontier identified by S-SBM model may not be strongly Pareto-efficient that makes the super-efficiency score misestimated. This paper revises the usual slacks-based measure of super-efficiency by incorporating input saving and output surplus scaling factors into the objection function for measuring DMUs. We integrate SBM model and S-SBM model effectively and yield input saving and output surplus scaling factors as well as input and output slacks under only one integrated model. According to the study, the projection reference point identified by our method is strongly Pareto-efficient. Meanwhile, how each decision variable influences the efficiency score for a specific DMU is revealed and illustrated through two numerical examples and an empirical study in paper chemical mills.

In this work, some reasoning's mistakes in the paper by Kohli (doi:10.3934/jimo.2020114) are highlighted. Furthermore, we correct the flaws, propose a correct formulation of the main result (Theorem 5.1) and give alternative proofs.

This paper considers a cell-free massive MIMO system with multiple-antenna access points and single-antenna users. The APs use conjugate beamforming to beamform the data to all users in the network. Total energy efficiency maximization is investigated. This optimization problem is nonconvex and thus difficult to solve. Existing solutions are based on second-order optimization methods in connection with convex approximations. These methods have been shown to perform very well but their complexity does not scale favorably with the network size. To tackle this issue, in this paper we propose to use a first-order method for nonconvex programming to our energy efficiency problem. Compared to the second-order methods, the proposed method achieves the same performance, while its run time is much faster. Thus, it is considered as a feasible solution for resource allocation in cell-free massive MIMO systems.

Fine paper mills produce a variety of paper grades to satisfy demand for a large number of sheeted products. Huge reels of different paper grades are produced on a cyclical basis on paper machines. These reels are then cut into rolls of smaller size which are then either sold as such, or sheeted into finished products in converting plants. A huge number of roll sizes would be required to cut all finished products without trim loss and they cannot all be inventoried. An assortment of rolls is inventoried with the implication that the sheeting operations may yield trim loss. The selection of the assortment of roll sizes to stock and the assignment of these roll sizes to finished products have a significant impact on performances. This paper presents a model to decide the parent roll assortment and assignments to finished products based on these products demand processes, desired service levels, trim loss and inventory holding costs. Risk pooling economies made by assigning several finished products to a given roll size is a fundamental aspect of the problem. The overall model is a binary non-linear program. Two solution methods are developed: a branch and price algorithm based on column generation and a fast pricing heuristic, and a marginal cost heuristic. The two methods are tested on real data and also on randomly generated problem instances. The approach proposed was implemented by a large pulp and paper company.

Paper is an example of a valuable material that can be recycled and recovered. In this study, a two-stage stochastic revenue-maximization model is presented to determine a long-term strategy under uncertainty for a large-scale real-world paper recycling company. This network-design problem includes optimal recycling center locations and optimal flow amounts between the nodes in the multi-facility environment. The proposed model is formulated with two-stage stochastic mixed-integer and robust programming approaches. The models are solved by commercial software GAMS 21.6/CPLEX 9.0 and the results are compared. The study is followed by the analyses of the results.

In this paper, an efficient greedy scheduler for zero-forcing dirty-paper coding (ZF-DPC), which can be incorporated in complex Householder QR factorization of the channel matrix, is proposed. The ratio of the complexity of the proposed scheduler to the complexity of the channel matrix factorization required by ZF-DPC is O(M-1), while such ratio for the original greedy scheduler is O(M), where M is the number of transmitters. Therefore, the new scheduler reduces the overhead of scheduling from being the bottleneck of ZF-DPC to being negligible.

The paper proposes an algorithm of setting norms of permissible discharge within the frameworks of natural/technological system with the use of “GIMSriver” geo/information modeling complex.

Parallel test paper generation is a biobjective distributed resource optimization problem, which aims to generate multiple similarly optimal test papers automatically according to multiple user-specified assessment criteria. Generating high-quality parallel test papers is challenging due to its NP-hardness in both of the collective objective functions. In this paper, we propose a submodular memetic approximation algorithm for solving this problem. The proposed algorithm is an adaptive memetic algorithm (MA), which exploits the submodular property of the collective objective functions to design greedy-based approximation algorithms for enhancing steps of the multiobjective MA. Synergizing the intensification of submodular local search mechanism with the diversification of the population-based submodular crossover operator, our algorithm can jointly optimize the total quality maximization objective and the fairness quality maximization objective. Our MA can achieve provable near-optimal solutions in a huge search space of large datasets in efficient polynomial runtime. Performance results on various datasets have shown that our algorithm has drastically outperformed the current techniques in terms of paper quality and runtime efficiency.

The paper introduces a new taxonomy of ill-posed parametric inverse problems, formulated as global optimization ones. It systematizes irremediable problems, which appear quite often in the real life but cannot be solved using the regularization method. The paper also shows a new way of solving irremediable inverse problems by a complex memetic approach including: genetic computation with adaptive accuracy, random sample clustering and a sophisticated local approximation of misfit plateau regions. Finally, we use a benchmark function featuring cross-shaped plateau to discuss some factors that influence the quality of plateau shape approximation.

Dirty paper coding (DPC) scheme is the capacity achieving transmission technique in multiuser MIMO downlink channels. As a suboptimal solution to DPC, successive zero-forcing DPC (SZF-DPC) has been proposed recently. The zero-interference constraint in designing the precoding matrices limits the number of supportable users. In this correspondence, we propose three low-complexity suboptimal user scheduling algorithms to exploit the multiuser diversity gain in SZF-DPC as the number of users grows. The first algorithm greedily maximizes the true sum rate. The second algorithm is based on eigenvalues. The third algorithm relies on the diagonal elements of the effective channel matrix since eigenvalues and diagonal entries of a Hermitian matrix have a strong relationship. Simulation results show that the proposed scheduling algorithms can obtain a significant fraction of sum rate of the optimal solution. Furthermore, the performance analysis is provided to prove that the proposed user selection algorithms can achieve the same asymptotic sum rate as that of DPC.

The heat recovery system (HRS) is a vital part in a paper machine when it comes to the overall energy economy in papermaking. For a typical newsprint machine more than 60% of the exhaust energy from the dryer section can be recovered, resulting typically in a regain of about 30 MW. The synthesis task of a HRS is a decision process where the target is on the one hand to achieve maximal energy recovery and on the other hand to obtain it with minimal investment costs. These goals are contradictory and thus the problem is to find a solution minimizing the overall costs, considering simultaneously both energy and investment costs. One further challenge is how to take into account known variations in the parameters so that the design is robust, i.e. it is capable of handling all evolving situations and furthermore the design should be the most economical one when also considering the duration of all expected operational situations. In this work a hybrid method based on evolutionary programming and non-linear programming for the synthesis of robust and optimal heat recovery systems (HRS) for paper machines is presented. Variations and uncertainties on process parameters are modeled with probability distributions and the cost of the utilities are thus obtained as an integrated value of all different situations expected to evolve. The importance of obtaining a robust design is shown in a case study.

Automatically matching reviewers to papers is a crucial step of the peer review process for venues receiving thousands of submissions. Unfortunately, common paper matching algorithms often construct matchings suffering from two critical problems: (1) the group of reviewers assigned to a paper do not collectively possess sufficient expertise, and (2) reviewer workloads are highly skewed. In this paper, we propose a novel local fairness formulation of paper matching that directly addresses both of these issues. Since optimizing our formulation is not always tractable, we introduce two new algorithms, FairIR and FairFlow, for computing fair matchings that approximately optimize the new formulation. FairIR solves a relaxation of the local fairness formulation and then employs a rounding technique to construct a valid matching that provably maximizes the objective and only compromises on fairness with respect to reviewer loads and papers by a small constant. In contrast, FairFlow is not provably guaranteed to produce fair matchings, however it can be 2x as efficient as FairIR and an order of magnitude faster than matching algorithms that directly optimize for fairness. Empirically, we demonstrate that both FairIR and FairFlow improve fairness over standard matching algorithms on real conference data. Moreover, in comparison to state-of-the-art matching algorithms that optimize for fairness only, FairIR achieves higher objective scores, FairFlow achieves competitive fairness, and both are capable of more evenly allocating reviewers., Appears at KDD 2019 Research Track, 20 pages

This paper considers an industrial scheduling problem. It involves profit maximization and the determination of the optimal cycle time, while meeting the minimum demands for the several products. Resource-Task Network-based formulations are employed and a detailed comparison between continuous- and discrete-time models is provided. Both have the improved capability of handling tasks with flexible proportions of input materials in order to consider the incorporation of different flowrates of byproducts that are recycled back to the first production stage. The continuous-time formulation is shown to be more efficient and the resulting mixed integer nonlinear program (MINLP) can be solved to optimality within reasonable computational time. A new recycling policy is proposed that achieves the double goal of making the process more profitable due to important savings on the more expensive raw-materials and also more environmentally friendly, due to the reduction of waste disposal requirements.

In order to overcome the problems of unclear objective function and inaccurate optimal solution in traditional test paper generation methods, an online intelligent test paper generation method based on UML and particle swarm optimisation is proposed. A mathematical model of test paper generation based on UML modelling tool is established, and the objective function of test paper generation is obtained. The improved particle swarm optimisation algorithm is used to solve the objective function of test paper generation. The optimal solution of the objective function is introduced into the question bank. The test questions in the test bank are combined and imported into the online examination system to realise the intelligent test paper formation of online examination. The experimental results show that this method has strong adaptability and can achieve better performance than the traditional intelligent test paper generation method.

The optimal control of a Dubins car endowed with a circular or a line segment capture set is considered and the connection to their Homicidal Chauffeur differential game (HCDG) analogues is discussed. The capture set endowed Dubins cars are tasked to reach a stationary target point in minimum time. The solutions to the Dubins car optimal control problems (DCOCP) are similar to the solutions of the HCDG where the Evader, which is slower than the pursuing car, has simple motion. The similarities can be attributed to the fact that both the DCOCPs and the HCDG dynamics have two states and the Pursuer in the HCDG is modeled as a Dubins car. Because of these similarities, the solutions to the DCOCPs yield insights into the solutions of the attendant HCDGs where the Pursuer is endowed with a circular, or a line segment, capture set. This paper develops the solution to the optimal control problem of a Dubins car with a line segment capture set, presents the solution when the car is endowed with a circular capture set, and then draws comparisons to the respective HCDGs.

A common problem encountered in paper-production facilities is that of allocating customer orders to machines so as to minimize the total cost of production. It can be formulated as a dual-angular integer program, with identical machines inducing symmetry. While the potential advantages of decomposing large mathematical programs into smaller subproblems have long been recognized, the solution of decomposable integer programs remains extremely difficult. Symmetry intensifies the difficulty. This paper develops an approach, based on the construction of tight subproblem bounds, to solve decomposable dual-angular integer programs and successfully applies it to solve the problem from the paper industry. This method is of particular interest as it significantly reduces the impact of symmetry.

This work describes a real-world industrial problem of production planning and cutting optimization of reels and sheets, occurring at a Portuguese paper mill. It will focus on a particular module of the global problem, which is concerned with the determination of the width combinations of the items involved in the planning process: the main goal consists in satisfying an order set of reels and sheets that must be cut from master reels. The width combination process will determine the quantity/weight of the master reels to be produced and their cutting patterns, in order to minimize waste, while satisfying production orders.A two-phase approach has been devised, naturally dependent on the technological process involved. Details of the models and solution methods are presented. Moreover some illustrative computational results are included.

We propose a new concept which is a generalization of fuzzy soft subset and fuzzy soft equal. Using such notions, we will be able to consider the distributive law of fuzzy soft sets. Using the distributive law of fuzzy soft sets, we point out that the distributive law of trapezoidal fuzzy soft sets as proposed by Xiao et al. (2012) is not true. The correction will further improve further extensions of the results of Xiao et al. (2012). We will also establish the generalized distributive law of trapezoidal fuzzy soft sets along with illustrative examples.