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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.

Necessary optimality conditions for a bilevel optimization problem are given in the paper by Kohli (J Optim Theory Appl 152: 632–651, 2012). Recently, the same author corrected his results in the note (J Optim Theory Appl 181:706–707, 2019). In this work, we have pointed out that some of the new modifications are wrong. We correct the flaws and present an alternative proof for the main result.

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

The problem of joint information transmission and input signalling estimation over a state-dependent channel is considered. The general single-letter upper and lower bounds of the optimal capacity-distortion trade-offs are provided. For the Gaussian dirty paper channel, we calculate the bounds by choosing parameters carefully, and obtain a computable form eventually. In such a way, we obtain a complete characterization for this problem setting.

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.

This paper proposes a hybrid algorithm to tackle a real-world problem arising in the context of pulp and paper production. This situation is modelled as a production problem where one has to decide which wood will be used by each available processing unit (wood cooker) in order to minimize the variance of wood densities within each cooker for each period of the planning horizon. The proposed hybrid algorithm is built around two distinct phases. The first phase uses two interacting heuristic methods to identify a promising reduced search space, which is then thoroughly explored in the second phase. This hybrid algorithm produces high-quality solutions in reasonable computation times, especially for the largest test instances. Extensive computational experiments demonstrated the robustness and efficiency of the method.

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.

There has been an increasing interest in mobile edge computing (MEC) in recent years. Different from the traditional centralized cloud computing, MEC servers are deployed at the edges of networks such as at base stations (BSs) and access points (APs), in order to support computation-intensive and latency-critical applications. In this paper, we consider a multi-user mobile edge computing (MEC) network in which the wireless data transmission/offloading is performed using finite blocklength (FBL) codes to satisfy the latency constraints. The reliability in the communication phase is characterized in the FBL regime, while the event of queue length violation in the computation phase is investigated via exploiting the extreme value theory. We first formulate the overall optimization problem in the scenario of multiple user equipments (UEs) aiming to minimize the maximal end-to-end error probability among all UEs under both the FBL and energy consumption constraints. We further propose a two-level learning-based approach to jointly determine time allocations in the FBL regime, the UEs' offloading decisions and MEC computational resource allocations to solve the overall optimization problem. Simulation results demonstrate that the proposed two-level learning-based algorithm solves the problem efficiently.

Symbolic execution is an essential approach for automated test case generation. However, the approach is generally not scalable to large programs. One critical reason is that the constraint solving problems in symbolic execution are generally hard. Consequently, the symbolic execution process may get stuck in solving such hard problems. To mitigate this issue, symbolic execution tools generally rely on a timeout threshold to terminate the solving. Such a timeout is generally set to a fixed, predefined value, e.g., five minutes in angr. Nevertheless, how to set a proper timeout is critical to the tool’s efficiency. This paper proposes an approach to tackle the problem by predicting the time required for solving a constraint model so that the symbolic execution engine could base on the information to determine whether to continue the current solving process. Due to the cost of the prediction itself, our approach triggers the predictor only when the solving time has exceeded a relatively small value. We have shown that such a predictor can achieve promising performance with several different machine learning models and datasets. By further employing an adaptive design, the predictor can achieve an F1-score ranging from 0.743 to 0.800 on these datasets. We then apply the predictor to eight programs and conduct simulation experiments. Results show that the efficiency of constraint solving for symbolic execution can be improved by 1.25x to 3x, depending on the distribution of the hardness of their constraint models.

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.

We explore the power of parallelism in a stochastic search for global optimum in high-dimensional optimization problems. This search, that often navigates a large solution space through a Markov-Chain Monte Carlo (MCMC) process, needs to make stochastic decisions at every step and needs to escape from local optima. Through parallelism, we are able to survey an entire neighbourhood (all states with Hamming distance of 1) to make efficient moves, use multiple replicas at different temperatures, such as in parallel tempering, or deploy a population of replicas at the same temperature. Once combined, these methods of parallelism can yield 100x to 10,000x speedups.

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.

In this paper, decision support system (DSS) for a paper making unit of a paper plant has been developed by using genetic algorithm. Process industries like paper plant, sugar industry, fertilizer, etc., are comprised of various complex engineering units. A paper plant comprises chipping, digesting, washing, bleaching, screening, stock preparation, paper making units, etc. These units are connected in combined configuration. The paper making unit is having four main subsystems. The mathematical model of paper making unit has been developed by using Markov birth–death process. On the basis of probabilistic approach, the differential equations are formed by using of transition diagram. These equations are solved recursively and then reduced to steady-state conditions, mostly required for the paper industry. Normalizing condition has been used to find out the probability of full working state with or without the use of any standby subsystems, and finally performance model has been developed for paper making unit. Finally, the genetic algorithm technique is used for optimum value of the unit performance by coordinating the parameters of all subsystems of paper making unit.

Pulp and Paper Industries (PPI) manufactures a wide range of papers based on three different GSM (Grams/sq. meter). i.e., lower GSM, middle GSM and higher GSM. In order to maximize the profit, the PPI must efficiently utilize its available resources thereby producing optimal units of three different GSMs. Such problems lie under the category of product mix problems and forms an important part of production planning for every process industry like paper mill. In the present study, this problem is represented as a Fuzzy Linear Programming (FLP) model, to include the inherent vagueness and uncertainties. The solutions obtained through FLP are further refined with the help of AHP (Analytical Hierarchy Process) to determine the most profitable solution. Results indicate that ranking results obtained by integrating AHP into FLP may help in providing a better guidance to the Decision Maker (DM) for determining an optimal product mix.

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

Whale Optimization Algorithm (WOA) proposed by Seyedali Mirjalili and Andrew Lewis in 2016 is popular and powerful metaheuristic algorithm to search the global solution of optimization problems. WOA is nature-inspired, metaheuristic (randomization and deterministic) algorithm, which has been widely used to solve various single objective, multi objective and multidimensional optimization problems. WOA and its variant have been introduced in engineering applications, bioinformatics, multi-level image segmentation, clustering applications, design of low pass filter, Email classification, Diabetes classification, heterogeneous networks, machine learning etc. WOA is gradient free, easy to represent, capable to explore, exploit the search space and able to avoid local optima. This paper presents overview of WOA, its variants and applications. The performance of WOA is enhanced by introducing hybridization of other methods with WOA such as WOA-PSO, WOA-Levy, WOA-BAT, WOA-ANN, WOA-SVM etc. Objective of metaheuristic algorithm is tofind best position or leader position X* which is near to optimal solution for target prey over successive iteration. Objective function could be based on minimization or maximization approach.

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.

The study aimed to realize the automatic production scheduling of waste paper in paper mills. Based on the quantities of the field data (mixing ratios of waste paper and pulp properties) stored in a paper mill, the methods of back propagation neural networks (BP-NN) and support vector machine (SVM), the all data set and the average data set, were firstly applied to develop the prediction models of the pulp properties; and then the genetic algorithm was used to search for the suitable scheduling solutions. The simulation results revealed that: demonstrating its acceptable prediction accuracy and fast training time, the prediction model with SVM method and the average data set was good enough to be used in the genetic algorithm to search for the optimal scheduling ratios of waste paper, the obtained optimal scheduling solution not only had the lowest purchase cost of waste paper, but also met the requirements of the mill: specified types of waste paper, specified ratio of some kind of waste paper and the required pulp brightness.

This work considers sustainable scheduling of manufacturing operations and preventive maintenance activities in a single-machine environment where the machine works continuously in three eight-hour shifts per day. The jobs can be produced at different processing speeds, which reduces energy consumption and/or processing times. In a tri-objective mixed integer linear programming model, sustainability is attained through minimizing total weighted earliness/ tardiness - economic pillar, total energy consumption - environmental pillar, and number of undesired activities - social pillar. Moreover, a multi-objective genetic algorithm finds near optimal solutions in a timely manner. Numerical results will be presented at the conference.

This paper proposes a soft computing technology based on belief rule base (BRB) system for the tipping paper permeability measurement in tobacco factory. In current studies about BRB, both the referential values of the antecedent attributes and the utilities of the consequents are given in advance and are not trained by using the dedicated optimal algorithms. The limitations of expert knowledge may lead to error of BRB because both the referential values and the utilities make a real difference on the structure of BRB, and the appropriate structure is helpful for tuning parameters more accurately. Therefore, this paper focuses on the structure and parameters optimization of BRB (SPO-BRB) by taking the referential values of the antecedent attributes and the utilities of the consequents into account to improve the input–output modeling ability of BRB. However, SPO-BRB is a nonlinear nonconvex optimization problem (NNOP). To deal with the NNOP of SPO-BRB, a particle swarm optimization algorithm with improved velocity update way and repair methods (PSO_VR) is proposed. A case study based on the data collected from a tobacco factory of china is carried out. The test results demonstrate the functionality of SPO-BRB and the effectiveness of PSO_VR.

Fleet optimisation can significantly reduce the time vehicles spend traversing road networks leading to lower costs and increased capacity. Moreover, reduced road use leads to lower emissions and improved air quality. Heuristic approaches such as Ant Colony Optimisation (ACO) are effective at solving fleet optimisation but scale poorly when dealing with larger fleets. The Partial-ACO technique has substantially improved ACO’s capacity to optimise large scale vehicle fleets but there is still much scope for improvement. A method to achieve this could be to integrate simple mutation with Partial-ACO as used by other heuristic methods. This paper explores a range of mutation strategies for Partial-ACO to both improve solution quality and reduce computational costs. It is found that substituting a majority of ant simulations with simple mutation operations instead improves both the accuracy and efficiency of Partial-ACO. For real-world fleet optimisation problems of up to 45 vehicles and 437 jobs reductions in fleet traversal of approximately 50% are achieved with much less computational cost enabling larger scale problems to be tackled. Moreover, CO\(_{2}\) and NO\(_{\text {x}}\) emissions are cut by 3.75 Kg and 1.71 g per vehicle a day respectively improving urban air quality.