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Improving the production efficiency is one of the main management tasks for the pulp and paper industry. This can be achieved through the use of automated planning systems, designed to take into account the features and limitations of particular production. Such systems allow, on the basis of orders received from consumers, a voluminous production schedule to be made to take into account the needs for raw materials, the productivity of paper or cardboard machines, and the optimal cutting of the canvas on longitudinal cutting machines. Linear programming method for solving optimization problems. Computer simulation using optimized packages to compare the results with the options used in production. Cutting planning processes were optimized on the basis of standard orders for rolled products of an enterprise in the Kyiv region. The problem of optimizing the cutting planning process and criteria for increasing the production efficiency is formalized. The target function is to increase the profit of production by minimizing material losses in planning the cutting of the master rill and minimizing the storage of related products in production warehouses. To solve the optimization problem, it is proposed to use our own method of calculating the optimal cutting patterns for the manufacture of finished products on a longitudinal cutting machine. The MS Office SOLVER package was used for the calculations. Different cutting options were compared via the target function. Important criteria for this inspection are the fulfillment of the entire order, the availability and storage of related products outside the order, and the minimization of time for the reconfiguration of machines. Longitudinal cutting machines of this production can be adjusted only manually, and it takes a long time. Also, technological restrictions on the allowable width of the edge are an important requirement for cutting patterns. All patterns that do not meet these restrictions cannot be compared. There are three options for comparison. An option is made according to the proposed method and with an optimization package using the Simplex method and a number of technological limitations inherent in this production. An option is selected by the production planner manually taking into account previous experience. And the variant of cutting orders is made by the production management system, which accompanies the process of planning and transfer of tasks for longitudinal cutting machines. The function takes the maximum value in the option offered by the production scheduler. But this option is not optimal, because a person adds rolls to the satellite to improve the cutting map to fulfill all orders. This in turn leads to overfulfillment of orders and production of additional products in a warehouse where they can be stored for years until the expiration date. The option offered by the automotive system does not require the use of accompanying rolls but unfortunately does not meet the requirements for optimal cutting of the material. Automatic cutting leaves a large edge that is already within the maximum allowable limitations of the machines and, moreover, does not lead to the execution of the entire order. This violates the basic requirement for production such as full execution of the order. Therefore, this option cannot be used either. The optimal cut for profit maximization is the option obtained using the roll planning technique. This technique allows the average weight of the roll to be obtained through the use of production history. The estimated weight is used to convert the order from tons to the number of pieces. Cutting patterns for the production of the order in this way are presented and, taking into account the technological limitations of production, can be calculated on the basis of the Simplex method in optimization packages. Such cutting patterns have no satellites and allow the fulfilment of all orders. The roll planning technique reduces the number of clippings and avoids overproduction. The analysed cutting plans show that the use of satellites reduces the profitability of the enterprise and does not always minimize material costs.

This work develops a multi-objective linear programming model for a multi-echelon, multi-product, multi-period supply chain planning. This model considers all dimensions of sustainable development paradigm simultaneously and expands six objective functions for making the model realistic. To solve this model and large-scale sustainable supply chain management, an improved version of the augmented e-constraint method (AUGMECON2) is used. Computational results illustrate the proposed model admitted various progresses in all the three pillars of sustainable development; namely remarkable progress, mildly progress and irregular one, respectively. In addition, important managerial insights are provided. Trade-off interactions between multi-objectives are perceived by the obtained Pareto solutions that represents the cost of being sustainable from the point of optimizing the social factors and environmental impacts. An application to the paper industry is presented and discussed.

Providing an efficient production plan that integrates the procurement and distribution plans into a unified framework is significant to achieving competitive advantage. In this paper, we consider a supply chain master planning model consisting of multiple suppliers, one manufacturer and multiple distribution centers. We propose a new multi-objective linear programming model in a wood and paper industry for integrating procurement, production and distribution planning, considering various objectives simultaneously as well as the imprecise nature of some critical parameters such as market demands, cost coefficients and capacity levels. Then, we use a fuzzy ranking method to solve this fuzzy multi-objective supply chain master planning model. Afterwards we compare the results of this approach with the results of the deterministic one.

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 production efficiency and shadow prices of three environmental outputs (BOD, COD, and SS) of 63 household-level paper-recycling units, from a recycling craft village in Vietnam, are assessed A two-stage procedure, linear programming and stochastic estimation, is used to estimate output distance function. Social capital as a production factor and environmental outputs are included in the output distance function. Results indicate that production efficiencies could potentially be improved by 28%. There is a substantial variation in the shadow prices of environmental outputs among the production units of different types of paper products. Furthermore, the average shadow prices of the three environmental outputs are all positive. This indicates a potential for improving environmental quality though introducing pollution-prevention methods to paper-recycling production processes in Vietnam (e.g., recirculation of wastewater), and suggests that it may be inappropriate to restrict the shadow prices of environmental outputs to be non-positive for the analysis of some production processes.

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.

Concerns for the environment has forced many firms define policies that protect the environment within which they operate. This paper presents a linear optimization model for paper industry to compare total system cost of wood as a raw material with recycling of waste paper. Objective of the proposed model is to minimize the cost of paper in the supply chain. Costs included in the objective functions are: costs for collection of raw material, transportation, inventory, manufacturing, segregation and disposal (during recycling). The model also includes the economic implications of using these alternative material sources on environment as well as quality of final product. To gain further insights into system behavior, sensitivity analysis, shortage analysis and indifference curve analysis have been performed. The analysis clearly reveals that the paper recycling is an economical option compared to wood as a raw material. The paper analysis highlights various strategies that could be followed under different market conditions.

This paper presents an application of a customized linear programming (LP) based model predictive control strategy to the paper machine cross direction (CD) control problem. The objective of CD control is to maintain flat profiles of variables of interest by minimizing worst case deviations from setpoints (defects). These control problems can have as many as 200 actuators (inputs) and 400 sensor measurements (outputs). This large size coupled with the stringent real-time requirement of computing a control move in a few seconds poses a very challenging control problem. Computational results that demonstrate the effectiveness of this strategy will be presented. For typical disturbances this algorithm can compute provably optimal control moves for a 400 input ×400 output control problem in approximately 5 s versus approximately 90 s for a generic LP algorithm on a HP 9000/770 workstation.

One of the main issues in a paper mill is the minimization of trim loss when cutting master reels and stocked reels into reels of smaller required widths. The losses produced in trimming at a paper mill are reprocessed by using different chemicals which contributes to significant discharge of effluent to surface water and causes environmental damage. This paper presents a real-world industrial problem of production planning and cutting optimization of reels at a paper mill and differs from other cutting stock problems by considering production and cutting of master reels of flexible widths and cutting already stocked over-produced and useable leftover reels of smaller widths. The cutting process of reels is performed with a limited number of cutting knives at the winder. The problem is formulated as a linear programming model where the generation of all feasible cutting patterns determines the columns of the constraint matrix. The model is solved optimally using simplex algorithm with the objective of trim loss minimization while satisfying a set of constraints. The solution obtained is rounded in a post-optimization procedure in order to satisfy integer constraints. When tested on data from the paper mill, the results of the proposed model showed a significant reduction in trim loss and outperformed traditional exact approaches. The cutting optimization resulted in minimum losses in paper trimming and a lesser amount of paper is reprocessed to make new reels which reduced the discharge of effluent to the environment.

The impacts of increased paper recycling on the U.S. pulp and paper sector are investigated, using the North American Pulp And Paper (NAPAP) model. This dynamic spatial equilibrium model forecasts the amount of pulp, paper and paperboard exchanged in a multi-region market, and the corresponding prices. The core of the model is a recursive price-endogenous linear programming system that simulates the behavior of a competitive industry. The model has been used to make forecasts of key variables describing the sector from 1986 to 2012, demand for paper would have the greatest impact on the amount of wood used. But the minimum recycled content policies envisaged currently would have no more effect than what will come about due to unregulated market forces.

The trim-loss problem appears when a customer's demand is to be satisfied in a paper converting mill. A set of product paper reels need to be cut from raw-paper reels. The problem is by nature an integer problem and therefore gives rise to a difficult combinatorial problem. Maybe the most difficult problem beside the combinatorics, is the fact that the paper converting mill needs to adapt itself to both the customer specified widths and the raw paper widths that are delivered from a raw paper mill. This fact makes it difficult to avoid material losses during the process. In the present paper some key issues are considered as to the combining the paper mill and the paper converting mill.

This paper deals with the two-dimensional cutting stock problem with set-up cost 2CSP-S. This problem is composed of three optimisation sub-problems: a 2-D bin packing 2BP problem to place images on patterns, a linear programming LP problem to find for each pattern the number of stock sheets to be printed and a combinatorial problem to find the number of each image on each pattern. We have already developed two different metaheuristics to solve the 2CSP-S focusing on this third sub-problem: a simulated annealing and a genetic algorithm. In this article, we propose to compare these two approaches. It is important to notice that our approaches are not new packing techniques. This work was conducted for a paper industry company and experiments were realised on real and artificial data sets.

Will paper recycling reduce the environmental impact of the European pulp and paper sector? If so, is maximal paper recycling the best policy to optimize the life cycle of the pulp and paper sector? We explore these questions using an approach that combines materials accounting methods and optimization techniques. Environmental impact data are inputs for a linear programming network flow model to find optimal configurations for the sector. These configurations consist of a mix of different pulping technologies, a geographical distribution of pulp and paper production, and a level of recycling consistent with the lowest environmental impacts. We use the model to analyse scenarios with different recycling strategies. Recycling offers a reduction in environmental impact in regions with a high population and a large production of paper and board products. Regions with a large production of graphic products should focus on cleaner virgin pulp production with energy recovery. We conclude that relocation of paper production also offers a reduction in environmental impact. However, the severe effects on the economy make this policy less attractive than a combination of recycling, cleaner pulp production and energy recovery.

This paper addresses the lot-sizing and scheduling problem of pulp and paper mills involving multiple paper machines. The underlying multi-stage integrated production process considers the following critical units: continuous digester, intermediate stocks of pulp and liquor, multiple paper machines and a recovery line to treat by-products. This work presents a mixed integer programming (MIP) model to represent the problem, as well as a solution approach based on a customized genetic algorithm (GA) with an embedded residual linear programming model. Some GA tools are explored, including literature and new operators, a novel diversification process and other features. In particular, the diversification process uses a new allele frequency measure to change between diversification and intensification procedures. Computational results show the effectiveness of the method to solve relatively large instances of the single paper machine problem when compared to other single paper machine solution methods found in the literature. For multiple paper machine settings, in most runs the GA solutions are better than those obtained for the MIP model using an optimization software. This paper addresses the lot-sizing and scheduling problem of pulp and paper mills involving multiple paper machines. The underlying multi-stage integrated production process considers the following critical units: continuous digester, intermediate stocks of pulp and liquor, multiple paper machines and a recovery line to treat by-products. This work presents a mixed integer programming (MIP) model to represent the problem, as well as a solution approach based on a customized genetic algorithm (GA) with an embedded residual linear programming model. Some GA tools are explored, including literature and new operators, a novel diversification process and other features. In particular, the diversification process uses a new allele frequency measure to change between diversification and intensification procedures. Computational results show the effectiveness of the method to solve relatively large instances of the single paper machine problem when compared to other single paper machine solution methods found in the literature. For multiple paper machine settings, in most runs the GA solutions are better than those obtained for the MIP model using an optimization software.

Paper fragment mosaic is one of the applications of Image Mosaic, which plays an important role for Historical Document Re. Based on a series of modeling ideas, we propose a novel method to mosaic strip-type paper fragments In this paper. Firstly, the boundaries are sampled and binarized from paper fragments. Secondly, a matching ratio model is built to calculate the possibility that two fragments are spatial adjacent. Thirdly, the mosaic problem is transformed into a Traveling Salesman Problem (TSP) based on graph theory, and then solved via Linear Programming. In the experiments, a Chinese and an English paper are vertically cut into pieces, each of which is scanned as a picture file. The program coded based on our method can automatically mosaic all the fragments into the complete original paper picture, which proves the effectiveness of our method. Keywords-Image mosaic; Paper mosaic; Traveling salesman problem; Linear programming.

An approach based on the characterisation of individual operations by water demands and sources has been utilised to study an operating integrated pulp and paper mill. The search for novel whitewater network configurations as a function of specified process constraints and objective functions was done by means of a genetic algorithm coupled with linear programming. Potential solutions are derived from an initial superstructure representation which embodies all possible source and demand connections by an evolutive process akin to natural selection and adaptation. Various objective functions were investigated. Innovative strategies have been identified and are being considered for implementation.

Peer review has become the most common practice for judging papers submitted to a conference for decades. An extremely important task involved in peer review is to assign submitted papers to reviewers with appropriate expertise which is referred to as paper-reviewer assignment. In this paper, we study the paper-reviewer assignment problem from both the goodness aspect and the fairness aspect. For the goodness aspect, we propose to maximize the topic coverage of the paper-reviewer assignment. This objective is new and the problem based on this objective is shown to be NP-hard. To solve this problem efficiently, we design an approximate algorithm which gives a 1/3-approximation. For the fairness aspect, we perform a detailed study on conflict-of-interest (COI) types and discuss several issues related to using COI, which, we hope, can raise some open discussions among researchers on the COI study. Finally, we conducted experiments on real datasets which verified the effectiveness of our algorithm and also revealed some interesting results of COI.

The present paper deals with a production optimization problem connected with the paper-converting industry. The problem considered is to produce a set of product paper reels from larger raw paper reels such that a cost function is minimized. The problem is generally non-convex due to a bilinear objective function and some bilinear constraints, both of which give rise to certain problems. The problem can, however, be solved as a two-step optimization procedure, in which the latter step is a mixed integer linear programming problem. A numerical example is introduced to illustrate the proposed procedure. The example is taken from a real-life daily production optimization problem encountered at a Finnish paper-converting mill, Wisapak Oy, having an annual production of just over 100,000 tons of printed paper.

In the present paper, trim loss problems connected to the paper-converting industry are analyzed and solved. The objective is to produce a set of paper rolls from storage rolls such that a cost function including the minimization of the trim loss as well as the time for cutting is considered. The problem is a non-convex integer non-linear programming (INLP) problem, due to its bilinear constraints. The problem can, however, be written in an expanded linear form and can, thus, be solved as an integer linear programming (ILP) or a mixed integer linear programming (MILP) problem. The linear formulation is attractive from the point of view of formality. One drawback of linear formulations is the increased number of variables and constraints they give rise to. It is, though, of interest to compare different ways of describing the problem as an ILP/MILP problem. There has previously been some academic interest in solving trim loss problems as linear programming problems. In this paper, we will present a general INLP formulation, some ways to formulate and solve it as an ILP or MILP problem and compare the efficiency of these different approaches. The examples considered are taken from real daily trim optimization problems encountered at a Finnish paper-converting mill with a capacity of 100,000 tons/year.

We use the models of cognitive psychology and the early literature on linear programming models to understand how experts organize their thinking about models. We show that several different patterns appear in the way models are related to each other. This paper also is a kind of cognitive history of the formulation of linear programming models in the first decade after the invention of the field.

Conference paper assignment, i.e., the task of assigning paper submissions to reviewers, presents multi-faceted issues for recommender systems research. Besides the traditional goal of predicting `who likes what?', a conference management system must take into account aspects such as: reviewer capacity constraints, adequate numbers of reviews for papers, expertise modeling, conflicts of interest, and an overall distribution of assignments that balances reviewer preferences with conference objectives. Among these, issues of modeling preferences and tastes in reviewing have traditionally been studied separately from the optimization of paper-reviewer assignment. In this paper, we present an integrated study of both these aspects. First, due to the paucity of data per reviewer or per paper (relative to other recommender systems applications) we show how we can integrate multiple sources of information to learn paper-reviewer preference models. Second, our models are evaluated not just in terms of prediction accuracy but in terms of the end-assignment quality. Using a linear programming-based assignment optimization formulation, we show how our approach better explores the space of unsupplied assignments to maximize the overall affinities of papers assigned to reviewers. We demonstrate our results on real reviewer preference data from the IEEE ICDM 2007 conference., Comment: 8 pages, 5 figures, submitted to the ACM Conference on Recommender Systems 2009

The control strategy is based on minimising the expected range of variations observed within a single scan across the width of the sheet. This can be considered as an l∞ minimisation problem and implemented using efficient algorithms based on the simplex method of linear programming. The BIBO stability of the controlled system is analysed and it is shown that the stability of the full multivariable system is determined by the stability of a scalar dynamic term. In general, the controller is suboptimal in the sense that it minimises an upper bound on the expected range of variations across the sheet, but for a certain class of disturbance models the expected value of the range can be minimised optimally by using the minimum variance solution.

In our research, we study the case of a pulp and paper producer who decides to establish a partnership with one buyer. Using two different types of relationship, namely a traditional system without any collaboration scheme and Collaborative Planning, Forecasting and Replenishment, we develop decision models describing the producer and the buyer planning processes. We also identify which approach is more profitable for each actor as well as for the network, based on real costs and parameters obtained from the industrial case. We then test how different incentives can improve the traditional system and provide higher gains for each partner. Our results show that using incentives increases the system's profit by up to 4% if parameters are well defined.

Skiving is the process of joining smaller rolls to form larger rolls. When this process is combined with traditional roll-cutting technology, profitable solutions to once "hopeless" one-dimensional cutting stock problems can be found. A generalization of the classic cutting stock problem models the combined skiving and cutting stock problem. The commercial software package MAJIQTRIM solves this combined problem using both heuristic and linear programming (LP) methods.

Optimization of utility energy system operations at a paper and pulp manufacturing plant isstudied. The system consists of three boilers and two turbine-generators. The plant discharges a large amount of black liquid which is used as a fuel in the system as well as heavy oil. Steam energy consumed at the plant is produced by this system, while electric energy consumed at the plant is not only generated from the system but also purchased from an electric power company. The goal of the optimization is to minimize the amounts of heavy oil consumed in the system subject to restrictions of black liquid consumption, steam and electric energy requirements and ranges of system operations. Actual operations of the system is analized using the data obtained on every 3 hours. It is found from this analysis that energy characteristic of every unit contained in the system can be represented by a linearized model. This leads to an application of the linear programming to the optimization of the system operations. The results of this study show that in order to optimize the system operations, in general, a unit with higher efficiency is operated with priority to other units, while some inefficient operations are found subject to restrictions of the system operations. Some proposals are thus given; one is to enlarge the capacity of storage tank of the black liquid and the other is to reduce the purchased electric energy under some special operating conditions. It is concluded that a large amount of heavy oil can be saved by these optimized operations of the utility energy system.

The Variation of the Spectrum of a Normal Matrix (with H W Wielandt) - Integral Boundary Points of Convex Polyhedra (with J Kruskal) - On Moore Graphs with Diameters 2 and 3 (with R R Singleton) - Cycling in the Simplex Algorithm - On Approximate Solutions of Systems of Linear Inequalities - On the Polynomial of a Graph - Some Recent Applications of the Theory of Linear Inequalities of Extremal Combinatorial Analysis - On Simple Linear Programming Problems - Self-Orthogonal Latin Squares (with R K Brayton & D Coppersmith) - On the Nonsingularity of Complex Matrices (with P Camion) - A Generalization of Max Flow-Min Cut - A Characterization of Comparability Graphs and of Interval Graphs (with P C Gilmore) - and 33 other papers