Simultaneous Cutting of Master Reels and Stocked Rolls in Solving Trim Loss Minimization Problem at Paper Mill

2019 ◽  
Author(s):  
Asnaf Aziz ◽  
Razaullah ◽  
Liaqat Ali ◽  
Khawar Naeem ◽  
Abdul Shakoor
Keyword(s):  
Minimization Problem ◽  
Paper Mill ◽  
Loss Minimization ◽  
Trim Loss

Enlarging the trim-loss problem to cover the raw paper mill

1998 ◽  
Vol 22 ◽  
pp. S1019-S1022 ◽  
Author(s):  
I. Harjunkoski ◽  
T. Westerlund
Keyword(s):  
Paper Mill ◽  
Trim Loss

New Hybrid Discrete PSO for Solving Non Convex Trim Loss Problem

2012 ◽  
Vol 3 (2) ◽  
pp. 19-41 ◽  
Author(s):  
Kusum Deep ◽  
Pinkey Chauhan ◽  
Millie Pant
Keyword(s):  
Optimal Solution ◽  
Complex Nature ◽  
Sigmoid Function ◽  
Loss Minimization ◽  
Physical Constraints ◽  
Trim Loss ◽  
Discrete Pso ◽  
Global Optimal ◽  
Power Mutation ◽  
Different Levels

Trim loss minimization is the most common problem that arises during the cutting process, when products with variable width or length are to be produced in bulk to satisfy customer demands from limited available/stocked materials. The aim is to minimize inevitable waste material. Under various environmental and physical constraints, the trim loss problem is highly constrained, non convex, nonlinear, and with integer restriction on all variables. Due to the highly complex nature of trim loss problem, it is not easy for manufacturers to select an appropriate method that provides a global optimal solution, satisfying all restrictions. This paper proposes a discrete variant of PSO, which embeds a mutation operator, namely power mutation during the position update stage. The proposed variant is named as Hybrid Discrete PSO (HDPSO). Binary variables in HDPSO are generated using sigmoid function with its domain derived from position update equation. Four examples with different levels of complexity are solved and results are compared with two recently developed GA and PSO variants. The computational studies indicate the competitiveness of proposed variant over other considered methods.

scholarly journals A Mathematical Model for Reduction of Trim Loss in Cutting Reels at a Make-to-Order Paper Mill

2020 ◽  
Vol 10 (15) ◽  
pp. 5274
Author(s):  
Razaullah Khan ◽  
Catalin Iulian Pruncu ◽  
Abdul Salam Khan ◽  
Khawar Naeem ◽  
Muhammad Abas ◽  
...  
Keyword(s):  
Programming Model ◽  
Optimization Procedure ◽  
Paper Mill ◽  
Simplex Algorithm ◽  
Cutting Stock ◽  
Environmental Damage ◽  
Constraint Matrix ◽  
Make To Order ◽  
Proposed Model ◽  
Trim Loss

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.

A Solution Method for 2-Dimensional Trim Loss Problem in a Paper Mill

2012 ◽  
Author(s):  
Sukonthip Puemsin ◽  
Wipawee Tharmmaphornphilas ◽  
Peerapon Siripongwutikorn
Keyword(s):  
Solution Method ◽  
Paper Mill ◽  
Trim Loss

scholarly journals Using Benson’s Algorithm for Regularization Parameter Tracking

2019 ◽  
Vol 33 ◽  
pp. 3689-3696
Author(s):  
Joachim Giesen ◽  
Sӧren Laue ◽  
Andreas Lӧhne ◽  
Christopher Schneider
Keyword(s):  
Approximate Solution ◽  
Minimization Problem ◽  
Regularization Parameter ◽  
Approximate Solutions ◽  
Loss Minimization ◽  
Real World Data ◽  
Approximation Accuracy ◽  
Parameter Domain ◽  
Parameter Tracking ◽  
Regularization Parameters

Regularized loss minimization, where a statistical model is obtained from minimizing the sum of a loss function and weighted regularization terms, is still in widespread use in machine learning. The statistical performance of the resulting models depends on the choice of weights (regularization parameters) that are typically tuned by cross-validation. For finding the best regularization parameters, the regularized minimization problem needs to be solved for the whole parameter domain. A practically more feasible approach is covering the parameter domain with approximate solutions of the loss minimization problem for some prescribed approximation accuracy. The problem of computing such a covering is known as the approximate solution gamut problem. Existing algorithms for the solution gamut problem suffer from several problems. For instance, they require a grid on the parameter domain whose spacing is difficult to determine in practice, and they are not generic in the sense that they rely on problem specific plug-in functions. Here, we show that a well-known algorithm from vector optimization, namely the Benson algorithm, can be used directly for computing approximate solution gamuts while avoiding the problems of existing algorithms. Experiments for the Elastic Net on real world data sets demonstrate the effectiveness of Benson’s algorithm for regularization parameter tracking.

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