152 Body Weight Standardized Cull Sow Non-edible Trim Loss Evaluation

Non-edible trim loss has been shown to reduce value in market hogs (Johnson et al., 2013). Non-edible trim loss from pork carcasses results from; adhesions, arthritis, and abscess (Keenlislide, 2005). Sow harvest facilities often encounter sow carcasses having one or more non-edible trim loss factors (Knauer, 2007). Non-edible trim loss observed repeatedly in high levels will result in carcass discounts to the seller. The objective of this study was to evaluate the economic impact of non-edible trim loss from cull sow carcasses. Data were collected as a convenience sample from a Midwestern cull-sow harvest facility. This facility focuses on harvesting high quality animals and harvesting “lean” or “boner” sows occurs relatively infrequently. For this study, trim was expressed as a percentage of carcass weight. At this harvest facility all carcasses have some non-edible trim loss. The average total pre-trim carcass weight was 149 kgs (n = 87). The relative percentage of non-edible trim loss was sorted into quartiles. Based on percent trim and average carcass weight the quartiles were classified as 1st Qu. = normal trim (0.5% – 1.2%, n = 22), 2nd Qu. = low trim (1.2% – 2.1%, n =20), 3rd Qu. = medium trim (2.1% – 3.4%, n = 21), and 4th Qu. = high trim (3.4% – 20.9%, n = 22). A 5-year average cull sow price (USDA, ERS) was utilized to calculate the economic loss represented from each quartile of percentage trim. Normal trim, low trim and medium trim showed to have low economic impact. High trim loss had an average economic loss of $9.37 (s.d. = 6.9) on a standardized basis. Substantial economic losses are observed when high trim is measured. Additional work is needed in identifying significant trim loss prior to harvest.

Optimasi Trim Loss menggunakan Integer Linear Programming pada Cutting Stock Problem untuk industri meubel (Studi Kasus pada UD. Flybers)

Trim loss merupakan kerugian yang timbul dari hasil pemotongan yang tidak optimal. Trim loss dipengaruhi beberapa faktor salah satunya yaitu peletakan pola pemotongan yang kurang tepat. Trim loss dapat diselesaikan dengan beberapa metode salah satunya menggunakan metode cutting stock. cutting stock digunakan pada pengoptimalan pemotongan sisa material yang tidak dapat digunakan lagi. Pada cutting stock dipengaruhi oleh masalah pola pemotongan disebut cutting stock problem(CSP). CSP dapat diselesaikan dengan menggunakan pendekatan integer linear programming (ILP). ILP adalah salah satu model dalam program linear yang variabel keputusannya berbentuk bilangan positif atau nol.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

Column Generation Model in Capacitated Multi-Periods Cutting Stock Problem with Pattern Set-Up Cost

Cutting Stock Problem (CSP) determines the cutting of stocks with standard length and width to meet the item’s demand. The optimal cutting pattern will minimize the usage of stocks and trim loss. This research implemented the pattern generation algorithm to form the Gilmore-Gomory and Column Generation model in two-dimensional CSP. The CSP in this research had three periods of cutting with different capacities in each period. The Column Generation model added the pattern set-up cost as the constraint. The Gilmore-Gomory model ensured that the first stage’s strips were used in the second stage and met the item’s demand. Based on the Column Generation model’s solution, the 1st period used the 2nd, 4th, and 5th patterns, the 2nd period used 4th and 5th patterns, and the 3rd period did not use any patterns. The first and second periods fulfilled all of the demands.

PSV-3 Detection of Bruising in Holstein Slaughter Cows with Infrared Thermography

Identifying severe bruising in livestock before slaughter is valuable because bruises are likely a source of pain for the animal that cannot be seen by visual inspection. This bruising may result in major trim losses because bruised tissue is considered unfit for human consumption. The objective of this study was to determine if bruising could be detected under the hide of live Holstein cull cows with infrared thermal imaging technology. A FLIR E8 thermal imaging camera was utilized to capture heat emission images pre-hide removal and post hide removal for 36 Holstein cull cows. Images were collected approximately 2 m from each cow and associated carcass images were examined on FLIR Tools software to determine if and where inflammation was detected on the carcass. Trimming occurs throughout the slaughter process, but carcasses may be ‘railed out’ if additional trimming is necessary. Trim loss was collected from railed out carcasses and weighed. The impact of rail-out status on trim loss was significant (P < 0.0001). Carcasses that were railed out (n = 15) yielded 16.2 ± 2.0 kg of trim loss. Carcasses that were not railed out (n = 21) did not experience additional trim loss. Carcasses railed out for additional trimming where inflammation suggested bruising existed (n = 7) yielded 21.0 ± 4.8 kg of trim loss. Carcasses railed out for trimming where thermography did not detect bruising (n = 4) yielded 12.6 ± 6.4 kg of trim loss. The relationship between thermographic detection of bruises and trim loss among railed-out carcasses was not significant (P = 0.3200). Inflammation that predicted rail-out events was 63.64% while carcasses in which rail-out was not detected by thermography was 36.36%. It appears that infrared thermography has potential to identify carcass bruising but additional investigation is warranted to understand its limitations.

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

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.

From unloading to trimming: studying bruising in individual slaughter cattle

Livestock bruising is both an animal welfare concern and a detriment to the economic value of carcasses. Understanding the causes of bruising is challenging due to the numerous factors that have been shown to be related to bruise prevalence. While most cattle bruising studies collect and analyze data on truckload lots of cattle, this study followed a large number (n = 585) of individual animals from unloading through postmortem processing at five different slaughter plants. Both visual bruise presence and location was recorded postmortem prior to carcass trimming. By linking postmortem data to animal sex, breed, trailer compartment, and traumatic events at unloading, a rich analysis of a number of factors related to bruise prevalence was developed. Results showed varying levels of agreement with other published bruising studies, underscoring the complexity of assessing the factors that affect bruising. Bruising prevalence varied across different sex class types (P < 0.001); 36.5% of steers [95% confidence interval (CI): 31.7, 41.6; n = 378], 52.8% of cows (45.6, 60.0; 193), and 64.3% of bulls (no CI calculated due to sample size; 14) were bruised. There was a difference in bruise prevalence by trailer compartment (P = 0.035) in potbelly trailers, indicating that cattle transported in the top deck were less likely to be bruised (95% CI: 26.6, 40.4; n = 63) compared to cattle that were transported in the bottom deck (95% CI: 39.6, 54.2; n = 89). Results indicated that visual assessment of bruising underestimated carcass bruise trimming. While 42.6% of the carcasses were visibly bruised, 57.9% of carcasses were trimmed due to bruising, suggesting that visual assessment is not able to capture all of the carcass loss associated with bruising. Furthermore, bruises that appeared small visually were often indicators of larger, subsurface bruising, creating an “iceberg effect” of trim loss due to bruising.

Solving a Real Problem in Plastic Industry: A Case in Trim-loss Problem

In this paper, a cutting plane model is presented for solving a problem in a cast polypropylene (CPP) plastic film manufacturer. The company produces plastic rolls from plastic pellets with widths ranging from 3 050 mm to 3 250 mm. The plastic rolls are trimmed according to customer’s orders. In prior to the trimming process, the production planning and inventory control (PPIC) department scheduled the machines and arranged the plastic trim compositions manually. In this work, the plastic trimming problem is solved by applying the trim loss model. Since trimmed loss problem is an NP-hard problem. In this case, the permutations are selected in advance so that the total length is feasible to the machine length. The computation is carried out using visual basic for application (VBA). The model outcomes are then used for optimizing the machine scheduling process. Modified earliest due date is proposed to schedule in which machines customer’s orders should be done. The machines scheduling represents the company conditions and the cutting production can be scheduled for daily basis. Keywords: cutting plane; cutting stock; earliest due date; machine scheduling; non-polinomial-hard problem.

3-Phase Matheuristic Model in Two-Dimensional Cutting Stock Problem of Triangular Shape Items

Cutting Stock Problem (CSP) is a problem of cutting stocks with certain cutting rules. This study used the data of rectangular stocks, which cut into triangular shape items with various order sizes. The Modified Branch and Bound Algorithm (MBBA) was used to determine the optimum cutting pattern then formulated it into the 3-Phase Matheuristic model which consisted of constructive phase, improvement phase, and compaction phase. Based on the results, it showed that the MBBA produces three optimum cutting patterns, which was used six times, eight times, and four times respectively to fulfill the consumer demand. Then the cutting patterns were formulated into the 3-Phase Matheuristic model whereas the optimum solution was the minimum trim loss for the first, second and third patterns.