A note on bi-objective optimization for sustainable supply chain network design in omnichannel

PurposeThis paper aims to correct the existing mixed integer programming (MIP) model proposed by Yadav et al. (2019) [“Bi-objective optimization for sustainable supply chain network design in omnichannel.”, Journal of Manufacturing Technology Management, Vol. 30 No. 6, pp. 972–986].Design/methodology/approachThis paper first presents a counterexample to show that the existing MIP model is incorrect and then proposes an improved mixed integer linear programming (MILP) model for the considered problem. Last, a numerical experiment is conducted to test our improved MILP model.FindingsThis paper demonstrates that the formulations of the facility capacity constraints and the product flow balance constraints in the existing MIP model are incorrect and incomplete. Due to this reason, infeasible solutions could be identified as feasible ones by the existing MIP model. Hence, the optimal solution obtained with the existing MIP model could be infeasible. A counter-example is used to verify our observations. Computational results verify the effectiveness of our improved MILP model.Originality/valueThis paper gives a complete and correct formulation of the facility capacity constraints and the product flow balance constraints, and conducts other improvements on the existing MIP model. The improved MILP model can be easily implemented and would help companies to have more effective distribution networks under the omnichannel environment.

224 Amino Acid Imbalance with Excess Methionine in Late-finishing Pigs: Effects on Performance and Carcass Quality

Limitations in harvest facility capacity can necessitate feeding programs to slow growth rate in late-finishing pigs. In Trial 1, six dietary regimens were used with 423 pigs (initial weight, 109 kg) randomly allotted in a 35 d study. The regimens consisted of: 1) Con (11.7% CP diet), 2) Low Protein (LP 8.8% CP), 3) Methionine (Met) 1% (LP plus 1% added Met for 35 d), 4) Met 1–1.8% (LP plus 1, 1.2, 1.4, 1.6 and 1.8% added Met for d 0–7, 7–14, 14–21, 21–28 and 28–35 d, respectively), 5) Met 2% (LP plus 2% added Met for 35 d) and 6) Met 1.5–2.3% (LP plus 1.5, 1.7, 1.9, 2.1 and 2.3%, added Met for d 0–7, 7–14, 14–21, 21–28 and 28–35 d, respectively). Pigs on Met 1% had reduced overall ADG and ADFI (P < 0.05) with similar G:F vs. pigs on LP. Compared to pigs on Met 1%, pigs on Met 1–1.8%, Met 2%, and Met 1.5–2.3% had reduced (P < 0.05) ADG of 28, 54 and 53%, respectively; and decreased (P < 0.05) ADFI of 12, 26 and 26%, respectively. In Trial 2, three regimens were used with 393 pigs (initial weight, 109 kg) in a 35 d study. The regimens consisted of 1) Met 2%, 2) Met 1.5–2.3% and 3) Met 2.0–2.8% (LP plus 2.0, 2.2, 2.4, 2.6 and 2.8% added Met for d 0–7, 7–14, 14–21, 21–28 and 28–35 d, respectively). Regimens 1 and 2 were identical to Trial 1. Overall, pigs on Met 1.5–2.3% had ADG and ADFI which were greater (P < 0.001) compared to pigs on Met 2%. Pigs on Met 2–2.8% had an overall ADG and ADFI which were less (P < 0.05) than those pigs on Met 2%. These data suggest that growth can be manipulated with excess Met.

Amino acid imbalance with excess methionine in late-finishing pigs: effects on performance and carcass quality

Limitations in harvest facility capacity can necessitate feeding strategies to slow growth rate in late-finishing pigs. In Exp. 1, six dietary treatments were used with 423 late-finishing pigs (initial weight of 109.5 kg) randomly allotted (mixed-sex) across 24 pens in a 35 d study. The treatments consisted of: 1) Positive Control (PC, 11.7% CP (Crude Protein) diet with added amino acids (AA), 2) Methionine (Met) 0% (8.8% CP with added AA), 3) Met 1% (8.8% CP with added AA plus 1% added Met for 35 d), 4) Met 1-1.8% (8.8% CP with added AA plus 1, 1.2, 1.4, 1.6 and 1.8% added Met for d 0-7, 7-14, 14-21, 21-28 and 28-35 d, respectively), 5) Met 2% (8.8% CP with added AA plus 2% added Met for 35 d) and 6) Met 1.5-2.3% (8.8% CP with added AA plus 1.5, 1.7, 1.9, 2.1 and 2.3% added Met for d 0-7, 7-14, 14-21, 21-28 and 28-35 d, respectively). Overall, pigs on the PC had improved ADG and G:F (P < 0.05) compared to the other five treatments. Pigs on Met 1% had reduced overall ADG and ADFI (P < 0.05) with similar G:F compared to pigs on Met 0%. Compared to pigs on Met 1%, pigs on Met 1-1.8%, Met 2%, and Met 1.5-2.3% had reduced (P < 0.05) ADG of 28, 54 and 53%, respectively; and decreased (P < 0.05) ADFI of 12, 26 and 26%, respectively. Dressing percent was similar among all six treatments. In Exp. 2, three dietary treatments were used with 393 late-finishing pigs (average initial weight was 108.8 kg) randomly allotted (mixed-sex) across 24 pens in a 35-d study. The treatments consisted of 1) Met 2%, 2) Met 1.5-2.3% and 3) Met 2.0-2.8% (8.8% CP with added AA plus 2.0, 2.2, 2.4, 2.6 and 2.8% added Met for d 0-7, 7-14, 14-21, 21-28 and 28-35 d, respectively). Treatments 1 and 2 were identical to Exp. 1. Overall, pigs on Met 1.5-2.3% had ADG of 241 g and ADFI of 2,040 g which were greater (P < 0.001) compared to pigs on Met 2% with ADG of 57 g and ADFI of 1,515 g. Furthermore, pigs on Met 2-2.8% had an overall ADG of -62 g and ADFI of 1,254 g which were less (P < 0.05) than those pigs on Met 2%. Percent lean and dressing percent were similar among the three treatments. These data suggest that late-finishing growth can be manipulated in a concise and effective manner by creating amino acid imbalances with varying levels of excess Met.

590 Influence of Hospital Characteristics on Patient Outcomes Following Cancer Surgery: An International, Mixed Methods Study Across 66 Countries

Aim Early death after cancer surgery is higher in low- and middle-income settings, yet the impact of hospital characteristics on early postoperative outcomes following cancer surgery worldwide are unknown. Method A mixed-methods analysis was performed using data from the GlobalSurg 3 study, a multicentre, international prospective cohort study of patients who underwent surgery for breast, gastric or colorectal cancer. The primary outcomes were 30-day mortality and major complication rates. Hierarchical clustering identified distinct hospital characteristics to create a facility capacity framework. Adjusted outcomes were determined after accounting for patient characteristics and country-income group. Results Hospital-level data were available for 9685 patients across 238 hospitals. The facility capacity framework consisted of seven distinct hospital resources and processes: presence of CT scan, postoperative recovery area, critical care facilities, opioid analgesia availability, oncologist, pathology and radiotherapy services. While complication rates were similar across hospitals with varying facility capacities, hospitals with five or less capacities (116 hospitals, 2251 patients) had increased mortality (aOR 1.67, 95% CI 1.13-2.48; P = 0.010). After adjustment for case-mix and country income group, patients undergoing surgery for gastric and colorectal cancer in hospitals with reduced capacities had higher mortality (6.2 vs. 4.0%; P < 0.001), predominantly explained by limited capacity to rescue following the development of major complications (61.3 vs. 72.1%; P < 0.001). Conclusions Hospitals with certain system capacities achieve better outcomes following cancer surgery, independent of country-income group. For cancers amenable to surgical treatment, national cancer care plans should include both surgical capacity building and concurrent hospital facility development to maximise reductions in cancer-associated mortality.

The Transformative Environmental Effects Large-Scale Indoor Farming May Have On Air, Water, and Soil

This article identifies the potential environmental effects large-scale indoor farming may have on air, water, and soil. We begin with an overview of what indoor farming is with a focus on greenhouses and indoor vertical farms (eg, plant factories). Next, the differences between these 2 primary methods of indoor farming are presented based on their structural requirements, methods of growing, media, nutrient sources, lighting requirements, facility capacity, and methods of climate control. We also highlight the benefits and challenges facing indoor farming. In the next section, an overview of research and the knowledge domain of indoor and vertical farming is provided. Various authors and topics for research are highlighted. In the next section, the transformative environmental effects that indoor farming may have on air, soil, and water are discussed. This article closes with suggestions for additional research on indoor farming and its influence on the environment.

Nutritional approaches to slow late finishing pig growth: implications on carcass composition and pork quality

Although pork producers typically aim to optimize growth rates, occasionally it is necessary to slow growth, such as when harvest facility capacity is limited. In finishing pigs, numerous dietary strategies can be used to slow growth so pigs are at optimal slaughter body weights when harvest facility capacity and/or access is restored. However, the impact of these diets on pork carcass quality is largely unknown. Thus, this study aimed to evaluate the efficacy of dietary strategies to slow growth in late finishing pigs and evaluate their effects on carcass composition and pork quality. Mixed-sex pigs (n = 897; 125 ± 2 kg BW) were randomly allotted across 48 pens and assigned to 1 of 6 dietary treatments (n = 8 pens/treatment): (1) Control diet representative of a typical finisher diet (CON); (2) diet containing 3% calcium chloride (CaCl2); (3) diet containing 97% corn and no soybean meal (Corn); (4) diet deficient in isoleucine (LowIle); (5) diet containing 15% neutral detergent fiber (NDF) from soybean hulls (15% NDF); and (6) diet containing 20% NDF from soybean hulls (20% NDF). Over 42 d, pen body weights and feed disappearance were collected. Pigs were harvested in 3 groups (14, 28, and 42 d on feed) and carcass data collected. From the harvest group, 1 loin was collected from 120 randomly selected carcasses (20 loins/treatment) to evaluate pork quality traits. Overall, ADG was reduced in CaCl2, Corn, and 20% NDF pigs compared with CON pigs (P < 0.001). However, ADFI was only reduced in CaCl2 and 20% NDF pigs compared with CON (P < 0.001). Feed efficiency was reduced in CaCl2 and Corn pigs compared with CON (P < 0.001). Hot carcass weights were reduced in CaCl2 pigs at all harvest dates (P < 0.001) and were reduced in Corn and 20% NDF pigs at days 28 and 42 compared with CON pigs (P < 0.001). In general, CaCl2 and 20% NDF diets resulted in leaner carcasses, whereas the Corn diet increased backfat by 42 d on test (P < 0.05). Loin pH was reduced and star probe increased in CaCl2 pigs compared with CON pigs (P < 0.05); no treatments differed from CON pigs regarding drip loss, cook loss, color, firmness, or marbling (P ≥ 0.117). Overall, these data indicate that several dietary strategies can slow finishing pig growth without evidence of behavioral vices. However, changes to carcass composition and quality were also observed, indicating quality should be taken into consideration when choosing diets to slow growth.

Forecasting of Power Demands Using Deep Learning

The forecasting of electricity demands is important for planning for power generator sector improvement and preparing for periodical operations. The prediction of future electricity demand is a challenging task due to the complexity of the available demand patterns. In this paper, we studied the performance of the basic deep learning models for electrical power forecasting such as the facility capacity, supply capacity, and power consumption. We designed different deep learning models such as convolution neural network (CNN), recurrent neural network (RNN), and a hybrid model that combines both CNN and RNN. We applied these models to the data provided by the Korea Power Exchange. This data contains the daily recordings of facility capacity, supply capacity, and power consumption. The experimental results showed that the CNN model outperforms the other two models significantly for the three features forecasting (facility capacity, supply capacity, and power consumption).

Decontaminating N95 respirators during the Covid-19 pandemic: simple and practical approaches to increase decontamination capacity, speed, safety and ease of use.

Background: The COVID-19 pandemic has caused a severe shortage of personal protective equipment (PPE), especially N95 respirators. Efficient, effective and economically feasible methods for large-scale PPE decontamination are urgently needed. Aims: (1) to develop protocols for effectively decontaminating PPE using vaporized hydrogen peroxide (VHP); (2) to develop novel approaches that decrease set up and take down time while also increasing decontamination capacity (3) to test decontamination efficiency for N95 respirators heavily contaminated by makeup or moisturizers. Methods: We converted a decommissioned Biosafety Level 3 laboratory into a facility that could be used to decontaminate N95 respirators. N95 respirators were hung on metal racks, stacked in piles, placed in paper bags or covered with makeup or moisturizer. A VHP VICTORYTM unit from STERIS was used to inject VHP into the facility. Biological and chemical indicators were used to validate the decontamination process. Findings: N95 respirators individually hung on metal racks were successfully decontaminated using VHP. N95 respirators were also successfully decontaminated when placed in closed paper bags or if stacked in piles of up to six. Stacking reduced the time needed to arrange N95 respirators for decontamination by approximately two-thirds while almost tripling facility capacity. Makeup and moisturizer creams did not interfere with the decontamination process. Conclusions: Respirator stacking can reduce the hands-on time and increase decontamination capacity. When personalization is needed, respirators can be decontaminated in labeled paper bags. Make up or moisturizers do not appear to interfere with VHP decontamination.