Determination of Sugars in Soft Drinks by High Performance Liquid Chromatography

The fructose, glucose and sucrose contents of selected soft' drinks available in Malaysian markets were determined by high performance liquid chromatography (HPLC). The soft drinks tested had a soluble sugar content of between 8.5 to 15.3 g 100 ml- 1 • The average fructose, glucose and sucrose contents werefound to be in the ranges of 0 - 6. 7, 0 - 6.9 and 0 - 10.5 g 100 ml I respectively. The content of individual sugars were found to be more variable than the content of total sugar in different samples of a specific soft drink

Shared pathways to infectious disease susceptibility?

The recent advent of genomic approaches for association testing is starting to enable a more comprehensive understanding of the role of human immune response in determining infectious disease outcomes. Progressing from traditional linkage approaches using microsatellite markers to high-resolution genome-wide association scans, these new approaches are leading to the robust discovery of a large number of disease susceptibility genes and the beginnings of an appreciation of their connections. In this commentary, we discuss how this technology development has led to increasingly complex and common infectious diseases being unraveled, and how this is starting to dissect pathogen-specific human responses. Intriguingly, these still preliminary findings suggest that pathogen innate detection mechanisms may not be as shared among diseases as immune response mechanisms

The scenario of two-dimensional instabilities of the cylinder wake under EHD forcing: A linear stability analysis

We propose to study the stability properties of an air flow wake forced by a dielectric barrier discharge (DBD) actuator, which is a type of electrohydrodynamic (EHD) actuator. These actuators add momentum to the flow around a cylinder in regions close to the wall and, in our case, are symmetrically disposed near the boundary layer separation point. Since the forcing frequencies, typical of DBD, are much higher than the natural shedding frequency of the flow, we will be considering the forcing actuation as stationary. In the first part, the flow around a circular cylinder modified by EHD actuators will be experimentally studied by means of particle image velocimetry (PIV). In the second part, the EHD actuators have been numerically implemented as a boundary condition on the cylinder surface. Using this boundary condition, the computationally obtained base flow is then compared with the experimental one in order to relate the control parameters from both methodologies. After validating the obtained agreement, we study the Hopf bifurcation that appears once the flow starts the vortex shedding through experimental and computational approaches. For the base flow derived from experimentally obtained snapshots, we monitor the evolution of the velocity amplitude oscillations. As to the computationally obtained base flow, its stability is analyzed by solving a global eigenvalue problem obtained from the linearized Navier–Stokes equations. Finally, the critical parameters obtained from both approaches are compared

MTHFR C677T polymorphism, homocysteine and B vitamins status in a sample of Chinese and Malay subjects in Universiti Putra Malaysia.

INTRODUCTION: Methylenetetrahydrofolate reductase (MTHFR) C677T is involved in folate and homocysteine metabolism. Disruption in the activity of this enzyme will alter their levels in the body. METHODOLOGY: This study assessed MTHFR C677T polymorphism and its relationship with serum homocysteine and B-vitamins levels in a sample of Chinese and Malays subjects in UPM, Serdang. One hundred subjects were randomly selected from among the university population. Folate, vitamin B12, B6, and homocysteine levels were determined using MBA, ECLIA, and HPLC, respectively. PCR coupled with HinfI digestion was used for detection of MTHFR C677T polymorphism. RESULTS: The frequency of T allele was higher in the Chinese subjects (0.40) compared to the Malay (0.14). Folate, vitamin B12 and B6 levels were highest in the wild genotype in both ethnic groups. Subjects with heterozygous and homozygous genotype showed the highest homocysteine levels. The serum folate and homocysteine were mainly affected by homozygous genotype. CONCLUSION: MTHFR C677T polymorphism plays an important role in influencing the folate and homocysteine metabolism

Pandemic preparedness in the 21st century: which way forward?

Comment - No abstract available

Local existence of analytic sharp fronts for singular SQG

In this paper, we prove local existence and uniqueness of analytic sharp-front solutions to a generalised SQG equation by the use of an abstract Cauchy–Kovalevskaya theorem. Here, the velocity is determined by $u=|nabla|^{-2beta} nabla^{perp} \theta$ which (for $1<beta \leq 2$ ) is more singular than in SQG. This is achieved despite the appearance of pseudodifferential operators of order higher than one in our equation, by recasting our equation in a suitable integral form. We also provide a full proof of the abstract version of the Cauchy–Kovalevskaya theorem we use

ATG5 regulates plasma cell differentiation

Autophagy is a conserved homeostatic process in which cytoplasmic contents are degraded and recycled. Two ubiquitin-like conjugation pathways are required for the generation of autophagosomes, and ATG5 is necessary for both of these processes. Studies of mice deficient in ATG5 reveal a key role for autophagy in T lymphocyte function, as well as in B cell development and B-1a B cell maintenance. However, the role of autophagy genes in B cell function and antibody production has not been described. Using mice in which Atg5 is conditionally deleted in B lymphocytes, we showed here that this autophagy gene is essential for plasma cell homeostasis. In the absence of B cell ATG5 expression, antibody responses were significantly diminished during antigen-specific immunization, parasitic infection and mucosal inflammation. Atg5-deficient B cells maintained the ability to produce immunoglobulin and undergo class-switch recombination, yet had impaired SDC1 expression, significantly decreased antibody secretion in response to toll-like receptor ligands, and an inability to upregulate plasma cell transcription factors. These results build upon previous data demonstrating a role for ATG5 in early B cell development, illustrating its importance in late B cell activation and subsequent plasma cell differentiation

Morphology, carbohydrate distribution, gene expression, and enzymatic activities related to cell wall hydrolysis in four barley varieties during simulated malting

Many biological processes, such as cell wall hydrolysis and the mobilisation of nutrient reserves from the starchy endosperm, require stringent regulation to successfully malt barley (Hordeum vulgare) grain in an industrial context. Much of the accumulated knowledge defining these events has been collected from individual, unrelated experiments, and data have often been extrapolated from Petri dish germination, rather than malting, experiments. Here, we present comprehensive morphological, biochemical, and transcript data from a simulated malt batch of the three elite malting cultivars Admiral, Navigator, and Flagship, and the feed cultivar Keel. Activities of lytic enzymes implicated in cell wall and starch depolymerisation in germinated grain have been measured, and transcript data for published cell wall hydrolytic genes have been provided. It was notable that Flagship and Keel exhibited generally similar patterns of enzyme and transcript expression, but exhibited a few key differences that may partially explain Flagship's superior malting qualities. Admiral and Navigator also showed matching expression patterns for these genes and enzymes, but the patterns differed from those of Flagship and Keel, despite Admiral and Navigator having Keel as a common ancestor. Overall (1,3;1,4)-β-glucanase activity differed between cultivars, with lower enzyme levels and concomitantly higher amounts of (1,3;1,4)-β-glucan in the feed variety, Keel, at the end of malting. Transcript levels of the gene encoding (1,3;1,4)-β-glucanase isoenzyme EI were almost three times higher than those encoding isoenzyme EII, suggesting a previously unrecognised importance for isoenzyme EI during malting. Careful morphological examination showed that scutellum epithelial cells in mature dry grain are elongated but expand no further as malting progresses, in contrast to equivalent cells in other cereals, perhaps demonstrating a morphological change in this critical organ over generations of breeding selection. Fluorescent immuno-histochemical labelling revealed the presence of pectin in the nucellus and, for the first time, significant amounts of callose throughout the starchy endosperm of mature grain.Natalie S. Betts, Laura G. Wilkinson, Shi F. Khor, Neil J. Shirley, Finn Lok, Birgitte Skadhauge, Rachel A. Burton, Geoffrey B. Fincher and Helen M. Collin

Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

[EN] Lack of homogeneity in the product (LHP) appears in some production processes that confer heterogeneity in the characteristics of the products obtained. Supply chains with this issue have to classify the product in different homogeneous subsets, whose quantity is uncertain during the production planning process. This paper proposes a generic framework for reviewing in a unified way the literature about production planning models dealing with LHP uncertainty. This analysis allows the identification of similarities among sectors to transfer solutions between them and gaps existing in the literature for further research. The results of the review show: (1) sectors affected by LHP inherent uncertainty, (2) the inherent LHP uncertainty types modelled, and (3) the approaches for modelling LHP uncertainty most widely employed. Finally, we suggest a conceptual model reflecting the aspects to be considered when modelling the production planning in sectors with LHP in an uncertain environment.This research was initiated within the framework of the project funded by the Ministerio de Economía y Competitividad [Ref. DPI2011-23597] entitled ‘Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity’ (PLANGES-FHP) already finished. After, the project leading to this application has received funding from the European Union’s research and innovation programme under the H2020 Marie Skłodowska-Curie Actions with the grant agreement No 691249, Project entitled ’Enhancing and implementing Knowledge based ICT solutions within high Riskand Uncertain Conditions for Agriculture Production Systems’ (RUC-APS).Mundi, I.; Alemany Díaz, MDM.; Poler, R.; Fuertes-Miquel, VS. (2019). Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model. International Journal of Production Research. 57(15-16):5239-5283. https://doi.org/10.1080/00207543.2019.1566665S523952835715-16Ahumada, O., Rene Villalobos, J., & Nicholas Mason, A. (2012). Tactical planning of the production and distribution of fresh agricultural products under uncertainty. Agricultural Systems, 112, 17-26. doi:10.1016/j.agsy.2012.06.002Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European Journal of Operational Research, 196(1), 1-20. doi:10.1016/j.ejor.2008.02.014Alarcón, F., Alemany, M. M. E., Lario, F. C., & Oltra, R. F. (2011). La falta de homogeneidad del producto (FHP) en las empresas cerámicas y su impacto en la reasignación del inventario. Boletín de la Sociedad Española de Cerámica y Vidrio, 50(1), 49-58. doi:10.3989/cyv.072011Albornoz, V. M., M. González-Araya, M. C. Gripe, and S. V. Rodrıguez. 2014. “A Mixed Integer Linear Program for Operational Planning in a Meat Packing Plant.” Accessed January 15, 2015. http://www.researchgate.net/profile/Victor_Albornoz/publication/268687089_A_Mixed_Integer_Linear_Program_for_Operational_Planning_in_a_Meat_Packing_Plant/links/547382bf0cf29afed60f55c7.pdf.José Alem, D., & Morabito, R. (2012). Production planning in furniture settings via robust optimization. Computers & Operations Research, 39(2), 139-150. doi:10.1016/j.cor.2011.02.022Alemany, M. M. E., Lario, F.-C., Ortiz, A., & Gómez, F. (2013). Available-To-Promise modeling for multi-plant manufacturing characterized by lack of homogeneity in the product: An illustration of a ceramic case. Applied Mathematical Modelling, 37(5), 3380-3398. doi:10.1016/j.apm.2012.07.022Alemany, M., Ortiz, A., & Fuertes-Miquel, V. S. (2018). A decision support tool for the order promising process with product homogeneity requirements in hybrid Make-To-Stock and Make-To-Order environments. Application to a ceramic tile company. Computers & Industrial Engineering, 122, 219-234. doi:10.1016/j.cie.2018.05.040Alfalla-Luque, R., Medina-Lopez, C., & Dey, P. K. (2012). Supply chain integration framework using literature review. Production Planning & Control, 24(8-9), 800-817. doi:10.1080/09537287.2012.666870Al-Othman, W. B. E., Lababidi, H. M. S., Alatiqi, I. M., & Al-Shayji, K. (2008). Supply chain optimization of petroleum organization under uncertainty in market demands and prices. European Journal of Operational Research, 189(3), 822-840. doi:10.1016/j.ejor.2006.06.081Al-Shammari, A., & Ba-Shammakh, M. S. (2011). Uncertainty Analysis for Refinery Production Planning. Industrial & Engineering Chemistry Research, 50(11), 7065-7072. doi:10.1021/ie200313rAmaro, A. C. S., & Barbosa-Póvoa, A. P. F. D. (2009). The effect of uncertainty on the optimal closed-loop supply chain planning under different partnerships structure. Computers & Chemical Engineering, 33(12), 2144-2158. doi:10.1016/j.compchemeng.2009.06.003ARAS, N., BOYACI, T., & VERTER, V. (2004). The effect of categorizing returned products in remanufacturing. IIE Transactions, 36(4), 319-331. doi:10.1080/07408170490279561Aydin, R., Kwong, C. K., Geda, M. W., & Okudan Kremer, G. E. (2017). Determining the optimal quantity and quality levels of used product returns for remanufacturing under multi-period and uncertain quality of returns. The International Journal of Advanced Manufacturing Technology, 94(9-12), 4401-4414. doi:10.1007/s00170-017-1141-0Bakhrankova, K., Midthun, K. T., & Uggen, K. T. (2014). Stochastic optimization of operational production planning for fisheries. Fisheries Research, 157, 147-153. doi:10.1016/j.fishres.2014.03.018Banasik, A., Kanellopoulos, A., Claassen, G. D. H., Bloemhof-Ruwaard, J. M., & van der Vorst, J. G. A. J. (2017). Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain. International Journal of Production Economics, 183, 409-420. doi:10.1016/j.ijpe.2016.08.012Beaudoin, D., LeBel, L., & Frayret, J.-M. (2007). Tactical supply chain planning in the forest products industry through optimization and scenario-based analysis. Canadian Journal of Forest Research, 37(1), 128-140. doi:10.1139/x06-223Begen, M. A., & Puterman, M. L. (2003). Development Of A Catch Allocation Tool Design For Production Planning At Js Mcmillan Fisheries. INFOR: Information Systems and Operational Research, 41(3), 235-244. doi:10.1080/03155986.2003.11732678Benedito, E., & Corominas, A. (2010). Optimal manufacturing and remanufacturing capacities of systems with reverse logistics and deterministic uniform demand. Journal of Industrial Engineering and Management, 3(1). doi:10.3926/jiem.2010.v3n1.p33-53Bertrand, J. W. ., & Rutten, W. G. M. . (1999). Evaluation of three production planning procedures for the use of recipe flexibility. European Journal of Operational Research, 115(1), 179-194. doi:10.1016/s0377-2217(98)00166-0Björheden, R., & Helstad, K. (2005). Raw Material Procurement in Sawmills’ Business Level Strategy-A Contingency Perspective. International Journal of Forest Engineering, 16(2), 47-56. doi:10.1080/14942119.2005.10702513Bohle, C., Maturana, S., & Vera, J. (2010). A robust optimization approach to wine grape harvesting scheduling. European Journal of Operational Research, 200(1), 245-252. doi:10.1016/j.ejor.2008.12.003Cai, X., Lai, M., Li, X., Li, Y., & Wu, X. (2014). Optimal acquisition and production policy in a hybrid manufacturing/remanufacturing system with core acquisition at different quality levels. European Journal of Operational Research, 233(2), 374-382. doi:10.1016/j.ejor.2013.07.017Carneiro, M. C., Ribas, G. P., & Hamacher, S. (2010). Risk Management in the Oil Supply Chain: A CVaR Approach. Industrial & Engineering Chemistry Research, 49(7), 3286-3294. doi:10.1021/ie901265nChakraborty, M., & Chandra, M. K. (2005). Multicriteria decision making for optimal blending for beneficiation of coal: a fuzzy programming approach. Omega, 33(5), 413-418. doi:10.1016/j.omega.2004.07.005LUO, C., & RONG, G. (2009). A Strategy for the Integration of Production Planning and Scheduling in Refineries under Uncertainty. Chinese Journal of Chemical Engineering, 17(1), 113-127. doi:10.1016/s1004-9541(09)60042-2Davoli, G., Gallo, S., Collins, M., & Melloni, R. (2011). A stochastic simulation approach for production scheduling and investment planning in the tile industry. International Journal of Engineering, Science and Technology, 2(9). doi:10.4314/ijest.v2i9.64006Denizel, M., Ferguson, M., & Souza, G. (2010). Multiperiod Remanufacturing Planning With Uncertain Quality of Inputs. IEEE Transactions on Engineering Management, 57(3), 394-404. doi:10.1109/tem.2009.2024506Dong, M., Lu, S., & Han, S. (2011). Production Planning for Hybrid Remanufacturing and Manufacturing System with Component Recovery. Advances in Electrical Engineering and Electrical Machines, 511-518. doi:10.1007/978-3-642-25905-0_66Dubois, D., Fargier, H., & Fortemps, P. (2003). Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge. European Journal of Operational Research, 147(2), 231-252. doi:10.1016/s0377-2217(02)00558-1DUENYAS, I., & TSAI, C.-Y. (2000). Control of a manufacturing system with random product yield and downward substitutability. IIE Transactions, 32(9), 785-795. doi:10.1080/07408170008967438Esteso, A., Alemany, M. M. E., & Ortiz, A. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research, 56(13), 4418-4446. doi:10.1080/00207543.2018.1447706French, M. L., & LaForge, R. L. (2005). Closed-loop supply chains in process industries: An empirical study of producer re-use issues. Journal of Operations Management, 24(3), 271-286. doi:10.1016/j.jom.2004.07.012Gallo, M., R. Grisi, G. Guizzi, and E. Romano. 2009. “A Comparison of Production Policies in Remanufacturing Systems,” Proceedings of the 8th WSEAS International Conference on System Science and Simulation in Engineering, ICOSSSE ‘09, pp. 334.Goodfellow, R., & Dimitrakopoulos, R. (2017). Simultaneous Stochastic Optimization of Mining Complexes and Mineral Value Chains. Mathematical Geosciences, 49(3), 341-360. doi:10.1007/s11004-017-9680-3Graves, S. C. (2010). Uncertainty and Production Planning. Planning Production and Inventories in the Extended Enterprise, 83-101. doi:10.1007/978-1-4419-6485-4_5Grillo, H., Alemany, M. M. E., Ortiz, A., & Fuertes-Miquel, V. S. (2017). Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products. Applied Mathematical Modelling, 49, 255-278. doi:10.1016/j.apm.2017.04.037Guan, Z., & Philpott, A. B. (2011). A multistage stochastic programming model for the New Zealand dairy industry. International Journal of Production Economics, 134(2), 289-299. doi:10.1016/j.ijpe.2009.11.003Guide, V. D. R. (2000). Production planning and control for remanufacturing: industry practice and research needs. Journal of Operations Management, 18(4), 467-483. doi:10.1016/s0272-6963(00)00034-6Gupta, V., & Grossmann, I. E. (2011). Solution strategies for multistage stochastic programming with endogenous uncertainties. Computers & Chemical Engineering, 35(11), 2235-2247. doi:10.1016/j.compchemeng.2010.11.013Gupta, S., and Z. Nan. 2006. “‘Multiperiod Planning of Refinery Operations Under Market Uncertainty,’ AIChE Annual Meeting.” Conference Proceedings.Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk – Definition, measure and modeling. Omega, 52, 119-132. doi:10.1016/j.omega.2014.10.004Heydari, J., & Ghasemi, M. (2018). A revenue sharing contract for reverse supply chain coordination under stochastic quality of returned products and uncertain remanufacturing capacity. Journal of Cleaner Production, 197, 607-615. doi:10.1016/j.jclepro.2018.06.206Hovelaque, V., Duvaleix-Tréguer, S., & Cordier, J. (2009). Effects of constrained supply and price contracts on agricultural cooperatives. European Journal of Operational Research, 199(3), 769-780. doi:10.1016/j.ejor.2008.08.005Hsieh, S., & Chiang, C.-C. (2001). Manufacturing-to-Sale Planning Model for Fuel Oil Production. The International Journal of Advanced Manufacturing Technology, 18(4), 303-311. doi:10.1007/s001700170070Igarashi, M., de Boer, L., & Fet, A. M. (2013). What is required for greener supplier selection? A literature review and conceptual model development. Journal of Purchasing and Supply Management, 19(4), 247-263. doi:10.1016/j.pursup.2013.06.001Jamshidi, M., & Osanloo, M. (2019). Reliability analysis of production schedule in multi-element deposits under grade-tonnage uncertainty with multi-destinations for the run of mine material. International Journal of Mining Science and Technology, 29(3), 483-489. doi:10.1016/j.ijmst.2018.04.016Jin, X., Hu, S. J., Ni, J., & Xiao, G. (2013). Assembly Strategies for Remanufacturing Systems With Variable Quality Returns. IEEE Transactions on Automation Science and Engineering, 10(1), 76-85. doi:10.1109/tase.2012.2217741Jindal, A., & Sangwan, K. S. (2016). Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors. Annals of Operations Research, 257(1-2), 95-120. doi:10.1007/s10479-016-2219-zJohnson, P., G. Evatt, P. Duck, and S. Howell. 2010. “The Derivation and Impact of an Optimal Cut-off Grade Regime Upon Mine Valuations,” Proceedings of the World Congress on Engineering 2010 Vol I.Junior, M. L., & Filho, M. G. (2011). Production planning and control for remanufacturing: literature review and analysis. Production Planning & Control, 23(6), 419-435. doi:10.1080/09537287.2011.561815Kamrad, B., & Ernst, R. (2001). An Economic Model for Evaluating Mining and Manufacturing Ventures with Output Yield Uncertainty. Operations Research, 49(5), 690-699. doi:10.1287/opre.49.5.690.10610Kannegiesser, M., Günther, H.-O., van Beek, P., Grunow, M., & Habla, C. (2008). Value chain management for commodities: a case study from the chemical industry. OR Spectrum, 31(1), 63-93. doi:10.1007/s00291-008-0124-9Karabuk, S. (2008). Production planning under uncertainty in textile manufacturing. Journal of the Operational Research Society, 59(4), 510-520. doi:10.1057/palgrave.jors.2602370Khor, C. S., Elkamel, A., & Douglas, P. L. (2008). Stochastic Refinery Planning with Risk Management. Petroleum Science and Technology, 26(14), 1726-1740. doi:10.1080/10916460701287813Kumral, M. (2004). Genetic algorithms for optimization of a mine system under uncertainty. Production Planning & Control, 15(1), 34-41. doi:10.1080/09537280310001654844Lalmazloumian, M., and K. Y. Wong. 2012. “A Review of Modelling Approaches for Supply Chain Planning Under Uncertainty,” Service Systems and Service Management (ICSSSM), 2012 9th International Conference on, pp. 197.Leiras, A., Ribas, G., Hamacher, S., & Elkamel, A. (2013). Tactical and Operational Planning of Multirefinery Networks under Uncertainty: An Iterative Integration Approach. Industrial & Engineering Chemistry Research, 52(25), 8507-8517. doi:10.1021/ie302835nLiao, H., Deng, Q., & Wang, Y. (2017). Optimal Acquisition and Production Policy for End-of-Life Engineering Machinery Recovering in a Joint Manufacturing/Remanufacturing System under Uncertainties in Procurement and Demand. Sustainability, 9(3), 338. doi:10.3390/su9030338Loomba, A. P. S., & Nakashima, K. (2011). Enhancing value in reverse supply chains by sorting before product recovery. Production Planning & Control, 23(2-3), 205-215. doi:10.1080/09537287.2011.591652Macedo, P. B., Alem, D., Santos, M., Junior, M. L., & Moreno, A. (2015). Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs. The International Journal of Advanced Manufacturing Technology, 82(5-8), 1241-1257. doi:10.1007/s00170-015-7445-zMartinez, L. 2009. “Why Accounting for Uncertainty and Risk Can Improve Final Decision-Making in Strategic Open Pit Mine Evaluation.” Project Evaluation Conference, Melbourne, pp. 1.Matamoros, M. E. V., & Dimitrakopoulos, R. (2016). Stochastic short-term mine production schedule accounting for fleet allocation, operational considerations and blending restrictions. European Journal of Operational Research, 255(3), 911-921. doi:10.1016/j.ejor.2016.05.050Meredith, J. (1993). Theory Building through Conceptual Methods. International Journal of Operations & Production Management, 13(5), 3-11. doi:10.1108/01443579310028120Miller, W. A., Leung, L. C., Azhar, T. M., & Sargent, S. (1997). Fuzzy production planning model for fresh tomato packing. International Journal of Production Economics, 53(3), 227-238. doi:10.1016/s0925-5273(97)00110-2Mitra, K. (2009). Multiobjective optimization of an industrial grinding operation under uncertainty. Chemical Engineering Science, 64(23), 5043-5056. doi:10.1016/j.ces.2009.08.012Moghaddam, K. S. (2015). Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty. Expert Systems with Applications, 42(15-16), 6237-6254. doi:10.1016/j.eswa.2015.02.010Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204(3), 377-390. doi:10.1016/j.ejor.2009.09.008Mula, J., Peidro, D., & Poler, R. (2010). The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand. International Journal of Production Economics, 128(1), 136-143. doi:10.1016/j.ijpe.2010.06.007MUNDI, I., ALEMANY, M. M. E., BOZA, A., & POLER, R. (2013). A Model-Driven Decision Support System for the Master Planning of Ceramic Supply Chains with Non-uniformity of Finished Goods. Studies in Informatics and Control, 22(2). doi:10.24846/v22i2y201305Mundi, M. I., Alemany, M. M. E., Poler, R., & Fuertes-Miquel, V. S. (2016). Fuzzy sets to model master production effectively in Make to Stock companies with Lack of Homogeneity in the Product. Fuzzy Sets and Systems, 293, 95-112. doi:10.1016/j.fss.2015.06.009Munhoz, J. R., & Morabito, R. (2014). Optimization approaches to support decision making in the production planning of a citrus company: A Brazilian case study. Computers and Electronics in Agriculture, 107, 45-57. doi:10.1016/j.compag.2014.05.016Olivetti, E. A., Gaustad, G. G., Field, F. R., & Kirchain, R. E. (2011). Increasing Secondary and Renewable Material Use: A Chance Constrained Modeling Approach To Manage Feedstock Quality Variation. Environmental Science & Technology, 45(9), 4118-4126. doi:10.1021/es103486sOsmani, A., & Zhang, J. (2013). Stochastic optimization of a multi-feedstock lignocellulosic-based bioethanol supply chain under multiple uncertainties. Energy, 59, 157-172. doi:10.1016/j.energy.2013.07.043Paksoy, T., Pehlivan, N. Y., & Özceylan, E. (2012). Application of fuzzy optimization to a supply chain network design: A case study of an edible vegetable oils manufacturer. Applied Mathematical Modelling, 36(6), 2762-2776. doi:10.1016/j.apm.2011.09.060Pauls-Worm, K. G. J., Hendrix, E. M. T., Haijema, R., & van der Vorst, J. G. A. J. (2014). An MILP approximation for ordering perishable products with non-stationary demand and service level constraints. International Journal of Production Economics, 157, 133-146. doi:10.1016/j.ijpe.2014.07.020Peidro, D., Mula, J., Alemany, M. M. E., & Lario, F.-C. (2012). Fuzzy multi-objective optimisation for master planning in a ceramic supply chain. International Journal of Production Research, 50(11), 3011-3020. doi:10.1080/00207543.2011.588267Peidro, D., Mula, J., Jiménez, M., & del Mar Botella, M. (2010). A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. European Journal of Operational Research, 205(1), 65-80. doi:10.1016/j.ejor.2009.11.031Peidro, D., Mula, J., Poler, R., & Lario, F.-C. (2008). Quantitative models for supply chain planning under uncertainty: a review. The International Journal of Advanced Manufacturing Technology, 43(3-4), 400-420. doi:10.1007/s00170-008-1715-yPendharkar, P. C. (1997). A fuzzy linear programming model for production planning in coal mines. Computers & Operations Research, 24(12), 1141-1149. doi:10.1016/s0305-0548(97)00024-5Pendharkar, P. C. (2013). Scatter search based interactive multi-criteria optimization of fuzzy objectives for coal production planning. Engineering Applications of Artificial Intelligence, 26(5-6), 1503-1511. doi:10.1016/j.engappai.2013.01.001Pieter van Donk, D. (2000). Customer‐driven manufacturing in the food processing industry. British Food Journal, 102(10), 739-747. doi:10.1108/00070700010362176Pitty, S. S., Li, W., Adhitya, A., Srinivasan, R., & Karimi, I. A. (2008). Decision support for integrated refinery supply chains. Computers & Chemical Engineering, 32(11), 2767-2786. doi:10.1016/j.compchemeng.2007.11.006Poles, R., and F. Cheong. 2009. “A System Dynamics Model for Reducing Uncertainty in Remanufacturing Systems,” PACIS 2009–13th Pacific Asia Conference on Information Systems: IT Services in a Global Environment.Pongsakdi, A., Rangsunvigit, P., Siemanond, K., & Bagajewicz, M. J. (2006). Financial risk management in the planning of refinery operations. International Journal of Production Economics, 103(1), 64-86. doi:10.1016/j.ijpe.2005.04.007Radulescu, M., G. Zbaganu, and C. Z. Radulescu. 2008. “Crop Planning in the Presence of Production Quotas (Invited Paper),” Computer Modeling and Simulation, 2008.UKSIM 2008. Tenth International Conference on, pp. 549.Rajaram, K., & Karmarkar, U. S. (2002). Product Cycling With Uncertain Yields: Analysis and Application to the Process Industry. Operations Research, 50(4), 680-691. doi:10.1287/opre.50.4.680.2867Ramasesh, R. V., &