Application of Computer Simulation Modeling to Evaluate Business Continuity Plans

Business continuity plans (BCP) help organizations plan for and withstand the occurrence of unexpected events that interrupt the normal operation of business. Managers typically develop several alternate plans to minimize the business impact of unexpected events. The problem for decision makers is that comparative evaluation of BCP is typically done using subjective judgments. This research uses a case study approach focusing on a single organization and a single business continuity application to propose the use of computer simulation as a tool for managers to identify and evaluate different BCP prior to committing resources. In the context of an insurance firm, a specific plan was evaluated using simulation methods. A simulation model was used to model the operational aspects of the call center in an insurance company. After the model was validated, it was used to answer questions about what-if scenarios. Results suggest that scenario analysis using simulated model enables managers to ask useful questions that can help evaluate the plan. Managers at the insurance company used the simulation model to determine the level of service required and evaluate business continuity strategies to achieve it

Study of tyrosine and dopa enantiomers as tyrosinase substrates initiating L‐ and D‐melanogenesis pathways

Tyrosinase starts melanogenesis and determines its course, catalyzing the oxidation by molecular oxygen of tyrosine to dopa, and that of dopa to dopaquinone. Then, nonenzymatic coupling reactions lead to dopachrome, which evolves toward melanin. Recently, it has been reported that d‐tyrosine acts as tyrosinase inhibitor and depigmenting agent. The action of tyrosinase on the enantiomers of tyrosine (l‐tyrosine and d‐tyrosine) and dopa (l‐dopa and d‐dopa) was studied for the first time focusing on quantitative transient phase kinetics. Post‐steady‐state transient phase studies revealed that l‐dopachrome is formed more rapidly than d‐dopachrome. This is due to the lower values of Michaelis constants for l‐enantiomers than for d‐enantiomers, although the maximum rates are equal for both enantiomers. A deeper analysis of the inter‐steady‐state transient phase of monophenols demonstrated that the enantiomer d‐tyrosine causes a longer lag period and a lower steady‐state rate, than l‐tyrosine at the same concentration. Therefore, d‐melanogenesis from d‐tyrosine occurs more slowly than does l‐melanogenesis from l‐tyrosine, which suggests the apparent inhibition of melanin biosynthesis by d‐tyrosine. As conclusion, d‐tyrosine acts as a real substrate of tyrosinase, with low catalytic efficiency and, therefore, delays the formation of d‐melanin

Multiple-criteria cash-management policies with particular liquidity terms

[EN] Eliciting policies for cash management systems with multiple assets is by no means straightforward. Both the particular relationship between alternative assets and time delays from control decisions to availability of cash introduce additional difficulties. Here we propose a cash management model to derive short-term finance policies when considering multiple assets with different expected returns and particular liquidity terms for each alternative asset. In order to deal with the inherent uncertainty about the near future introduced by cash flows, we use forecasts as a key input to the model. We express uncertainty as lack of predictive accuracy and we derive a deterministic equivalent problem that depends on forecasting errors and preferences of cash managers. Since the assessment of the quality of forecasts is recommended, we describe a method to evaluate the impact of predictive accuracy in cash management policies. We illustrate this method through several numerical examples.Salas-Molina, F.; Pla Santamaría, D.; Garcia-Bernabeu, A.; Mayor-Vitoria, F. (2020). Multiple-criteria cash-management policies with particular liquidity terms. IMA Journal of Management Mathematics. 31(2):217-231. https://doi.org/10.1093/imaman/dpz010S217231312Abdelaziz, F. B., Aouni, B., & Fayedh, R. E. (2007). Multi-objective stochastic programming for portfolio selection. European Journal of Operational Research, 177(3), 1811-1823. doi:10.1016/j.ejor.2005.10.021Aouni, B., Ben Abdelaziz, F., & La Torre, D. (2012). The Stochastic Goal Programming Model: Theory and Applications. Journal of Multi-Criteria Decision Analysis, 19(5-6), 185-200. doi:10.1002/mcda.1466Aouni, B., Colapinto, C., & La Torre, D. (2014). Financial portfolio management through the goal programming model: Current state-of-the-art. European Journal of Operational Research, 234(2), 536-545. doi:10.1016/j.ejor.2013.09.040Baccarin, S. (2009). Optimal impulse control for a multidimensional cash management system with generalized cost functions. European Journal of Operational Research, 196(1), 198-206. doi:10.1016/j.ejor.2008.02.040Ballestero, E. (2001). Stochastic goal programming: A mean–variance approach. European Journal of Operational Research, 131(3), 476-481. doi:10.1016/s0377-2217(00)00084-9Ballestero, E., & Romero, C. (1998). Multiple Criteria Decision Making and its Applications to Economic Problems. doi:10.1007/978-1-4757-2827-9Bemporad, A., & Morari, M. (1999). Control of systems integrating logic, dynamics, and constraints. Automatica, 35(3), 407-427. doi:10.1016/s0005-1098(98)00178-2Cabello, J. G. (2013). Cash efficiency for bank branches. SpringerPlus, 2(1). doi:10.1186/2193-1801-2-334García Cabello, J., & Lobillo, F. J. (2017). Sound branch cash management for less: A low-cost forecasting algorithm under uncertain demand. Omega, 70, 118-134. doi:10.1016/j.omega.2016.09.005Charnes, A., & Cooper, W. W. (1959). Chance-Constrained Programming. Management Science, 6(1), 73-79. doi:10.1287/mnsc.6.1.73Charnes, A., & Cooper, W. W. (1977). Goal programming and multiple objective optimizations. European Journal of Operational Research, 1(1), 39-54. doi:10.1016/s0377-2217(77)81007-2Constantinides, G. M., & Richard, S. F. (1978). Existence of Optimal Simple Policies for Discounted-Cost Inventory and Cash Management in Continuous Time. Operations Research, 26(4), 620-636. doi:10.1287/opre.26.4.620Moraes, M. B. da C., & Nagano, M. S. (2014). Evolutionary models in cash management policies with multiple assets. Economic Modelling, 39, 1-7. doi:10.1016/j.econmod.2014.02.010Da Costa Moraes, M. B., Nagano, M. S., & Sobreiro, V. A. (2015). Stochastic Cash Flow Management Models: A Literature Review Since the 1980s. Decision Engineering, 11-28. doi:10.1007/978-3-319-11949-6_2Eppen, G. D., & Fama, E. F. (1969). Cash Balance and Simple Dynamic Portfolio Problems with Proportional Costs. International Economic Review, 10(2), 119. doi:10.2307/2525547Gormley, F. M., & Meade, N. (2007). The utility of cash flow forecasts in the management of corporate cash balances. European Journal of Operational Research, 182(2), 923-935. doi:10.1016/j.ejor.2006.07.041Gregory, G. (1976). Cash flow models: A review. Omega, 4(6), 643-656. doi:10.1016/0305-0483(76)90092-xHerrera-Cáceres, C. A., & Ibeas, A. (2016). Model predictive control of cash balance in a cash concentration and disbursements system. Journal of the Franklin Institute, 353(18), 4885-4923. doi:10.1016/j.jfranklin.2016.09.007Higson, A., Yoshikatsu, S., & Tippett, M. (2009). Organization size and the optimal investment in cash. IMA Journal of Management Mathematics, 21(1), 27-38. doi:10.1093/imaman/dpp015Miller, M. H., & Orr, D. (1966). A Model of the Demand for Money by Firms. The Quarterly Journal of Economics, 80(3), 413. doi:10.2307/1880728Miller, T. W., & Stone, B. K. (1985). Daily Cash Forecasting and Seasonal Resolution: Alternative Models and Techniques for Using the Distribution Approach. The Journal of Financial and Quantitative Analysis, 20(3), 335. doi:10.2307/2331034Penttinen, M. J. (1991). Myopic and stationary solutions for stochastic cash balance problems. European Journal of Operational Research, 52(2), 155-166. doi:10.1016/0377-2217(91)90077-9Prékopa, A. (1995). Stochastic Programming. doi:10.1007/978-94-017-3087-7Salas-Molina, F. (2017). Risk-sensitive control of cash management systems. Operational Research, 20(2), 1159-1176. doi:10.1007/s12351-017-0371-0Salas-Molina, F., Martin, F. J., Rodríguez-Aguilar, J. A., Serrà, J., & Arcos, J. L. (2017). Empowering cash managers to achieve cost savings by improving predictive accuracy. International Journal of Forecasting, 33(2), 403-415. doi:10.1016/j.ijforecast.2016.11.002Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research, 267(1-2), 515-529. doi:10.1007/s10479-016-2359-1Salas-Molina, F., Pla-Santamaria, D., & Rodríguez-Aguilar, J. A. (2017). Empowering Cash Managers Through Compromise Programming. Financial Decision Aid Using Multiple Criteria, 149-173. doi:10.1007/978-3-319-68876-3_7Salas-Molina, F., Rodríguez-Aguilar, J. A., & Pla-Santamaria, D. (2018). Boundless multiobjective models for cash management. The Engineering Economist, 63(4), 363-381. doi:10.1080/0013791x.2018.1456596Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145-166. doi:10.1016/0305-0483(86)90017-4Stone, B. K. (1972). The Use of Forecasts and Smoothing in Control-Limit Models for Cash Management. Financial Management, 1(1), 72. doi:10.2307/3664955Stone, B. K., & Miller, T. W. (1987). Daily Cash Forecasting with Multiplicative Models of Cash Flow Patterns. Financial Management, 16(4), 45. doi:10.2307/366610

A Process Oriented MCDM Approach to Construct a Circular Economy Composite Index

[EN] The purpose of this contribution is to develop a Circular Economy Composite indicator to benchmark EU countries performance. Europe is at the forefront of the global transition towards a sustainable and circular economy. To this end, the European Commission has launched in 2015 a Circular Economy Action Plan including a monitoring framework to measure progress and to assess the effectiveness of initiatives towards the circular economy in the European Union (EU) and Member States. Still, this monitoring framework lacks a composite indicator at the national level to aggregate the circular economy dimensions into a single summary indicator. Although there is a wide range of sustainability composite indicators, no aggregate circular economy index exits to this date. We use a multi-criteria approach to construct a circular economy composite index based on TOPSIS (Technique for Order Preferences by Similarity to Ideal Solutions) methodology. In addition, we introduce a novel aggregation methodology for building a composite indicator where different levels of compensability for the distances to the ideal and anti-ideal (or negative-ideal) values of each indicator are considered. In order to illustrate the advantages of this proposal, we have applied it to evaluate the Circular Economy performance of EU Member States for the year 2016. This proposal can be a valuable tool for identifying areas in which the countries need to concentrate their efforts to boost their circular economy performance.Garcia-Bernabeu, A.; Hilario Caballero, A.; Pla Santamaría, D.; Salas-Molina, F. (2020). A Process Oriented MCDM Approach to Construct a Circular Economy Composite Index. Sustainability. 12(2):1-14. https://doi.org/10.3390/su12020618S114122Genovese, A., Acquaye, A. A., Figueroa, A., & Koh, S. C. L. (2017). Sustainable supply chain management and the transition towards a circular economy: Evidence and some applications. Omega, 66, 344-357. doi:10.1016/j.omega.2015.05.015Di Maio, F., & Rem, P. C. (2015). A Robust Indicator for Promoting Circular Economy through Recycling. Journal of Environmental Protection, 06(10), 1095-1104. doi:10.4236/jep.2015.610096Geng, Y., Sarkis, J., Ulgiati, S., & Zhang, P. (2013). Measuring China’s Circular Economy. Science, 339(6127), 1526-1527. doi:10.1126/science.1227059Geng, Y., Fu, J., Sarkis, J., & Xue, B. (2012). Towards a national circular economy indicator system in China: an evaluation and critical analysis. Journal of Cleaner Production, 23(1), 216-224. doi:10.1016/j.jclepro.2011.07.005Elia, V., Gnoni, M. G., & Tornese, F. (2017). Measuring circular economy strategies through index methods: A critical analysis. Journal of Cleaner Production, 142, 2741-2751. doi:10.1016/j.jclepro.2016.10.196Huijbregts, M. A. J., Rombouts, L. J. A., Hellweg, S., Frischknecht, R., Hendriks, A. J., van de Meent, D., … Struijs, J. (2006). Is Cumulative Fossil Energy Demand a Useful Indicator for the Environmental Performance of Products? Environmental Science & Technology, 40(3), 641-648. doi:10.1021/es051689gBrown, M. T., & Ulgiati, S. (2004). Energy quality, emergy, and transformity: H.T. Odum’s contributions to quantifying and understanding systems. Ecological Modelling, 178(1-2), 201-213. doi:10.1016/j.ecolmodel.2004.03.002Rees, W. E. (1992). Ecological footprints and appropriated carrying capacity: what urban economics leaves out. Environment and Urbanization, 4(2), 121-130. doi:10.1177/095624789200400212Wiedmann, T., & Barrett, J. (2010). A Review of the Ecological Footprint Indicator—Perceptions and Methods. Sustainability, 2(6), 1645-1693. doi:10.3390/su2061645Narodoslawsky, M., & Krotscheck, C. (1995). The sustainable process index (SPI): evaluating processes according to environmental compatibility. Journal of Hazardous Materials, 41(2-3), 383-397. doi:10.1016/0304-3894(94)00114-vMunda, G. (2005). «Measuring Sustainability»: A Multi-Criterion Framework. Environment, Development and Sustainability, 7(1), 117-134. doi:10.1007/s10668-003-4713-0Janeiro, L., & Patel, M. K. (2015). Choosing sustainable technologies. Implications of the underlying sustainability paradigm in the decision-making process. Journal of Cleaner Production, 105, 438-446. doi:10.1016/j.jclepro.2014.01.029Diaz-Balteiro, L., González-Pachón, J., & Romero, C. (2017). Measuring systems sustainability with multi-criteria methods: A critical review. European Journal of Operational Research, 258(2), 607-616. doi:10.1016/j.ejor.2016.08.075Wilson, M. C., & Wu, J. (2016). The problems of weak sustainability and associated indicators. International Journal of Sustainable Development & World Ecology, 24(1), 44-51. doi:10.1080/13504509.2015.1136360Arrow, K. J., Chenery, H. B., Minhas, B. S., & Solow, R. M. (1961). Capital-Labor Substitution and Economic Efficiency. The Review of Economics and Statistics, 43(3), 225. doi:10.2307/1927286Blackorby, C., Donaldson, D., & Weymark, J. A. (1982). A normative approach to industrial-performance evaluation and concentration indices. European Economic Review, 19(1), 89-121. doi:10.1016/0014-2921(82)90007-1Rennings, K., Ludwig Brockmann, K., & Bergmann, H. (1997). Voluntary agreements in environmental protection: experiences in Germany and future perspectives. Business Strategy and the Environment, 6(5), 245-263. doi:10.1002/(sici)1099-0836(199711)6:53.0.co;2-fMathews, J. A., & Tan, H. (2016). Circular economy: Lessons from China. Nature, 531(7595), 440-442. doi:10.1038/531440aCherchye, L., Moesen, W., Rogge, N., Van Puyenbroeck, T., Saisana, M., Saltelli, A., … Tarantola, S. (2008). Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index. Journal of the Operational Research Society, 59(2), 239-251. doi:10.1057/palgrave.jors.2602445Giannetti, B. F., Bonilla, S. H., Silva, C. C., & Almeida, C. M. V. B. (2009). The reliability of experts’ opinions in constructing a composite environmental index: The case of ESI 2005. Journal of Environmental Management, 90(8), 2448-2459. doi:10.1016/j.jenvman.2008.12.018Makkonen, T., & van der Have, R. P. (2012). Benchmarking regional innovative performance: composite measures and direct innovation counts. Scientometrics, 94(1), 247-262. doi:10.1007/s11192-012-0753-2Mazziotta, M., & Pareto, A. (2015). On a Generalized Non-compensatory Composite Index for Measuring Socio-economic Phenomena. Social Indicators Research, 127(3), 983-1003. doi:10.1007/s11205-015-0998-2Greco, M., Mazziotta, M., & Pareto, A. (2016). A Composite Index to Measure the Italian «Enological Vocation». Agriculture and Agricultural Science Procedia, 8, 691-697. doi:10.1016/j.aaspro.2016.02.045Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2018). On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Social Indicators Research, 141(1), 61-94. doi:10.1007/s11205-017-1832-9Attardi, R., Cerreta, M., Sannicandro, V., & Torre, C. M. (2018). Non-compensatory composite indicators for the evaluation of urban planning policy: The Land-Use Policy Efficiency Index (LUPEI). European Journal of Operational Research, 264(2), 491-507. doi:10.1016/j.ejor.2017.07.064Angilella, S., Catalfo, P., Corrente, S., Giarlotta, A., Greco, S., & Rizzo, M. (2018). Robust sustainable development assessment with composite indices aggregating interacting dimensions: The hierarchical-SMAA-Choquet integral approach. Knowledge-Based Systems, 158, 136-153. doi:10.1016/j.knosys.2018.05.041Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2019). Sigma-Mu efficiency analysis: A methodology for evaluating units through composite indicators. European Journal of Operational Research, 278(3), 942-960. doi:10.1016/j.ejor.2019.04.012Ruiz, F., El Gibari, S., Cabello, J. M., & Gómez, T. (2020). MRP-WSCI: Multiple reference point based weak and strong composite indicators. Omega, 95, 102060. doi:10.1016/j.omega.2019.04.003Sands, G. R., & Podmore, T. H. (2000). A generalized environmental sustainability index for agricultural systems. Agriculture, Ecosystems & Environment, 79(1), 29-41. doi:10.1016/s0167-8809(99)00147-4Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281. doi:10.1016/0022-2496(77)90033-5Singh, R. K., Murty, H. R., Gupta, S. K., & Dikshit, A. K. (2007). Development of composite sustainability performance index for steel industry. Ecological Indicators, 7(3), 565-588. doi:10.1016/j.ecolind.2006.06.004Ülengin, B., Ülengin, F., & Güvenç, Ü. (2001). A multidimensional approach to urban quality of life: The case of Istanbul. European Journal of Operational Research, 130(2), 361-374. doi:10.1016/s0377-2217(00)00047-3Buckland, S. T., Studeny, A. C., Magurran, A. E., Illian, J. B., & Newson, S. E. (2011). The geometric mean of relative abundance indices: a biodiversity measure with a difference. Ecosphere, 2(9), art100. doi:10.1890/es11-00186.1El Gibari, S., Gómez, T., & Ruiz, F. (2018). Building composite indicators using multicriteria methods: a review. Journal of Business Economics, 89(1), 1-24. doi:10.1007/s11573-018-0902-zGan, X., Fernandez, I. C., Guo, J., Wilson, M., Zhao, Y., Zhou, B., & Wu, J. (2017). When to use what: Methods for weighting and aggregating sustainability indicators. Ecological Indicators, 81, 491-502. doi:10.1016/j.ecolind.2017.05.068Li, H., Bao, W., Xiu, C., Zhang, Y., & Xu, H. (2010). Energy conservation and circular economy in China’s process industries. Energy, 35(11), 4273-4281. doi:10.1016/j.energy.2009.04.02

Monitoring multidimensional phenomena with a multicriteria composite performance interval approach

[EN] In the last two decades, the construction of composite indicators to measure and compare multidimensional phenomena in a broad spectrum of domains has increased considerably. Different methodological approaches are used to summarise huge datasets of information in a single figure. This paper proposes a new approach that consists in computing a multicriteria composite performance interval based on different aggregation rules. The suggested approach provides an additional layer of information as the performance interval displays a lower bound from a non-compensability perspective, and an upper bound allowing for full-compensability. The outstanding features of this proposal are: 1) a distance-based multicriteria technique is taken as the baseline to construct the multicriteria performance interval; 2) the aggregation of distances/separation measures is made using particular cases of Minkowski Lp metric; 3) the span of the multicriteria performance interval can be considered as a sign of the dimensions or indicators balance.Garcia-Bernabeu, A.; Hilario Caballero, A.; Pla Santamaría, D.; Salas-Molina, F. (2021). Monitoring multidimensional phenomena with a multicriteria composite performance interval approach. International Journal of Multicriteria Decision Making (Online). 8(4):368-385. https://doi.org/10.1504/IJMCDM.2021.120760S3683858

A Compact Representation of Preferences in Multiple Criteria Optimization Problems

[EN] A critical step in multiple criteria optimization is setting the preferences for all the criteria under consideration. Several methodologies have been proposed to compute the relative priority of criteria when preference relations can be expressed either by ordinal or by cardinal information. The analytic hierarchy process introduces relative priority levels and cardinal preferences. Lexicographical orders combine both ordinal and cardinal preferences and present the additional difficulty of establishing strict priority levels. To enhance the process of setting preferences, we propose a compact representation that subsumes the most common preference schemes in a single algebraic object. We use this representation to discuss the main properties of preferences within the context of multiple criteria optimization.Salas-Molina, F.; Pla Santamaría, D.; Garcia-Bernabeu, A.; Reig-Mullor, J. (2019). A Compact Representation of Preferences in Multiple Criteria Optimization Problems. Mathematics. 7(11):1-16. https://doi.org/10.3390/math7111092S116711Ahmadi, A., Ahmadi, M. R., & Nezhad, A. E. (2014). A Lexicographic Optimization and Augmented ϵ-constraint Technique for Short-term Environmental/Economic Combined Heat and Power Scheduling. Electric Power Components and Systems, 42(9), 945-958. doi:10.1080/15325008.2014.903542González-Arteaga, T., Alcantud, J. C. R., & de Andrés Calle, R. (2016). A new consensus ranking approach for correlated ordinal information based on Mahalanobis distance. Information Sciences, 372, 546-564. doi:10.1016/j.ins.2016.08.071Miettinen, K., & M�kel�, M. M. (2002). On scalarizing functions in multiobjective optimization. OR Spectrum, 24(2), 193-213. doi:10.1007/s00291-001-0092-9Ignizio, J. P. (1983). Generalized goal programming An overview. Computers & Operations Research, 10(4), 277-289. doi:10.1016/0305-0548(83)90003-5Sitorus, F., Cilliers, J. J., & Brito-Parada, P. R. (2019). Multi-criteria decision making for the choice problem in mining and mineral processing: Applications and trends. Expert Systems with Applications, 121, 393-417. doi:10.1016/j.eswa.2018.12.001Zyoud, S. H., & Fuchs-Hanusch, D. (2017). A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications, 78, 158-181. doi:10.1016/j.eswa.2017.02.016Erdoğan, M., & Kaya, İ. (2016). A combined fuzzy approach to determine the best region for a nuclear power plant in Turkey. Applied Soft Computing, 39, 84-93. doi:10.1016/j.asoc.2015.11.013Chen, Y., Liu, R., Barrett, D., Gao, L., Zhou, M., Renzullo, L., & Emelyanova, I. (2015). A spatial assessment framework for evaluating flood risk under extreme climates. Science of The Total Environment, 538, 512-523. doi:10.1016/j.scitotenv.2015.08.094Zammori, F. (2010). The analytic hierarchy and network processes: Applications to the US presidential election and to the market share of ski equipment in Italy. Applied Soft Computing, 10(4), 1001-1012. doi:10.1016/j.asoc.2009.07.013Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: moving toward new theory. International Journal of Physical Distribution & Logistics Management, 38(5), 360-387. doi:10.1108/09600030810882816Ignizio, J. P. (1976). An Approach to the Capital Budgeting Problem with Multiple Objectives. The Engineering Economist, 21(4), 259-272. doi:10.1080/00137917608902798Lonergan, S. C., & Cocklin, C. (1988). The use of lexicographic goal programming in economic/ecolocical conflict analysis. Socio-Economic Planning Sciences, 22(2), 83-92. doi:10.1016/0038-0121(88)90020-1González-Pachón, J., & Romero, C. (2014). Properties underlying a preference aggregator based on satisficing logic. International Transactions in Operational Research, 22(2), 205-215. doi:10.1111/itor.1211

Teaching operations management to lawyers

[EN] This paper deals with a problem present in most Master in Business Administration (MBA) classrooms. This problem is the heterogeneity of students and their backgrounds. Different backgrounds pose a challenge to teachers that require the use of quantitative techniques such as optimization within an Operations Management subject. Engineers and other students with mathematical training probably get bored if the level is too low. On the contrary, Lawyers and other students without technical background usually find contents cumbersome. This paper aims to find a compromise between the heterogeneity of backgrounds and the fulfilment of learning objectives for students of Operations Management in an MBA. To this end, we propose a methodology to support the selection of teaching methods from a multiobjective perspective. The results derived from this methodology enable professors to consider their particular preferences and to integrate important decision-making principles by selecting the appropriate distance function to an ideal point that acts as a reference.Salas-Molina, F.; Vercher-Ferrandiz, M.; Pla Santamaría, D.; Garcia-Bernabeu, A. (2022). Teaching operations management to lawyers. IATED. 9293-9299. https://doi.org/10.21125/edulearn.2022.22399293929

The pivotal functionality of the amyloid protein TasA in bacillus physiology and fitness

Biofilms are complex bacterial communities formed on any virtual surface and composed of cells embedded in an extracellular matrix. Studies on Bacillus subtilis have demonstrated this tissue-like structure comprised of diverse exopolymeric substances (eps) including exopolysaccharides, the protein BslA, and TasA and TapA the two main components of the amyloid fibers that confer robustness to the architecture of the biofilm. It has been demonstrated that the genetic pathways involved in formation of biofilms are active in the interaction of B. subtilis with plant surfaces. Indeed, we previously showed that surfactin acts as a self-trigger of biofilm in the plant phylloplane, which connected with the suppressive activity of B. subtilis against phytopathogenic fungi. These findings led us to hypothesize a major contribution of the extracellular matrix in the ecology of B. subtilis in the poorly explore plant phylloplane. In this work, we show that the amyloid protein TasA has a meaningful role in adhesion and biofilm formation over the plant phylloplane, however, despite the inability of the tasA mutant to form a biofilm, it still retained a similar antagonistic activity compared to the wild type strain. An in-depth transcriptomic analysis of the mutant led us to find unexpected variations in the expression levels of over 300 genes, including the overexpression of: i) production of acetoin ii) secondary metabolites and non-ribosomal peptides iii) eps and other biofilm-related components and iv) general stress, among others. These findings suggested that besides the structural role, TasA might have a regulatory function on the physiological stage of the cells. Indeed, an allele of TasA unable to restore biofilm formation allowed us to separate both functions, supporting the importance of this functional amyloid in regulating bacterial physiology and fitness.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Sufficient consumption as a missing link toward sustainability: The case of fast fashion

The fashion industry has been driven by limitless consumption-led growth spearheaded by companies in the fast fashion segment, with a dominant business model based on massive accelerated demand, production, consumption, and disposal. Despite companies’ efforts to decouple the pursuit of growth from its negative impacts, a more sufficiency-driven approach seems imperative to curb consumerism and contribute more effectively to sustainability. This study draws on the literature to build a three-pillar framework of potential strategies to enable fashion companies to foster sufficient consumption and reduce dependence on the sale of new items, with benefits expected for both consumers and companies. Subsequently, it uses multiple case study to examine qualitatively the annual reports issued during 2013–2014 and 2020–2021 by a sample of ten top companies in this segment. The goal is to assess whether these companies are embracing such strategies, what (if any) evolution occurs between these two periods, whether the 2030 Agenda with its SDG12 ‘Responsible consumption and production’ plays a mediating role in their adoption, and what is the logic behind such evolution. The results show that, although such adoption is gaining momentum, companies tend first to embrace strategies with less impact on their traditional modus operandi. Further, the laxity of SDG12 enables companies to profess commitment even when not addressing any of the strategies to foster sufficient consumption. This study aims to give actors critical awareness of this issue and provide practical guidance for managers to adopt and combine these strategies decisively to fully embrace the principles of circular economy and a more holistic approach to sustainability. It also advises companies to avoid the risk of ‘anti-consumerist washing’—a newly identified variant of greenwashing—and proposes to study a ‘hierarchical pyramid of business strategies to rationalize consumptionMinistry of Science and Innovation of the Government of Spain PDI2021.124396NB.I00European Regional Development Fund (European Union)CRUE-Universitat Politecnica de Valenci