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The emergence and spread of COVID-19 has severely impacted the tourism industry worldwide. In order to limit the effect of new pandemics or any unforeseen crisis, coordinated actions need to be adopted among tourism stakeholders. In this paper, we use an evolutionary game model to analyze the conditions that promote cooperation among different stakeholders in a tourism network to control high-risk crises. A data sample of 280 EU regions is used to define the tourism network of regions with a heterogeneous dependence on tourism. The results show that cooperation is helped by the existence of a structured tourism network. Moreover, cooperation is enhanced when coordination groups include small numbers of participants and when they are formed according to the similarity of tourism dependence., University of Las Palmas de Gran Canaria COVID-19-04, Spanish Government, Andalusian Government, European Commission P18-TP-4475 PID2021-122916NB-I00 RYC-2016-19800

The location of East African ports, along with difficulties in building and maintaining effective road corridors, has led to the consideration of intermodal transport through Short Sea Shipping (SS...

The joint optimization of production scheduling and maintenance planning has a significant influence on production continuity and machine reliability. However, limited research considers preventive maintenance (PM) and corrective maintenance (CM) in assembly permutation flow shop scheduling. This paper addresses the bi-objective joint optimization of both PM and CM costs in assembly permutation flow shop scheduling. We also propose a new mixed integer linear programming model for the minimization of the makespan and maintenance costs. Two lemmas are inferred to relax the expected number of failures and CM cost to make the model linear. A restarted iterated Pareto greedy (RIPG) algorithm is applied to solve the problem by including a new evaluation of the solutions, based on a PM strategy. The RIPG algorithm makes use of novel bi-objective-oriented greedy and referenced local search phases to find non-dominated solutions. Three types of experiments are conducted to evaluate the proposed MILP model and the performance of the RIPG algorithm. In the first experiment, the MILP model is solved with an epsilon-constraint method, showing the effectiveness of the MILP model in small-scale instances. In the remaining two experiments, the RIPG algorithm shows its superiority for all the instances with respect to four well-known multi-objective metaheuristics.

M.C. was supported by the Spanish Ministry of Science, Andalusian Government, University of Granada, and ERDF under grants SIMARK (P18-TP-4475) , RYC-2016-19800, and PPJIA2020-09 (TURCOMPLEX) . J.H. was supported by the University of Las Palmas de Gran Canaria, under grant COVID-19 04. M.P. was supported by the Slovenian Research Agency (Grant Nos. P1-0403 and J1-2457) . Funding for open access charge: Universidad de Granada/CBUA., Many countries worldwide rely on tourism for their economic well-being and development. But with issues such as over-tourism and environmental degradation looming large, there is a pressing need to determine a way forward in a sustainable and mutually rewarding manner. With this motivation, we here propose an asymmetric evolutionary game with mobility where local stakeholders and tourists can either cooperate or defect in a spatially structured setting. Our study reflects that sustainable tourism is primarily determined by an optimal trade-off between economic benefits of the stakeholders and their costs related to the application of sustainability policies. In contrast, the specific benefits and costs of the tourists are comparatively less relevant. The reader can also observe that allowing for greater tourist mobility decreases cooperation and leads to faster polarization among local stakeholders. In agreement with observations worldwide, we identify decreasing population densities in tourist areas in terms of both, stakeholders and tourists, to be a key aid to greater cooperation and overall sustainability of tourism. These results are rooted in spatial formations and complex alliances that manifest spontaneously through the evolutionary dynamics in a structured population., Spanish Ministry of Science, Andalusian Government, University of Granada, European Commission P18-TP-4475 RYC-2016-19800 PPJIA2020-09, University of Las Palmas de Gran Canaria COVID-19 04, Slovenian Research Agency - Slovenia P1-0403 J1-2457, Universidad de Granada/CBUA

This paper introduces a decision support tool for sustainable intermodal chains with seaborne transport, in which the optimization of a multi-objective model enables conflicting objectives to be handled simultaneously. Through the assessment of &lsquo, door-to-door&rsquo, transport in terms of costs, time, and environmental impact, the most suitable maritime route and the optimized fleet are jointly calculated to maximize the opportunities for success of intermodal chains versus trucking. The resolution of the model through NSGA-II algorithms permits to obtain Pareto fronts that offer groups of optimized solutions. This is not only useful to make decisions in the short term, but also to establish long-term strategies through assessment of the frontiers&rsquo, behavior obtained when a sensitivity analysis is undertaken. Thus, consequences of transport policies on intermodal performance can be analyzed. A real-life case is studied to test the usefulness of the model. From the application case, not only the most suitable Motorway of the Seas with their optimized fleets are identified for Chile, but also significant general findings are provided for both policy makers and heads of ports to promote the intermodal option regardless of their geographical locations.

Calibration is a crucial task on building valid models before exploiting their results. This process consists of adjusting the model parameters in order to obtain the desired outputs. Automatic calibration can be performed by using an optimization algorithm and a fitness function, which involves a deviation measure to compare the time series coming from the model. In this paper, we apply a memetic IPOP-CMA-ES for the calibration of an agent-based model and we study the effect of different deviation measures in this calibration problem. Classical metrics calculate the mean point-to-point error, but we also propose using an extension of dynamic time warping, which considers trend series evolution. In order to determine if calibrating with an specific metric leads to better solutions, we carry out an exhaustive experimentation by including statistical tests, analysis on the values of the calibrated parameters, and qualitative results. Our results show IPOP-CMA-ES obtains better performance than a genetic algorithm. In addition, MAE, MAPE and Soft-DTW are the metrics which report best results, although we get a similar behavior for all of them.

“Bee pollen” is pollen collected from flowers by honey bees. It is used by the bees to nourish themselves, mainly by providing royal jelly and brood food, but it is also used for human nutrition. For the latter purpose, it is collected at the hive entrance as pellets that the bees bring to the hive. Bee pollen has diverse bioactivities, and thus has been used as a health food, and even as medication in some countries. In this paper, we provide standard methods for carrying out research on bee pollen. First, we introduce a method for the production and storage of bee pollen which assures quality of the product. Routine methods are then provided for the identification of the pollen’s floral sources, and determination of the more important quality criteria such as water content and content of proteins, carbohydrates, fatty acids, vitamins, alkaloids, phenolic and polyphenolic compounds. Finally, methods are described for the determination of some important bioactivities of bee pollen such as its antioxidant, anti-inflammatory, antimicrobial and antimutagenic properties. Métodos estándar Para la investigación del polen El "polen de abeja" es el polen recogido de las flores por las abejas melíferas. El polen de abeja es utilizado para nutrir a las propias abejas, principalmente para proporcionar jalea real y alimento para las crías, pero también se utiliza para la nutrición humana. Para este último fin, se recoge en la entrada de la colmena en forma de gránulos que las abejas llevan a la colmena. El polen de abeja tiene diversas bioactividades, por lo que se hautilizado como alimento para la salud, e incluso como medicamento en algunos países. En este artículo, proporcionamos métodos estándar para llevar a cabo investigaciones sobre el polen de abeja. En primer lugar, presentamos un método de producción y almacenamiento de polen de abeja que garantiza la calidad del producto. A continuación, se ofrecen métodos de rutina para la identificación de las fuentes florales del polen y la determinación de los criterios de calidad más importantes, como el contenido de agua y de proteínas, carbohidratos, ácidos grasos, vitaminas, alcaloides y compuestos fenólicos y polifenólicos. Por último, se describen métodos para la determinación de algunas bioactividades importantes del polen de abeja, como sus propiedades antioxidantes, antiinflamatorias, antimicrobianas y antimutagénicas. The COLOSS (Prevention of honey bee COlony LOSSes) Association aims to explain and prevent massive honey bee colony losses. It was funded through the COST Action FA0803. COST (European Cooperation in Science and Technology) is a unique means for European researchers to jointly develop their own ideas and new initiatives across all scientific disciplines through trans-European networking of nationally funded research activities. Based on a pan-European intergovernmental framework for cooperation in science and technology, COST has contributed since its creation more than 40 years ago to closing the gap between science, policy makers and society throughout Europe and beyond. COST is supported by the EU Seventh Framework Program for research, technological development and demonstration activities (Official Journal L 412, 30 December 2006). The European Science Foundation as implementing agent of COST provides the COST Office through an EC Grant Agreement. The Council of the European Union provides the COST Secretariat. The COLOSS network is now supported by the Ricola Foundation – Nature & Culture. Figures 26–28 are reproduced from Sawyer (1981) with the permission of the publishers University College Cardiff Press and Northern Bee Books. Lidia Barreto and J Nordi wish to thank the Apiculture Research Center of Taubate University (UNITAU-SP/Brazil) and Agriculture Secretary of Bahia State (SEAGRI-BA/ BRAZIL). Maria Campos wishes to thank (UI0204): UIDB/ 00313/2020, Center of Chemistry from Faculty of Sciences and Technology of University of Coimbra, Portugal. Of elia Anjos wishes to thank to Forest Research Centre, a research unit funded by Fundac¸~ao para a Ci^encia e a Tecnologia I.P. (FCT), Portugal (UIDB/00239/2020), and to Centro de Biotecnologia de Plantas da Beira Interior for the OPUS software availability. Norma Almaraz Abarca thanks to the Instituto Politecnico Nacional for financial and logistic support. Manuel Chica and Pascual Campoy wish to thank the APIFRESH project. APIFRESH has been co-funded by the European Commission under the R4SMEs 7th Framework Program. Olena Lokutova thanks the Austrian Institute of beekeeping, Dr H Pehhacker and the same members of the Institute H Hagel and E H€uttinger for conducting photomicroscopic studies and pollen analysis Ukrainian samples of pollen loads, which were the basis of the atlas of pollen “honey plants” of Ukraine. Olena is grateful also to Polish colleagues Z Warakomska (Department of Botany University of Lublin) and D Teper (Polish Institute of beekeeping) for their professionalism and consultations to determine the botanical origin of some Ukrainian honey. Also thanks to their scientific advisers’ academician G Bogdanov (National Academy of Agrarian Sciences of Ukraine), Prof. V Polishuk (Department of beekeeping National University of Life and Environmental Sciences of Ukraine) and O Martynyuk (M.G. Kholodny Institute of Botany, Kiev, Ukraine) for his helpful co-operation in the field of beekeeping and palinology. Janka Nozkova wishes to thank the Operational Program Research and Development of the European Regional Development Fund in the frame of the project “Support of technologies innovation for special bio-food products for human healthy nutrition” (ITMS 26220220115) and also by the Excellence Center for Agrobiodiversity Conservation and Benefit – project implemented under the Operational Program Research and Development financed by European Fund for Regional Development ITMS 26220120015 (Slovak Republic) and all colleagues from Institute of Biodiversity and Biosafety, Slovak University of Agriculture in Nitra for their help with image analysis. Ananias Pascoal, Georgina Tolentino and Let ıcia Estevinho would like to thank Fundac¸~ao para a Ci^encia e Tecnologia (FCT), Programa Operacional Pontencial Humano (POPH) and European Union (EU) for his Postdoctoral grant SFRH/BPD/91380/2012. Wiebke Sickel, Markus Ankenbrand, Gudrun Grimmer, Frank F€orster, Ingolf Steffan-Dewenter and Alexander Keller thank the financial support by the DFG Collaborative Research Center 1047, Insect Timing. MJA was further supported by a grant of the German Excellence Initiative to the Graduate School of Life Sciences of the University of W€urzburg. They are grateful to the members of the Departments of Animal Ecology and Tropical Biology; Bioinformatics; and Human Genetics, University of W€urzburg, for constructive input on the design of the workflow. Additionally thank to the Department of Human Genetics, especially S. Rost, for granting access to the Illumina MiSeq device. Zivoslav Te si c, Mirjana Mosi c, Aleksandar Kosti c, Mirjana Pe si c, Du sanka Milojkovi c-Opsenica thank the Ministry of Education, Science and Technological Development of Serbia, Grants 172017 and TR 31069. Gina Tolentino would like to thank the Mountain Research Center (CIMO), Agricultural College of Bragança, Polytechnic Institute of Braganc¸a for his research grant in the project titled " Development of new bee products in biological production way.” info:eu-repo/semantics/publishedVersion

Agent-based modeling (ABM) is a simulation paradigm to model complex systems by defining heterogeneous individual-level behaviors in a bottom-up approach. ABM is typically employed to simulate markets to study consumer decisions and to see how consumers make their purchase decisions. In this work, we present a marketing ABM where consumer perceptions are modeled using 2-tuple fuzzy linguistic variables. These variables represent the opinions the consumers have on the different features of every product, which drive their decisions (e.g., price or quality). In contrast to numerical or crisp values, fuzzy linguistic variables are a realistic representation of these qualitative aspects. In our ABM, agents use a decision-making heuristic to select a product, which is based on those perceptions and a probabilistic utility maximization rule. This process requires a fuzzy aggregation of the perceptions of every product, based on an ordered weighted average (OWA). In addition, consumers can be aware or unaware of each product in the market. In our ABM, we model this information by introducing a brand awareness filter when applying the decision-making heuristic. Thus, consumer agents can only select those products they are aware of. Our experimental results show that our realistic representation of the consumer preferences is more accurate than other existing approaches.

Trust and trustworthiness are of great importance in social and human systems, especially when considering managerial and economic decision-making. In this paper, we investigate the emergent dynamics of an evolutionary game-theoretic model—the ${N}$ -player evolutionary trust game—consisting of three types of players: 1) an investor; 2) a trustee who is trustworthy; and 3) a trustee who is untrustworthy. Here, we limit the interactions between players to local neighborhoods defined by a specific spatial topology or social network. Players are able to adjust their game-playing strategies using an evolutionary update rule based on the payoffs obtained by their neighbors. Through comprehensive simulation experiments, we find that trust can be promoted when players interact on a social network despite a substantial number of untrustworthy individuals in the initial population. These results differ from findings reported for an unstructured population of the same game, where the existence of a single untrustworthy individual would eliminate trust completely. We compare the dynamics of the model with different social network densities and structures (e.g., from regular lattices to scale-free and random networks). We observe that the levels of trust vary under different network structures, and the level is correlated with how “difficult” the game is. When game conditions are easy (i.e., low temptation to defect and/or almost no initial untrustworthy trustees), homogeneous networks with higher densities can promote higher levels of trust. However, when the game becomes harder, heterogeneous social networks with lower densities are able to promote higher levels of trust and global net wealth.

The unbalanced evolution of the environmental normative in the European Union for the different transport modes has led to a broader debate about whether Short Sea Shipping is still a green transport mode. This discussion is especially pertinent because there is no technological alternative indisputably identified as the most adequate one to meet the emission requirements in the emission control area without penalizing the competitiveness of Short Sea Shipping. The objective of this article is to assess the performance of intermodal chains versus trucking in terms of costs, times, and externalities when the selected fleet for Short Sea Shipping is made up of optimal container vessels operating with different propulsion plants and fuels in compliance with emission control area requirements. This is, Tier III- four-stroke diesel engine with marine gas oil, a Tier III- four-stroke diesel engine Tier III with scrubber and heavy fuel oil, and a four-stroke dual engine operating with liquefied natural gas. To this aim, a mathematical model, which is able to provide optimized technical and operative features of the vessels, is modified and solved for an intermodal chain between Spain and France through the Atlantic coast. This study shows that dual liquefied natural gas engines prove to be not only the most sustainable solution but also the most suitable in terms of costs, as long as the difference in price between liquefied natural gas and petroleum fuels is equal to the base case or within a modification range of 20%. This study also highlights that due to the limited range of Short Sea Shipping vessels, the loss of the cargo capacity in holds by the gas tanks arrangement was not significant.

The recent European regulations on emissions from heavy duty vehicles (Euro VI) along with the enforcement of ECA regulations have represented an additional challenge for the sustainability of the motorways of the sea. The main aim of this paper is to identify the optimal sizing and the most adequate propulsion plant for a fleet of feeder vessels that, by operating under motorways of the sea conditions, is able to articulate competitive intermodal chains versus the road for the door-to-door transport by ensuring the sustainability of the intermodality in the current normative framework. Thus, a mathematical model is developed to evaluate, aside from the total costs and the time invested in the transport, the environmental costs of the unimodal transport and of intermodal chains with different sizing and technologies for the vessels. The resolution of this multiobjective model was carried out with an NSGA-II algorithm in an application to a transport network between Spain and France. This application concluded that fast and small vessels with LNG propulsion plants are the most convenient to maximize the competitiveness advantage against the road alternative. Likewise, the analysis of the environmental performance of both transport systems in the application case from 2010 to 2015 shows an unfavourable environmental evolution for the intermodality.

An appropriate visualization of multiobjective nondominated solutions is a valuable asset for decision making. Although there are methods for visualizing the solutions in the design space, they do not provide any information about their relationship. In this paper, we propose a novel methodology that allows the visualization of the nondominated solutions in the design space and their relationships by means of a network. The nodes represent the solutions in the objective space while the edges show the relationships among the solutions in the design space. Our proposal (called moGrams) thus provides a joint visualization of both objective and design spaces. It aims at helping the decision maker to get more understanding of the problem so that (s)he can choose the most appropriate and flexible final solution. moGrams can be applied to any multicriteria problem in which the solutions are related by a similarity metric. Besides, the decision maker interaction is facilitated by modifying the network based on the current preferences to obtain a clearer view. An exhaustive experimental study is performed using four multiobjective problems with a variable number of objectives to show both usefulness and versatility of moGrams. The results exhibit interesting characteristics of our methodology for visualizing and analyzing solutions of multiobjective problems.

Marketers must constantly decide how to implement word-of-mouth (WOM) programs, and a well-developed decision support system (DSS) can provide them valuable assistance in doing so. The authors propose an agent-based framework that aggregates social network–level individual interactions to guide the construction of a successful DSS for WOM. The framework presents a set of guidelines and recommendations to (1) involve stakeholders, (2) follow a data-driven iterative modeling approach, (3) increase validity through automated calibration, and (4) understand the DSS behavior. This framework is applied to build a DSS for a freemium app in which premium users discuss the product with their social network and promote its viral adoption. After its validation, the agent-based DSS forecasts the aggregate number of premium sales over time and the most likely users to become premium in the near future. The experiments show how the DSS can help managers by forecasting premium conversions and increasing the number of premiums through targeting and implementing reward policies.

From smart cities to factories and business, many decision-making processes in our society involve NP-hard optimization problems. In a real environment, these problems are frequently large-scale, which limits the potential of exact optimization methods and justifies the use of metaheuristic algorithms in their resolution. Real-world problems are also distinguished by high levels of dynamism and uncertainty, which affect the formulation of the optimization model, its input data, and constraints. However, metaheuristic algorithms usually assume deterministic inputs and constraints, and thus end up solving oversimplified models of the real system being considered, casting doubt on validity and even meaning of the results and recommendations. Accordingly, this paper argues that approaches combining simulation with metaheuristics, i.e., simheuristics, not only constitute a natural extension of metaheuristics, but also should be considered as a “first resort” method when dealing with large-scale stochastic optimization problems, which constitute most realistic problems in industry and business. To this end, this paper highlights the main benefits and limitations of these simheuristic algorithms, reviews some examples of applications to different fields, and analyzes the most suitable simulation paradigms to be used within a simheuristic. Finally, we outline a series of best practices to consider during the design and implementation stages of a simheuristic algorithm.

Dissertação de Mestrado em Engenharia Zootécnica/Produção Animal A inclusão de suplementos lipídicos, geralmente compostos por triacilgliceróis, na dieta de ruminantes permite aumentar o seu desempenho produtivo e alterar a composição lipídica dos seus produtos. Sabe-se que os triacilgliceróis ingeridos são extensivamente hidrolisados no rúmen (i.e., lipólise), mas pouco se conhece sobre o comportamento cinético da lipólise de diferentes tipos de triacilgliceróis. O objetivo geral deste trabalho consistiu na avaliação da lipólise de diferentes triacilgliceróis (TGL-16:0/16:0/16:0, TGL- 18:0/18:0/18:0, TGL-18:1/18:1/18:1, TGL-18:2/18:2/18:2 e TGL-18:3/18:3/18:3) através de estudos in vitro com inóculo ruminal durante 0, 0,5, 2, 4 e 6 horas de incubação. Especificamente pretendeu-se avaliar na lipólise o efeito do comprimento da cadeia e grau de insaturação dos AG no triacilglicerol. Adicionalmente analisaram-se os produtos provenientes da bioidrogenação e síntese microbiana. Através dos resultados obtidos constatou-se que o desaparecimento dos triacilgliceróis testados aumentou significativamente (P

We investigate the effects of update rules on the dynamics of an evolutionary game-theoretic model – the N-player evolutionary trust game – consisting of three types of players: investors (trusters), trustworthy trustees, and untrustworthy trustees. Interactions among players are constrained by local neighborhoods predefined by spatial or social network topologies. We compare different evolutionary update rules with behaviors that rely on the level of payoffs obtained by neighbors. In particular, we study the dynamics resulting from players using a deterministic rule (i.e., unconditional imitation with and without using a noise process induced by a voter model), a stochastic pairwise payoff-based strategy (i.e., proportional imitation), and stochastic local Wright-Fisher processes. We study these dynamics with different social network structures and varying levels of game difficulty. We see that there are significant differences on the level of promoted trust and global net wealth depending on the update rule. Under ‘harder’ game settings, rules based on unconditional imitation achieve the highest global net wealth in the population. We observe that there are key spatio-temporal correlations in the system for all rules. The update rules lead to the formation of fractal structures on a lattice, and low frequencies in the output signal of the system (i.e., long-term memory) when the rules are stochastic., This work is jointly supported by the Spanish Ministry of Science, Innovation and Universities, the National Agency for Research Funding AEI, and ERDF (EU) under grants EXASOCO (PGC2018-101216-B-I00) and PACSS (RTI2018-093732-B-C22), and incentive funds from the School of Electrical Engineering and Computing, The University of Newcastle, Australia. M. Chica is supported through the Ramón y Cajal program (RYC-2016-19800). J. J. Ramasco acknowledges partial funding from the Maria de Maeztu program of the AEI for Units of Excellence in R&D (MDM-2017-0711).

Artificial intelligence (AI) has proven to be useful in many applications from automating cars to providing customer service responses. However, though many firms want to take advantage of AI to improve marketing, they lack a process by which to execute a Marketing AI project. This article discusses the use of AI to provide support for marketing decisions. Based on the established Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, it creates a process for managers to use when executing a Marketing AI project and discusses issues that might arise. It explores how this framework was used to develop three cutting-edge Marketing AI applications.

Migration is one of the many responses humans and societies make to ongoing demographic, economic, societal and environmental changes. In this work, we use agent-based modeling (ABM) to study the dynamics of migration flows across provinces and cities in the Mekong Delta, Vietnam. The strength of ABM is that it allows a bottom-up approach that focuses on how individuals make decisions in a complex system comprising various factors. Outputs of our agent-based model are automatically calibrated with actual data using a genetic algorithm. This automated calibration yields some significant improvement in the results, with all observed net- and out-migration data captured within the 95% confidence interval. Sensitivity analysis carried out helps to further understand the impact of critical factors on the final migration decision.

Through Directive 2014/94/EU, European Union (EU) has required European ports to provide facilities to enable Cold Ironing (CI) use by 2025. This new reality advances a stricter normative in terms of port emissions. This paper introduces a calculation method to estimate a specific environmental charge in ports to incentivize CI use in Short Sea Shipping. The impact of this charge on the economic performance of the vessel’s operators is assessed through Internal Rate of Return of the CI retrofitting investment in vessels. The calculation method assumes a pollutant differentiation system by considering kinds of vessel, technical features, port localization and hinterlands populations. Results show that, only when the generation of on-shore electricity is dominated by sustainable sources, the environmental charge is effective to stimulate the CI retrofitting of vessels in adverse scenarios. Sensitivity analyses determine that low gross tonnages’ vessels with longer berthing times take greater advantage from CI use.

The Mekong Delta (MKD) region in Vietnam has experienced large-scale inter-provincial migration flows. This study employs agent-based modeling to explore migration dynamics across the provinces and cities in the MKD. An agent-based model, which incorporates the Theory of Planned Behaviour into the decision-making process to effectively break down migration intention into related components, is developed to simulate how an individual makes migration decisions considering different economic, social, and environmental circumstances. Outputs of the agent-based model are calibrated via real province-level data of in-, out-, and net-migration flows in the MKD region with the use of a data analytics approach, in an attempt to validate the results produced.

“This is a pre-print of an article published inJ. Flex Serv Manuf. The final authenticated version is available online at: https://doi.org/10.1007/s10696-018-9309-y ” Chica, M., Bautista, J. & de Armas, J. Flex Serv Manuf J (2018). https://doi.org/10.1007/s10696-018-9309-y Changing conditions and variations in the demand are frequent in real industrial environments. Decision makers have to take into account this uncertainty and manage it properly. One clear example is the automotive industry where manufacturers have to assume an uncertain and heterogeneous demand. For instance, automotive manufacturers must adapt their decisions when balancing the assembly line by considering different flexible solutions. Our proposal is using robust multiobjective optimization and simulation techniques to provide managers with a set of robust and equally-preferred solutions for assembly line balancing. We study a Nissan case where the demand of each product family is uncertain. The problem is addressed by considering a robust multiobjective model for assembly line balancing based on a high number of production plans. After the selection of six different assembly line configurations, we study the implications of robustness metrics based on workstations’ overload. We show that the adverse managerial effects of not having flexible line configuration when demand changes are alleviated. For the real Nissan automotive case, our analysis and conclusions show the managerial and industrial benefits of using robust assembly lines. We also encourage decision makers to use robust multiobjective optimization methods for selecting the most flexible decisions.

The reliability estimation of engineered components is fundamental for many optimization policies in a production process. The main goal of this paper is to study how machine learning models can fit this reliability estimation function in comparison with traditional approaches (e.g., Weibull distribution). We use a supervised machine learning approach to predict this reliability in 19 industrial components obtained from real industries. Particularly, four diverse machine learning approaches are implemented: artificial neural networks, support vector machines, random forest, and soft computing methods. We evaluate if there is one approach that outperforms the others when predicting the reliability of all the components, analyze if machine learning models improve their performance in the presence of censored data, and finally, understand the performance impact when the number of available inputs changes. Our experimental results show the high ability of machine learning to predict the component reliability and particularly, random forest, which generally obtains high accuracy and the best results for all the cases. Experimentation confirms that all the models improve their performance when considering censored data. Finally, we show how machine learning models obtain better prediction results with respect to traditional methods when increasing the size of the time-to-failure datasets.

We present a contract farming model in the context of rice supply chain in the Mekong Delta, Vietnam, with the use of agent-based simulation. The purpose of the simulation is to understand the motivation of farmers and contractors in their participation into the contract rice farming scheme. The decision-making process is based on two main factors: cost-benefit analysis and the role of trust. The context is also extended with the introduction of a spot market in which both parties can renege on the contractual relationship. The simulation model then evaluates the performance of contract farming under different commitment decisions of farmers and contractors.

Image registration (IR) is an extended and important problem in computer vision. It involves the transformation of different sets of image data having a shared content into a common coordinate system. Specifically, we will deal with the 3D intensity-based medical IR problem where the intensity distribution of the images is considered, one of the most complex and time consuming variants. The limitations of traditional IR methods have boomed the application of evolutionary and metaheuristic-based approaches to solve the problem, aiming to improve the performance of existing methods both in terms of accuracy and efficiency. In this contribution, we consider the use of a recently proposed bio-inspired meta-heuristic: the Coral Reef Optimization Algorithm (CRO). This novel algorithm simulates the natural phenomena underlying a coral reef, where different corals grow, reproduce and fight with other corals for space in the colony. CRO has recently obtained promising results in different real-world applications and we think its operation mode can properly cope with the 3D intensity-based medical IR problem. We adapt the algorithm to the real-coding problem nature and run an experimental setup tackling sixteen real-world problem instances. The new proposal is benchmarked with recent, state-of-the-art IR techniques. The results show that the CRO-based overcomes the state-of-the-art results in terms of its robustness and time efficiency.

In this paper, we present an evolutionary trust game to investigate the formation of trust in the so-called sharing economy from a population perspective. To the best of our knowledge, this is the first attempt to model trust in the sharing economy using the evolutionary game theory framework. Our sharing economy trust model consists of four types of players: a trustworthy provider, an untrustworthy provider, a trustworthy consumer, and an untrustworthy consumer. Through systematic simulation experiments, five different scenarios with varying proportions and types of providers and consumers were considered. Our results show that each type of players influences the existence and survival of other types of players, and untrustworthy players do not necessarily dominate the population even when the temptation to defect (i.e., to be untrustworthy) is high. Our findings may have important implications for understanding the emergence of trust in the context of sharing economy transactions.

The objective of the ForFire project is the development of an outdoor fire detection system by using an innovative solar blind camera based on the technology of photosensitive gas and solid-state detectors. The development of this new sensor together with an appropriate algorithm for pattern recognition aims to provide a high capability and a high reliability flame-detection system with cost effectiveness, early detection and accurate localization of fire hazards. This is achieved by focusing specifically on the detection of the VUV part ( 180 nm ≤ λ ≤ 260 nm ) of the electromagnetic spectrum emitted by the fire source. The advantage of this approach is that on Earth only fire flames emit in this spectral range thus avoiding potential interferences with other wavelength sources where the Sun is a dominant background.