Dr Manuel Chica Serrano

In practical assembly enterprises, customization and rush orders lead to an uncertain demand environment. This situation requires managers and researchers to configure an assembly line that increases production efficiency and robustness. Hence, this work addresses cost-oriented mixed-model multimanned assembly line balancing under uncertain demand, and presents a new robust mixed-integer linear programming model to minimize the production and penalty costs simultaneously. In addition, a reinforcement learning-based multiobjective evolutionary algorithm (MOEA) is designed to tackle the problem. The algorithm includes a priority-based solution representation and a new task-worker-sequence decoding that considers robustness processing and idle time reductions. Five crossover and three mutation operators are proposed. The <inline-formula> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula>-learning-based strategy determines the crossover and mutation operator at each iteration to effectively obtain Pareto sets of solutions. Finally, a time-based probability-adaptive strategy is designed to effectively coordinate the crossover and mutation operators. The experimental study, based on 269 benchmark instances, demonstrates that the proposal outperforms 11 competitive MOEAs and a previous single-objective approach to the problem. The managerial insights from the results as well as the limitations of the algorithm are also highlighted.

Tourism is a growing sector worldwide, but many popular destinations are facing sustainability problems due to excessive tourist flows and inappropriate behavior. In these areas, there is an urgent need to apply mechanisms to stimulate sustainable practices. This paper studies the most efficient strategy to incentivize sustainable tourism by using an asymmetric evolutionary game. We analyze the application of rewarding policies to the asymmetric game where tourists and stakeholders interact in a spatial lattice, and where tourists can also migrate. The incentives of the rewarding policies have an economic budget which can be allocated to tourists, to stakeholders, or to both sub-populations. The results show that an adaptive rewarding strategy, where the incentive budget changes over time to one or the other sub-population, is more effective than simple rewarding strategies that are exclusively focused on one sub-population. However, when the population density in the game decreases, rewarding just tourists becomes the most effective strategy.

Agent-based models establish a suitable simulation technique to recreate real complex systems, such as those approached in marketing. Reinforcement learning is about learning a behavior policy in order to maximize a long-term reward signal. In this work, we develop a deep reinforcement learning agent that represents a brand in an agent-based model of a market. The goal of the learning agent is to obtain a marketing investment strategy that improves the awareness of its corresponding brand in the marketing scenario. In opposition to conventional marketing investment strategies, the learned strategy is dynamic, so the agent makes its investment decision on-line based on the current state of the market. We choose the Double Deep Q-Network algorithm to train this agent on diverse instances of the model, each of them with different knowledge levels of the brand. First we adjust a subset of the hyperparameters of Double Deep Q-Network on two of the model instances, and then we use the best configuration found to train the agent on all the available instances. The brand agent learns a dynamic policy that optimizes brand's awareness levels. We perform an expert analysis of the policy obtained, where we observe that the learning brand agent tends to increase investment in media channels with greater awareness impact, but it also invests in other channels according to the situation and the characteristics of the model instance. These results show the benefits of having an on-line dynamic learning environment in a decision support system for media planning in marketing.

Global cooperation poses problems shaped by the effects of stakeholders¿ decisions on future losses, and how collective risks are perceived. Institutional sanctioning of free-riders may prevent widespread defection in this context. Yet, the prevalence of such institutions represents another second-order dilemma, often as tricky as the tragedy of the commons we aim to avert. Here, we combine evolutionary game theory with techniques widely used in marketing to find, target, and seed leaders and institutions¿ promoters to boost cooperation in climate dilemmas. By doing so, we identify the increase in cooperation when introducing a heterogeneous networked population and the conditions under which seeding policies can ensure the self-organization of cooperation and stable institutions. Counter-intuitively, we show that seeding a small fraction of institutional supporters at random network positions practically is as good policy as seeding highly central players in climate dilemmas. We show that cooperation and institutions prevalence are mainly determined by the interaction network and, to a less extent, by imitation ties, and that seeding cooperators only offers marginal benefits when compared with directly seeding sanctioning institutions. Our study also presents a way of incorporating costs when deciding the best policy to apply. Finally, this work suggests that the potential benefits of seeding and targeting techniques are not exclusive to collective dilemmas and can be applied to other dilemmas having structured populations.

Agent-based modeling has proven to be a useful simulation tool in marketing to analyze what-if scenarios and support strategic marketing decisions. Over the years, the field has evolved and there is a substantial number of scientific publications that focus on different aspects of agent-based modeling. However, there is no recent bibliometric analysis covering the entire marketing area. This article provides an updated bibliometric analysis on agent-based modeling in marketing in the last 2¿decades. The goal of this study is to provide both a performance and a science mapping analysis. The performance analysis explores highly cited articles, researchers, and geographical features. The science mapping analysis examines the relationships between words, authors, and citations of the literature. Moreover, this survey includes a comprehensive reference table characterizing the agent-based models collected. The results show that the most common aspects studied are related with diffusion and social networks. Additionally, we see that extensive research has been mainly carried out for the study of the effects of topology and innovation diffusion.

The maritime shipping industry will increasingly switch to low carbon fuels and adopt energy saving technologies (ESTs) to achieve the industry target of decarbonization. Among ESTs, deck equipment, including those based on wind propulsion technologies (WPTs), represents the largest potential fuel savings and a source of increasing innovation initiatives by industry actors. Previous contributions to WPT innovation have addressed barriers and drivers for increased adoption in the industry but failed to consider the specific aspects of the fleet retrofitting market. Through an agent-based simulation model, this work studies the effects of different policy and market scenarios (subsidies, fuel prices, and networking) on the adoption of WPT retrofitting solutions. The proposed model incorporates two decision steps for each vessel to adopt the technology (acquiring awareness of the technology, and a utility decision process to determine the WPT option). The study also expands on previous knowledge by modeling three WPT options and by integrating real world data of technology costs and their fuel savings as well as vessel features. Insights from simulations allow to identify the most convenient policies as well as the potential of alternative models to reduce introduction barriers (e.g., product-service business models).

We present an evolutionary trust game to investigate the formation of trust in sharing economy situations, where participants have a fixed provider or consumer role, and can only choose between trustworthy or untrustworthy behaviour. There are a variety of sharing economy platforms catering for differing goods and services, the properties of which may affect the degree to which these roles are variable for users. To the best of our knowledge, this is the first time the evolution of trust in sharing economy situations with fixed provider and consumer roles is being studied in the literature. Our trust model comprises four player types: trustworthy consumer, trustworthy provider, untrustworthy consumer, and untrustworthy provider. Five scenarios with varying initial population ratios of these player types under different host network structures are systematically investigated. Our results show that, in contrast to previous work that allowed switching roles between providers and consumers, trust declines monotonically as the reward for trustworthy behaviour is reduced, with a critical transition point for inversion of trustworthy/untrustworthy populations. In addition, the initial population of trustworthy providers is shown to significantly affect the point at which trustworthy behaviour most declines, with a high proportion resulting in the persistence of trustworthiness even when the reward for such behaviour is significantly low. Our results also show that, when different host network structures are considered, the average degree of the underlying network is an important factor in determining the level of trustworthiness in the population. Our findings may be of importance for understanding the emergence and maintenance of trust in sharing economy platforms where user roles are completely or predominantly rigid.

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.

The spread of COVID-19 and ensuing containment measures have accentuated the profound interdependence among nations or regions. This has been particularly evident in tourism, one of the sectors most affected by uncoordinated mobility restrictions. The impact of this interdependence on the tendency to adopt less or more restrictive measures is hard to evaluate, more so if diversity in economic exposures to citizens¿ mobility are considered. Here, we address this problem by developing an analytical and computational game-theoretical model encompassing the conflicts arising from the need to control the economic effects of global risks, such as in the COVID-19 pandemic. The model includes the individual costs derived from severe restrictions imposed by governments, including the resulting economic interdependence among all the parties involved in the game. By using tourism-based data, the model is enriched with actual heterogeneous income losses, such that every player has a different economic cost when applying restrictions. We show that economic interdependence enhances cooperation because of the decline in the expected payoffs by free-riding parties (i.e., those neglecting the application of mobility restrictions). Furthermore, we show (analytically and through numerical simulations) that these cross-exposures can transform the nature of the cooperation dilemma each region or country faces, modifying the position of the fixed points and the size of the basins of attraction that characterize this class of games. Finally, our results suggest that heterogeneity among regions may be used to leverage the impact of intervention policies by ensuring an agreement among the most relevant initial set of cooperators.

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.

The current COVID-19 pandemic has impacted millions of people and the global economy. Tourism has been one the most affected economic sectors because of the mobility restrictions established by governments and uncoordinated actions from origin and destination regions. The coordination of restrictions and reopening policies could help control the spread of virus and enhance economies, but this is not an easy endeavor since touristic companies, citizens, and local governments have conflicting interests. We propose an evolutionary game model that reflects a collective risk dilemma behind these decisions. To this aim, we represent regions as players, organized in groups; and consider the perceived risk as a strict lock-down and null economic activity. The costs for regions when restricting their mobility are heterogeneous, given that the dependence on tourism of each region is diverse. Our analysis shows that, for both large populations and the EU NUTS2 case study, the existence of heterogeneous costs enhances global agreements. Furthermore, the decision on how to group regions to maximize the regions¿ agreement of the population is a relevant issue for decision makers to consider. We find out that a layout of groups based on similar costs of cooperation boosts the regions¿ agreements and avoid the risk of having a total lock-down and a negligible tourism activity. These findings can guide policy makers to facilitate agreements among regions to maximize the tourism recovery.

Calibrating agent-based models involves estimating multiple parameter values. This can be performed automatically using automatic calibration but its success depends on the optimization method's ability for exploring the parameter search space. This paper proposes to carry out this process using coral reefs optimization algorithms, a new branch of competitive bio-inspired metaheuristics that, beyond its novel metaphor, has shown its good behavior in other optimization problems. The performance of these metaheuristics for model calibration is evaluated by conducting an exhaustive experimentation against well-established and recent evolutionary algorithms, including their hybridization with local search procedures. The study analyzes the calibration accuracy of the metaheuristics using an integer coding scheme over a benchmark of 12 problem instances of an agent-based model with an increasing number of decision variables. The outstanding performance of the memetic coral reefs optimization is reported after performing statistical tests to the results.

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.

Startup companies boost the quality of everyday life in almost all dimensions, and their products and services are of relevance everywhere. One of the most important goals that startups pursue is to increase the number of their users quickly. Users are of two types, new and returning. The present study presents an agent-based model to simultaneously deal with these two types of users by placing them in a preferential attachment network to interact. In this model, new users can be added at each time step according to word-of-mouth (WOM) and marketing activities. To define the retention probability for an agent, a set of real users in the records with the same properties as the agent are looked for to check what they have done in the same situation. To validate and test the model, the agent-based system is first thoroughly verified and then applied to the real data of a startup in the game app industry. After the experiments on a real scenario, the best decisions are made about the users to focus on or incentivize, and the best combination of acquisition and retention policies is adopted. The results show that user retention on the early days of adoption is better than the acquisition of new users. In this regard, acquisition should be focused on when the retention is in an acceptable state. Furthermore, the highest increase in the number of users occurs when there is a good balance between acquisition and retention.

This paper presents a computational evolutionary game model to study and understand fraud dynamics in the consumption tax system. Players are cooperators if they correctly declare their value added tax (VAT), and are defectors otherwise. Each player's payoff is influenced by the amount evaded and the subjective probability of being inspected by tax authorities. Since transactions between companies must be declared by both the buyer and seller, a strategy adopted by one influences the other?s payoff. We study the model with a wellmixed population and different scalefree networks. Model parameters were calibrated using real-world data of VAT declarations by businesses registered in the Canary Islands region of Spain. We analyzed several scenarios of audit probabilities for high and low transactions and their prevalence in the population, as well as social rewards and penalties to find the most efficient policy to increase the proportion of cooperators. Two major insights were found. First, increasing the subjective audit probability for low transactions is more efficient than increasing this probability for high transactions. Second, favoring social rewards for cooperators or alternative penalties for defectors can be effective policies, but their success depends on the distribution of the audit probability for low and high transactions.

Recent large-scale migration flows from rural areas of the Mekong Delta (MKD) to larger cities in the South-East (SE) region of Vietnam have created the largest migration corridor in the country. This migration trend has further contributed to greater rural¿urban disparities and widened the development gap between regions. In this study, our aim is to understand the migration dynamics and determine the most critical factors affecting the behavior of migrants in the MKD region. We present an agent-based model and incorporate the Theory of Planned Behavior to effectively break down migration intention into related components and contributing factors. A genetic algorithm is used for automated calibration and sensitivity analysis of model parameters, in order to validate our agent-based model. We further explore the migration behavior of people in certain demographic groups and delineate migration flows across cities and provinces from the MKD to the SE region.

Current dynamic markets require manufacturing industries to organize a robust plan to cope with uncertain demand planning. This work addresses the mixed-model multi-manned assembly line balancing under uncertain demand and aims to optimize the assembly line configuration by a robust mixed-integer linear programming (MILP) model and a robust solution generation mechanism embedded with dispatching rules. The proposed model relaxes the cycle time constraint and designs robust sequencing constraints and objective functions to ensure the line configuration can meet all the demand plans. Furthermore, two solution generation mechanisms, including a task-operator-sequence and an operator-task-sequence, are designed. To quickly find a suitable line configuration, a gene expression programming (GEP) approach with multi-attribute representation is proposed to obtain efficient dispatching rules which are ultimately embedded into the solution generation mechanisms. Experimental results show that solving the proposed MILP model mathematically is effective when tackling small and medium-scale instances. However, for large instances, the dispatching rules generated by the GEP have significant superiority over traditional heuristic rules and those rules mined by a genetic programming algorithm.

This paper simulates the effect of the strategies implemented by politicians after the terrorists attacks in Madrid on 11 March 2004 on the ideological distance between voters and political parties. The attacks took place three days before the elections and changed the campaign's agenda, which centered around the issue of who was responsible for the attack: ETA or Al Qaeda. It also altered the agenda of the mass media, which focused its informative activity on broadcasting news related to this issue. We did an exhaustive selection process of all the news broadcast on television, radio, and newspapers that made reference to the authorship of the attack. Using these messages we developed an agent-based model for explaining how the political strategies implemented by political parties influenced the ideological distance. The proposed model is based on the ideological proximity model by Downs (1957). After calibrating and validating the model with real data, we simulated the effect of three political strategies from the theory of terror management on the ideological distance between voters and political parties: the rally around the flag, the opinion leadership, and the priming of public opinion and media coverage. The results show that these strategies have a significant and stable impact on the ideological distance. In particular, the rally around the flag can have a lasting effect, capable of changing the ideological distance in the short term after a terrorist attack.

The introduction of Electric Vehicles (EVs) in modern fleets facilitates green road transportation. However, the driving ranges of EVs are limited by the duration of their batteries, which arise new operational challenges. Hybrid fleets of gas and EVs might be heterogeneous both in loading capacities as well as in driving-rangecapabilities, which makes the design of efficient routing plans a difficult task. In this paper, we propose a new Multi-Round Iterated Greedy (MRIG) metaheuristic to solve the Heterogeneous Vehicle Routing Problem with Multiple Driving ranges and loading capacities (HeVRPMD). MRIG uses a successive approximations method to offer the decision maker a set of alternativefleet configurations, with different distance-based costs and green levels. The numerical experiments show that MRIG is able to outperform previous works dealing with the homogeneous version of the problem, which assumes the same loading capacity for all vehicles in the fleet. The numerical experiments also confirm that the proposed MRIG approach extends previous works by solving a more realistic HeVRPMD and provides the decision-maker with fleets with higher green levels.

Changes in demand when manufacturing different products require an optimization model that includes robustness in its definition and methods to deal with it. In this work we propose the r-TSALBP, a multiobjective model for assembly line balancing to search for the most robust line configurations when demand changes. The robust model definition considers a set of demand scenarios and presents temporal and spatial overloads of the stations in the assembly line of the products to be assembled. We present two multiobjective evolutionary algorithms to deal with one of the r-TSALBP variants. The first algorithm uses an additional objective to evaluate the robustness of the solutions. The second algorithm employs a novel adaptive method to evolve separate populations of robust and non-robust solutions during the search. Results show the improvements of using robustness information during the search and the outstanding behavior of the adaptive evolutionary algorithm for solving the problem. Finally, we analyze the managerial impacts of considering the r-TSALBP model for the different organization departments by exploiting the values of the robustness metrics.

In this contribution, we propose an interactive multicriteria optimisation framework for the time and space assembly line balancing problem. The framework allows decision maker interaction by means of reference points to obtain the most interesting non-dominated solutions. The principal components of the framework are the g-dominance preference scheme and a state-of-the-art memetic multiobjective ant colony optimisation approach. In addition, the framework includes a novel adaptive multi-colony mechanism to be able to handle the preferences in an interactive way. Results show how the multiobjective framework can interactively obtain the most useful solutions with higher convergence than previous a priori methods. The experimentation also makes use of original data of the Nissan Pathfinder engine and practical bounds to define industrially feasible solutions in a set of scenarios. By solving the problem in these scenarios, we show the search guidance advantages of using an interactive multiobjective ant colony optimisation method.

Time and space assembly line balancing considers realistic multi-objective versions of the classical assembly line balancing industrial problems. It involves the joint optimisation of conflicting criteria such as the cycle time, the number of stations, and/or the area of these stations. The different problems included in this area also inherit the precedence constraints and the cycle time limitations from assembly line balancing problems. The presence of these hard constraints and their multi-criteria nature make these problems very hard to solve. Multi-objective constructive metaheuristics (in particular, multi-objective ant colony optimisation) have demonstrated to be suitable approaches to solve time and space assembly line balancing problems. The aim of this contribution is to present a new mechanism to induce diversity in an existing multi-objective ant colony optimisation algorithm for the 1/3 variant of the time and space assembly line balancing problem. This variant is quite realistic in the automative industry as it involves the joint minimisation of the number and the area of the stations given a fixed cycle time limit. The performance of our proposal is validated considering ten real-like problem instances. Moreover, the diversity induction mechanism is also tested on a real-world instance from the Nissan plant in Barcelona (Spain). © 2010 Springer-Verlag.

Robust optimization tries to find flexible solutions when solving problems with uncertain scenarios and vague information. In this paper we present a multiobjective evolutionary algorithm (EMO) to solve robust multiobjective optimization problems. This algorithm is a novel adaptive method able to evolve separate populations of robust and non robust solutions during the search. It is based on the existing infeasibility driven evolutionary algorithm (IDEA) and uses an additional objective to evaluate the robustness of the solutions. The original and adaptive IDEAs are applied to solve the rTSALBP-m/A, an assembly line balancing model that considers a set of demand production plans and includes temporal overloads of the stations of the assembly line with respect to these plans as robustness functions. Our results show that the proposed adaptive IDEA gets more robust non-dominated solutions for the problem. Also, we show that, for the case of the r-TSALBP-m/A, we can obtain Pareto fronts with a higher convergence when including robustness information during the search of the algorithm.

Time and space assembly line balancing considers realistic multiobjective versions of the classical assembly line balancing industrial problems, involving the joint optimization of conflicting criteria such as the cycle time, the number of stations, and/or the area of these stations. The aim of this contribution is to present a new multiobjective memetic algorithm based on ant colony optimization for the 1/3 variant of this family of industrial problems. This variant involves the joint minimisation of the number and the area of the stations, given a fixed cycle time limit. The good behaviour of the proposal is shown in nine problem instances. © 2011 IEEE.

Time and space assembly line balancing considers realistic multiobjective versions of the classical assembly line balancing industrial problems, involving the joint optimisation of conflicting criteria such as the cycle time, the number of stations, and/or the area of these stations. The aim of this contribution is to present a new algorithm, based on the GRASP methodology, for the 1/3 variant of this family of industrial problems. This variant involves the joint minimisation of the number and the area of the stations, given a fixed cycle time limit. The good behaviour of our proposal is demonstrated by means of performance indicators in four problem instances and a real one from a Nissan factory. © 2010 Springer-Verlag.

We present a new mechanism to introduce diversity to a multiobjective algorithm based on Ant Colony Optimization to solve a more realistic extension of a classical industrial problem: Time and Space Assembly Line Balancing. Promising results are shown after applying the designed Multiobjective Ant Colony Optimization algorithm to ten real-like problem instances. ©2009 IEEE.

We present an extension of a multi-objective algorithm based on Ant Colony Optimisation to solve a more realistic variant of a classical industrial problem: Time and Space Assembly Line Balancing. We study the influence of incorporating some domain knowledge by guiding the search process of the algorithm with preferences-based dominance. Our approach is compared with other techniques, and every algorithm tackles a real-world instance from a Nissan plant. We prove that the embedded expert knowledge is even more justified in a real-world problem. © 2008 Springer-Verlag Berlin Heidelberg.