Dr Manuel Chica Serrano

IEEE 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 & #x2013; the N-player evolutionary trust game & #x2013; consisting of three types of players: an investor, a trustee who is trustworthy, and 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 & #x2018;difficult & #x2019; 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.

© 2015 Elsevier Ltd. 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.

© 2014, Springer-Verlag Berlin Heidelberg. 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.

A large number of decision-making processes in real-life transportation, logistics and production applications can be modelled as NP-hard combinatorial optimization problems (COPs). These real-life COPs are frequently characterized by their large-scale sizes and the need to obtain high-quality solutions in short computing times. This imposes some severe limitations on the use of exact methods, so alternatives such as metaheuristics are required instead. Still, metaheuristics usually assume deterministic inputs and constraints. This is a strong assumption which is not usually in accordance with modern systems, typically characterized by high levels of dynamism and uncertainty. By combining simulation techniques with metaheuristics, simheuristic algorithms are able to cope with real-life uncertainty in a natural way. Simheuristics also facilitate the introduction of variability analysis during the evaluation of alternative solutions to stochastic COPs and, in this sense, they constitute a 'method of first resort'.

© 2015 IEEE. 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.