The evolution of negotiation and impasse in two-party multi-issue bargaining

Automated negotiation systems are becoming increasingly important and pervasive. Most previous research on automated negotiation has focused on understanding and formalizing “successful” negotiations, i.e., negotiations that do not become contentious to the point of impasse. This paper shifts the emphasis to negotiations that are “difficult” to resolve and can hit an impasse. It analyses a situation where two agents bargain over the division of the surplus of several distinct issues to demonstrate how a procedure to avoid impasses can be utilized in a specific negotiation setting. The procedure is based on the addition of new issues to the agenda during the course of negotiation and the exploration of the differences in the valuation of these issues to capitalize on Pareto optimal agreements. This paper also lays the foundation for performing an experiment to investigate how the evolution of negotiation contributes to the avoidance of impasses, paying particular attention to the expansion of the number of issues to be deliberated and its impact on the frequency of impasse

Interdisciplinary approach to automated negotiation: a preliminary report

Autonomous agents with negotiation competence are becoming increasingly important and pervasive. This paper follows an interdisciplinary approach to build autonomous negotiating agents by considering both game-theoretic techniques and bargaining procedures from the social sciences. The paper presents a generic model that handles bilateral multi-issue negotiation, describes equilibrium strategies for the bargaining game of alternating offers, and formalizes important strategies used by human negotiators. Autonomous agents equipped with the model are able to negotiate under both complete and incomplete information, thereby making them very compelling for automated negotiation

Towards an interdisciplinary framework for automated negotiation

Negotiation is an important and pervasive form of social interaction. The design of autonomous negotiating agents involves the consideration of insights from multiple relevant research areas to integrate different perspectives on negotiation. As a starting point for an interdisciplinary research effort, this paper presents a model that handles bilateral multi-issue negotiation, employs game-theoretic techniques to define equilibrium strategies for the bargaining game of alternating offers, and formalizes a set of negotiation strategies and tactics studied in the social sciences. Autonomous agents equipped with the model are currently being developed using the Jade framework. The agents are able to negotiate under both complete and incomplete information, thereby making the model in particular and the agents in general very compelling for automated negotiatio

Weekly self-scheduling, forward contracting, and pool involvement for an electricity producer: an adaptive robust optimization approach

his paper addresses the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be uncertain, and are described by uncertainty convex sets. To solve this problem, two variants of a constraint generation algorithm are proposed, and their application and characteristics discussed. Both algorithms are used to solve two case studies based on two producers, each operating equivalent generation units, differing only in the thermal units’ characteristics. Their market strategies are investigated for three different scenarios, corresponding to as many instances of electricity price forecasts. The effect of the producers’ approach, whether conservative or more risk prone, is also investigated by solving each instance for multiple values of the so-called budget parameter. It was possible to conclude that this parameter influences markedly the producers’ strategy, in terms of scheduling, profit, forward contracting, and pool involvement. These findings are presented and analyzed in detail, and an attempted rationale is proposed to explain the less intuitive outcomes. Regarding the computational results, these show that for some instances, the two variants of the algorithms have a similar performance, while for a particular subset of them one variant has a clear superiority

Hydrogen PEMFC stack performance analysis : a data-driven approach

For low power fuel cells, it is paramount that management of reactants, water and heat, be realized in a passive fashion in order to minimize parasitic losses. Effective fuel, oxygen supply and water management for reliable performance are also greatly affected by cell geometry and materials. Fuel cells are complex systems to optimize on a mere experimental basis. As an aid to this goal, data-driven analysis techniques, requiring no mathematical model to be fixed a priori, are gaining a reputation in other fields of work, where a phenomenological modeling approach might be intractable. This work presents a characterization study of a 12W PEMFC series stack by means of a new data-driven technique, M-NMF. The stack was developed for low temperature operation, uses own designed flow field plates, integrated in a series configuration, and is operated for 12 combinations of hydrogen/air flowrate ratios, generating as many polarization curves. M-NMF is applied, in combination with an alternating least squares algorithm, to the analysis of the overvoltage data matrix derived from the original experimental polarization data. From this analysis, it is possible to group and differentiate data according to similar overvoltage patterns and gain insight into their relative contribution to fuel cell performance immunization

Design and planning of green supply chains: a fuzzy approach

Green SC can be seen as logistic structures that guarantee production and global distribution of products in an environmental manner. To achieve this goal companies must invest on the optimal design and planning of their logistic structures, while accounting for the trade-off between profits and environmental impacts. This is addressed using a generic and uniform mathematical framework, the RTN. For this bi-level optimization a SFLP approach is applied, where those objectives are treated as constraints and replaced by a new one, the aspiration level, which embodies a compromise between them

A goal programming approach for the retrofit of supply chain networks

In order to achieve sustainability, the design and planning of a supply chain has to fulfil economic, social and environmental objectives. Traditionally the design of supply chains has been based on economic objectives. As societal environment concerns grow, environmental aspects are also emerging, not only at the industry level, but also within the context of supply chain management. The investment towards logistics structures that consider both economic and environmental performance is nowadays an important research topic. However, much is still to be done. This paper, addresses the retrofit of supply chain networks where planning aspects are also considered. The supply chain network design and planning is modeled through a Resource-Task-Network (RTN) methodology. A mixed integer linear programming (MILP) multi-objective approach is developed, which attempts to simultaneously maximize the annual profit of the supply chain, taking into account the network retrofit, while environmental impacts are minimized. The environmental impacts are accounted for through the Eco-indicator 99 methodology. Profit and environmental impacts are balanced through the use of goal programming. The model applicability is illustrated through the solution of an example

Supply chain network optimization with environmental impacts

Traditionally the design of supply chains has been based on economic objectives. As societal environment concerns grow, environmental aspects are also emerging, at the industry level, as decisive factors within the context of supply chain management. The investment towards logistics structures that consider both economic and environmental performance is nowadays an important research topic. However, much is still to be done. This paper, addresses the planning and design of supply chain structures for annual profit maximization, while considering environmental aspects. The latter are accounted through the Eco-indicator methodology, which is used to quantify the damage to human health. Profit and environmental impacts are balanced through the use of an optimization approach adapted from symmetric fuzzy linear programming (SFLP), while the supply chain is modelled as a mixed integer linear programming (MILP) optimization problem using the Resource-Task- Network (RTN) methodology. The obtained model is validated through the solution of an example, where its applicability to supply chain problems is demonstrated

Performance indicators for reactive distillation design

A cost indicator for the design and multi-objective optimization of reactive distillation columns, designated capacity, was introduced in previous work by the authors. The question of this indicator’s effectiveness as a measure of the actual column cost, is herein investigated over a number of designs by comparing it with the value obtained by means of conventional costing procedures. The results show that the level of accuracy obtained when using capacity is satisfactory and certainly acceptable for a preliminary design stage

Novel data-driven methodologies for parameter estimation and interpretation of fuel cells performance

Fuel cell based power generation systems are expected to become more widespread in the near future. Stationary fuel cells may be used as an uninterruptible or back-up power supply, or to supply micro-grids. In particular, proton exchange membrane fuel cells (PEMFC) are an attractive technology due to its high energy density, rigid and simple structure, low operating temperature and fast start-up characteristics. The power quality assessment of fuel cells as a viable power sources requires a good understanding of the fuel cell performance characteristics. This paper presents two novel data-driven methodologies for the identification of the main steady state (polarization curve) and the dynamic (impedance response) characteristics for fuel-cells allowing the development of rapid, accurate and empirical models based on the experimental data. M-NMF is a modified non-negative matrix factorization technique developed for the analysis of polarization curve data that allows to identify the three main contributions for the fuel-cell power degradation, while for impedance spectroscopy data, this paper proposes the use of fractional order transfer functions (FC-FOTC) to describe the main dynamic modes present in the fuel-cell. A brief description of these two approaches is presented, together with the analysis of a real experimental dataset obtained from a 12W open cathode PEMFC stack to illustrate their potential and scope. While the former is instrumental for the deconvolution of the fuel cell polarization curves into its major components, the latter enables the estimation of the parameters related to the inherent transport and kinetic phenomena, thus opening the way, in both cases, for the interpretation of the effect of the operating conditions on the relative dominance and magnitude of these components and phenomena