Frontier-based Approach Research Articles

Nested frontier-based best practice regulation under asymmetric information in a principal–agent framework

This study focuses on public or private organisations with a large set of operation units. Central managers in these organisations face asymmetric information on the operating costs of sub-units and wish to incentivise them to reveal their minimal costs. In this paper, best practice regulation is implemented to deal with the asymmetric information via a regulation game. In the regulation game, the operating costs of sub-units are divided into several cost levels. To describe the unknown cost levels, a nested frontier-based approach, called context-dependent data envelopment analysis (CD-DEA), is introduced. By using CD-DEA, the efficient feasible incentive contract to the regulation game is proposed, and the condition that guarantees optimality of the proposed contract is also presented. Moreover, the efficient feasible incentive contract shows that the best responses of sub-units in the regulation game are to announce their actual cost levels, which constitute the Nash equilibrium. Several discussions are further provided to ensure the applicability of the proposed approach in realistic environments. Lastly, the proposed approach is illustrated by data from electricity generation and distribution sectors.

A proposal for measuring efficiency losses of asset management companies: Frontier-based approach

The performance of funds by asset management companies needs to be based on an objective benchmark. Studies related to the topic focus on measuring performance using a discrete form that cannot capture precise total efficiency losses. In this study, we propose a continuous approach to compare the performance of funds by taking advantage of the mean-variance efficient frontier with consideration of multiple risk levels. In an empirical analysis, our proposed method is applied to asset management companies with respect to open-end funds. For comparison, we use the output of an averaged Sharpe index. Because averaging the Sharpe index is inevitable for multiple risk levels, this method of calculation causes a loss of efficiency information. Hence, the proposed method has a continuous form of measuring performance, and the results of the two methods demonstrate significantly different patterns.

Light-Emitting Diode (LED) versus High-Pressure Sodium Vapour (HPSV) Efficiency: A Data Envelopment Analysis Approach with Undesirable Output

Road lighting is essential to ensure the safety and comfort of its users, especially in preventing accidents and aiding visual tasks. The monumental shift from conventional road lighting technology to light-emitting diode (LED) lighting is driven by energy efficiency, associated cost savings, and environmental concerns in the road lighting system. This study aims to investigate the performance of LED in substitution for high-pressure sodium vapour (HPSV) road lighting in Penang Bridge, Malaysia using the Data Envelopment Analysis (DEA), a frontier-based optimisation approach, by modelling energy, cost, and environment together, as none of the previous studies has included energy, cost, and environmental concerns together in one model. The LED renewable energy industry that promotes zero carbon emissions has the potential to establish an affordable, clean, and carbon-free energy system for road lighting, especially in rural areas.

A Common Optimization Framework for Multi-Robot Exploration and Coverage in 3D Environments

This paper studies the problems of static coverage and autonomous exploration of unknown three-dimensional environments with a team of cooperating aerial vehicles. Although these tasks are usually considered separately in the literature, we propose a common framework where both problems are formulated as the maximization of online acquired information via the definition of single-robot optimization functions, which differs only slightly in the two cases to take into account the static and dynamic nature of coverage and exploration respectively. A common derivative-free approach based on a stochastic approximation of these functions and their successive optimization is proposed, resulting in a fast and decentralized solution. The locality of this methodology limits however this solution to have local optimality guarantees and specific additional layers are proposed for the two problems to improve the final performance. Specifically, a Voronoi-based initialization step is added for the coverage problem and a combination with a frontier-based approach is proposed for the exploration case. The resulting algorithms are finally tested in simulations and compared with possible alternatives.

Learning to Cooperate via an Attention-Based Communication Neural Network in Decentralized Multi-Robot Exploration

In a decentralized multi-robot exploration problem, the robots have to cooperate effectively to map a strange environment as soon as possible without a centralized controller. In the past few decades, a set of “human-designed” cooperation strategies have been proposed to address this problem, such as the well-known frontier-based approach. However, many real-world settings, especially the ones that are constantly changing, are too complex for humans to design efficient and decentralized strategies. This paper presents a novel approach, the Attention-based Communication neural network (CommAttn), to “learn” the cooperation strategies automatically in the decentralized multi-robot exploration problem. The communication neural network enables the robots to learn the cooperation strategies with explicit communication. Moreover, the attention mechanism we introduced additionally can precisely calculate whether the communication is necessary for each pair of agents by considering the relevance of each received message, which enables the robots to communicate only with the necessary partners. The empirical results on a simulated multi-robot disaster exploration scenario demonstrate that our proposal outperforms the traditional “human-designed” methods, as well as other competing “learning-based” methods in the exploration task.