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Our \"stakeholder synergy\" perspective identifies new value creation opportunities that are especially effective strategically because a single strategic action (1) increases different types of value for two or more essential stakeholder groups simultaneously, and (2) does not reduce the value already received by any other essential stakeholder group. This result is obtainable because multiple potential sources of value creation exist for each essential stakeholder group. Actions that meet these criteria increase the size of the value \"pie\" available for essential stakeholder groups, and thereby serve to attract exceptional stakeholders and obtain their increasing effort and commitment. The stakeholder synergy perspective extends stakeholder theory further into the strategy realm, and offers insights for realizing broader value creation that is more likely to produce sustainable competitive advantage.

As extreme weather conditions due to climate change can cause deadly flood damages all around the world, a role of the flood vulnerability assessment has become recognized as one of the preemptive measures in nonstructural flood mitigation strategies. Although the flood vulnerability is most commonly assessed by a composite indicator compiled from multidimensional phenomena and multiple conflicting criteria associated with floods, directly or indirectly, it has been often overlooked that the construction frameworks and processes can have a significant influence on the flood vulnerability indicator outcomes. This study has, therefore, compared the flood vulnerability ranking orders for the 54 administrative districts in the Nakdong River Watershed of the Korean Peninsula, ranked from composite indicators by different frameworks and multi-attribute utility functions for combining the three assessment components, such as exposure, sensitivity, and coping, presented in the IPCC Third Assessment Report. The results show that the different aggregation components and utility functions under the same proxy variable system can lead to larger volatility of flood vulnerability rankings than expected. It is concluded that the vulnerability indicator needs to be derived from all three assessment components by a multiplicative utility function for a desirable flood vulnerability assessment to climate change.

The aim of this paper is twofold. On the one hand, the λ -additive measure (Sugeno λ -measure) is revisited, and a state-of-the-art summary of its most important properties is provided. On the other hand, the so-called ν -additive measure as an alternatively parameterized λ -additive measure is introduced. Here, the advantages of the ν -additive measure are discussed, and it is demonstrated that these two measures are closely related to various areas of science. The motivation for introducing the ν -additive measure lies in the fact that its parameter ν ∈ ( 0 , 1 ) has an important semantic meaning as it is the fix point of the complement operation. Here, by utilizing the ν -additive measure, some well-known results concerning the λ -additive measure are put into a new light and rephrased in more advantageous forms. It is discussed here how the ν -additive measure is connected with the belief-, probability- and plausibility measures. Next, it is also shown that two ν -additive measures, with the parameters ν 1 and ν 2 , are a dual pair of belief- and plausibility measures if and only if ν 1 + ν 2 = 1 . Furthermore, it is demonstrated how a ν -additive measure (or a λ -additive measure) can be transformed to a probability measure and vice versa. Lastly, it is discussed here how the ν -additive measures are connected with rough sets, multi-attribute utility functions and certain operators of fuzzy logic.

Objectives: Do utility scores based on patient preferences and scores based on community preferences agree? The purpose is to assess agreement between directly measured standard gamble (SG) utility scores and utility scores from the Health Utilities Index Mark 2 (HUI2) and Mark 3 (HUI3) systems. Methods: Patients were assessed repeatedly throughout the process of waiting to see a surgeon, waiting for surgery, and recovery after total hip arthroplasty (THA). Group mean scores are compared using paired t-tests. Agreement is assessed using the intraclass correlation coefficient (ICC). Results: The mean SG, HUI2, and HUI3 (SD) scores at assessment 1 are 0.62 (0.31), 0.62 (0.19), and 0.52 (0.21); n=103. At assessment 2, the means are 0.67 (0.30), 0.68 (0.30), and 0.58 (0.22); n=84. There are no statistically significant differences between group mean SG and HUI2 scores. Mean SG and HUI3 scores are significantly different. ICCs are low. Conclusions: At the mean level for the group, SG and HUI2 scores match closely. At the individual level, agreement is poor. HUI2 scores were greater than HUI3 scores. HUI2 and HUI3 are appropriate for group level analyses relying on community preferences but are not a good substitute for directly measured utility scores at the individual leve.

The working group, which is concerned with operational research methods and applications to agricultural science in its broad meaning (i.e. including Forest Management and Fisheries), was formed in 2003 within the European Association of Operational Research Societies (EURO). The first meeting of the group was held at the former Silsoe Research Institute in 2004. The group intends to have regular meetings in Europe at approximately yearly intervals, usually within the EURO Conferences. However, the next meeting will be held in 2008 within the British Operational Research Society's OR50 Conference in York, followed by the EURO XXIII Conference in Bonn in 2009 and the EURO XXIV Conference in Lisbon in 2010. The third meeting of the working group, chaired by Dr L. M. Plà of the University of Lleida, with the assistance of D. L. Sandars of Cranfield University and organized as a stream within the XXII EURO Conference, was held at the University of Economics in Prague from 8 to 11 July 2007 where the following papers were read in a set of 10 sessions.

Cognitive radio is an intelligent wireless solution that aims to enhance the access to the radio spectrum. In this technology, secondary users sense the radio spectrum, select the best channel among a pool of free channels, and determine the optimal transmission parameters to meet their quality-of-service requirements while maximizing the spectral efficiency. Over the past decade, several channel-ranking mechanisms have been proposed. However, these mechanisms consider only the remaining idle time of the channel and exclude some crucial parameters. This convincingly demonstrates a strong need for a new channel quality-ranking model that jointly considers several parameters to select the best communication channel for transmission. This paper proposes a utility model that integrates several important parameters for ranking channels. First, we underline the importance of the process of the channel quality ranking. Then, we describe a multi-attributes, utility-based, channel quality-ranking model. Finally, we describe a series of experiments and their results, which show that our model effectively ranks the best communication channels first.

The deployment of low Earth orbit (LEO) satellite mega-constellations enables global broadband access, but their high orbital velocity demands frequent handover decisions that critically impact service continuity. Conventional strategies that maximize instantaneous signal quality often trigger excessive handovers, while stability-focused approaches may sacrifice link performance. In this paper, we propose the Hybrid Handover Strategy (HHS), a low-complexity algorithm that addresses this trade-off. The HHS utilizes a multi-attribute utility function that integrates the signal-to-interference-plus-noise ratio (SINR), satellite elevation angle, and network load with a novel logistic-decay stability bonus mechanism. We provide a formal mathematical analysis of the algorithm’s stability and performance trade-offs. To ensure industrial relevance, the strategy is validated using a high-fidelity simulator driven by real-world two-line element (TLE) data from the Starlink constellation. Results demonstrate that the HHS reduces the handover frequency by 64% compared to SINR-based benchmarks while maintaining service availability of 90.2%. The proposed algorithm delivers these improvements with significantly smaller computational overhead than machine learning approaches, making it suitable for resource-constrained on-board processing and ground terminals.

Evaluating national climate policy performance requires frameworks that integrate multiple dimensions while accommodating diverse development pathways. This study develops a Multi-Attribute Utility Theory (MAUT) framework to construct a Climate Policy Performance Index (CPPI) for 187 countries. The index integrates four dimensions—mitigation, adaptation, economic capacity, and governance—using explicit utility functions and policy-aligned weights derived from climate policy priorities. Results reveal substantial cross-national heterogeneity, with CPPI scores ranging from 33.67 (Turkmenistan) to 78.46 (Norway). Nordic countries lead with balanced excellence across dimensions, while alternative high-performance pathways emerge through mitigation leadership (Uruguay and Costa Rica) or governance–economy strength (Singapore). Regional analysis identifies Europe as the top-performing region, whereas Sub-Saharan Africa achieves unexpectedly high rankings despite low emissions owing to weak institutional capacity. The relationship between income and climate performance is non-monotonic: lower-middle-income countries achieve aggregate scores comparable to those of high-income nations, with near-perfect mitigation performance compensating for weaker governance. Sensitivity analysis shows that ranking robustness is comparable across equal, adaptation-focused, and multiplicative weighting schemes, whereas mitigation-focused weights yield substantially different orderings (ρ = 0.47). The CPPI correlates moderately with ND-GAIN (r = 0.40) and weakly and negatively with CO2 per capita (r = −0.28), indicating that the framework captures distinct aspects of climate policy performance. The proposed methodology advances beyond existing indices by providing axiomatic foundations, transparent utility specifications, and comprehensive sensitivity analysis, offering a theoretically grounded tool for cross-national climate policy evaluation.

Project schedule management is an important guarantee for the smooth implementation of the project, which is closely related to the interests of all parties involved in the project, and how to improve the efficiency and quality of the project is a problem that still needs to be solved. In this paper, with reference to the actual situation in the project engineering, the objective function of schedule, cost, quality, and safety is proposed, and the multi-objective optimization mathematical model of the project engineering is obtained through multi-attribute utility theory modeling. Based on the objective function and constraints, the fitness function is established, the constructed model is solved using a genetic algorithm, and safeguard measures are designed to improve the efficiency of project engineering. The empirical results show that the duration and labor demand in the project schedule optimization scheme proposed by the model in this paper are much lower than those in the planning scheme of traditional project schedule optimization methods. Compared to the traditional method, the total cost of the project works decreased by 8.70%, while the quality of the work improved by 0.71%. It shows that the model can improve the quality of project efficiency and enhance the economic benefits of the entire project. The model proposed in this paper makes project schedule optimization more scientific, reasonable, and practical and has certain theoretical and practical significance. In addition, the establishment of a perfect schedule risk management mechanism can be considered in future research in this field to further improve the project schedule optimization model.

This paper introduces an interactive framework to guide decision-makers in a multi-criteria supplier selection process. State-of-the-art multi-criteria methods for supplier selection elicit the decision-maker’s preferences among the criteria by processing pre-collected data from different stakeholders. We propose a different approach where the preferences are elicited through an active learning loop. At each step, the framework optimally solves a combinatorial problem multiple times with different weights assigned to the objectives. Afterwards, a pair of solutions among those computed is selected using a particular query selection strategy, and the decision-maker expresses a preference between them. These two steps are repeated until a specific stopping criterion is satisfied. We also introduce two novel fast query selection strategies, and we compare them with a myopically optimal query selection strategy. Computational experiments on a large set of randomly generated instances are used to examine the performance of our query selection strategies, showing a better computation time and similar performance in terms of the number of queries taken to achieve convergence. Our experimental results also show the usability of the framework for real-world problems with respect to the execution time and the number of loops needed to achieve convergence.