Governance is a complex, multidimensional construct difficult to measure and has economic, social, and sustainability implications. This study revisits the literature on governance, examining its conceptual frameworks and its relationships with economic, social, and sustainability factors. The study reveals several gaps in the representation of multidimensional governance. First, the almost exclusive use of Principal Component Analysis (PCA) and Simple Additive Weighting with Equal Weights (SAW-EW) to operationalize the construct. Second, the limited use of tests to verify the quality and reliability of the representations. Third, the analysis of governance as a unidimensional construct. This research aims to address these gaps by incorporating quality and reliability parameters into a new multicriteria decision model for constructing composite indicators. The multicriteria decision model uses ranking uncertainty, links to other variables, and the composite indicator's informational and discriminant power to identify the best method for representing multidimensional governance. The use of this framework across OECD countries shows that the Entropy method represents multidimensional governance with greater quality and reliability than the PCA, SAW-EW, Benefit of the Doubt (BoD), and Ordered Weighted Average (OWA) methods. The Entropy method ensures a more accurate alignment between the concept of multidimensional governance and economic, social, and sustainability aspects, providing robust information for political and investment decisions. The quality and reliability framework is adaptable to other multidimensional phenomena, with the best method defined for each context.
In a rational multi-criteria decision-making (MCDM) process based on multi-attribute utility theory (MAUT), the decision-maker defines quantitative parameters to identify the most promising alternative. However, it is often difficult to determine these parameters precisely. Therefore, the literature suggests using imprecise information, which allows the decision-maker to determine imprecise preference statements and parameters. This paper investigates the utilization and impact of an imprecise information approach regarding probabilities, utility functions, and objective weights in the decision support system Entscheidungsnavi. We examined 1511 personal student decisions. First, we analyzed how often imprecise information was utilized. Second, we surveyed how helpful participants found the use of imprecise information. Third, we investigated the impact of imprecise information in the three aforementioned categories on the final ranking of alternatives. We assessed how robust the rank of the best alternative remained by using Monte Carlo simulations in which evenly distributed values were drawn from all imprecisely defined intervals. This demonstrates the robustness of our results across varying levels of input uncertainty. Most participants chose imprecise parameters in their decisions. Moreover, most participants found it helpful to use imprecise information in all categories, with imprecise objective weights being the least helpful. In about 83% of the decisions, the best-ranked alternative is robust against the imprecise intervals the decision-maker chooses. Overall, our results show that incorporating imprecision improves the practical usefulness of MCDM-Support-Systems. We recommend that a robust MAUT-based system should address all three types of imprecision, guide probability and utility elicitation, support realistic trade-offs, and visualize how input uncertainty affects ranking stability helping decision-makers form sound judgements.
This study presents a novel two commodity stochastic retrial inventory model tailored to handle batch demands while incorporating an innovative incentive mechanism to manage customer behavior and demand fluctuations. The model considers two distinct customer classes, each primarily interested in one of two commodities, with demand occurring in batches. A conditional complementary item policy is introduced to encourage larger purchases and foster cross-selling opportunities reflecting real-world marketing strategies. The inventory system is formulated as a level-dependent quasi-birth-and-death process. An (s,S1) inventory replenishment policy is applied to the primary commodity, while the secondary commodity is assumed to be replenished instantaneously. To analyze the system’s steady-state behavior, the Neuts and Rao matrix truncation method is applied, enabling the derivation of stability conditions and performance measures. Comprehensive numerical analysis is performed to optimize total cost and investigate the sensitivity of critical factors, such as expected total cost, customer waiting time, and successful retrial rate. Additionally, a detailed simulation study is conducted to validate the analytical findings, demonstrating the model’s effectiveness in real-world scenarios by capturing stochastic variations and system dynamics that may not be fully addressed through analytical methods alone. The simulation results provide further insights into the model’s robustness and practical applicability. This research advances the field of multi-commodity inventory systems, offering a valuable framework for optimizing inventory strategies in complex, interconnected market environments.
This study proposes a hybrid multi-criteria decision-making method for purchasing electric vehicles, by combining the analytic hierarchy process (AHP) with the evaluation based on distance from the average solution (EDAS). AHP assigns criteria weights and derives the scores for qualitative criteria, while the technical specifications of the electric vehicles are considered as the decision data for quantitative criteria. EDAS is employed to rank the electric vehicles and determine the best option, based on the appraisal scores that indicate the overall performance of each vehicle, relative to that of the average solution. The proposed method is implemented for making electric-vehicle purchase decisions, in Thailand, based on real-world data and decision-makers’ perspectives. The findings reveal that the proposed method can make effective electric-vehicle purchase decisions. It straightforwardly elicits the decision-makers’ needs through criteria weights; the decision-makers place high emphasis on the comfort criterion, with a weight of 0.176. Furthermore, the proposed method successfully ranks all electric vehicles and appropriately identifies the best electric vehicle, with an appraisal score of 0.749, which demonstrates superior overall performance across all decision-making attributes. The theoretical novelty of this study lies in the ability of the proposed method to reflect the decision-makers’ judgments, analyze both quantitative and qualitative criteria, and systematically derive the best decision. This study offers practical benefits to consumers, manufacturers, and government agencies, by supporting electric-vehicle purchase decisions, guiding the development of new electric-vehicle models, and informing policy formulations to promote electric-vehicle adoption.
This critical review analyzed 118 articles applying multicriteria decision analysis (MCDA) to environmental problems that were published in operational research (OR) journals. Main aim was to evaluate whether OR research is adequately treating real-world decision processes. Research questions emerged from analyzing 34 review articles covering mainly environmental literature. In contrast to this applied literature, results indicate that the field of OR is strong in uncertainty treatment. However, advanced modelling and analysis of temporal or spatial aspects was rare. Documentation of MCDA processes was often sketchy. Stakeholder engagement throughout decision-making processes was low. Main steps of MCDA were neglected, ranging from problem structuring, over preference elicitation, to discussing results with stakeholders. Future OR research could contribute substantially to solving todays’ pressing environmental problems by (i) improving applications to real-world decision processes; (ii) developing methods of stakeholder engagement and integrating soft-OR methods; (iii) applying behavioral OR for analyzing decision processes; (iv) supporting real-world implementation after MCDA; and (v) linking efforts across disciplines for proper documentation of practice interventions.
