A Fuzzy Data Envelopment Analysis Framework for Dealing with Uncertainty Impacts of Input–Output Life Cycle Assessment Models on Eco-efficiency Assessment

The uncertainty in the results of input–output-based life cycle assessment models makes the sustainability performance assessment and ranking a challenging task. Therefore, introducing a new approach, fuzzy data envelopment analysis, is critical; since such a method could make it possible to integrate the uncertainty in the results of the life cycle assessment models into the decision-making for sustainability benchmarking and ranking. In this paper, a fuzzy data envelopment analysis model was coupled with an input–output-based life cycle assessment approach to perform the sustainability performance assessment of the 33 food manufacturing sectors in the United States. Seven environmental impact categories were considered the inputs and the total production amounts were identified as the output category, where each food manufacturing sector was considered a decision-making unit. To apply the proposed approach, the life cycle assessment results were formulated as fuzzy crisp valued-intervals and integrated with fuzzy data envelopment analysis model, thus, sustainability performance indices were quantified. Results indicated that majority (31 out of 33) of the food manufacturing sectors were not found to be efficient, where the overall sustainability performance scores ranged between 0.21 and 1.00 (efficient), and the average sustainability performance was found to be 0.66. To validate the current study\u27s findings, a comparative analysis with the results of a previous work was also performed. The major contribution of the proposed framework is that the effects of uncertainty associated with input–output-based life cycle assessment approaches can be successfully tackled with the proposed Fuzzy DEA framework which can have a great area of application in research and business organizations that use with eco-efficiency as a sustainability performance metric

A NEW LOGARITHM METHODOLOGY OF ADDITIVE WEIGHTS (LMAW) FOR MULTI-CRITERIA DECISION-MAKING: APPLICATION IN LOGISTICS

Logistics management has been playing a significant role in ensuring competitive growth of industries and nations. This study proposes a new Multi-Criteria Decision-making (MCDM) framework for evaluating operational efficiency of logistics service provider (LSP). We present a case study of comparative analysis of six leading LSPs in India using our proposed framework. We consider three operational metrics such as annual overhead expense (OE), annual fuel consumption (FC) and cost of delay (CoD, two qualitative indicators such as innovativeness (IN) which basically indicates process innovation and average customer rating (CR)and one outcome variable such as turnover (TO) as the criteria for comparative analysis. The result shows that the final ranking is a combined effect of all criteria. However, it is evident that IN largely influences the ranking. We carry out a comparative analysis of the results obtained from our proposed method with that derived by using existing established frameworks. We find that our method provides consistent results; it is more stable and does not suffer from rank reversal problem

Designing performance incentives, an international benchmark study in the water sector

Cross-country comparisons avoid the unsteady equilibrium in which regulators have to balance between economies of scale and a sufficient number of remaining comparable utilities. By the use of data envelopment analysis, we compare the efficiency of the drinking water sector in the Netherlands, England and Wales, Australia, Portugal and Belgium. After introducing a procedure to measure the homogeneity of an industry, robust order-m partial frontiers are used to detect outlying observations. By applying bootstrapping algorithms, bias-corrected first and second stage results are estimated. Our results suggest that incentive regulation in the sense of regulatory and benchmark incentive schemes have a significant positive effect on efficiency. By suitably adapting the conditional efficiency measures of Daraio and Simar (Advanced robust and nonparametric methods in efficiency analysis. Springer, New York 2007) to the bias corrected estimates of Simar and Wilson (Manage Sci, 44(1): 49-61, 1998), we incorporate environmental variables directly into the efficiency estimates. We firstly equalize the social, physical and institutional environment, and secondly, deduce the effect of incentive schemes on utilities as they would work under similar conditions. The analysis demonstrates that in absence of clear and structural incentives the average efficiency of the utilities falls in comparison with utilities which are encouraged by incentives

Stochastic Nonparametric Envelopment of Data: Combining Virtues of SFA and DEA in a Unified Framework

The literature of productive efficiency analysis is divided into two main branches: the parametric Stochastic Frontier Analysis (SFA) and nonparametric Data Envelopment Analysis (DEA). This paper attempts to combine the virtues of both approaches in a unified framework. We follow the SFA literature and introduce a stochastic component decomposed into idiosyncratic error and technical inefficiency components imposing the standard SFA assumptions. In contrast to the SFA, we do not make any prior assumptions about the functional form of the deterministic production function. In this respect, we follow the nonparametric route of DEA that only imposes free disposability, convexity, and some specification of returns to scale. From the postulated class of production functions, the proposed method identifies the production function with the best empirical fit to the data. The resulting function will always take a piece-wise linear form analogous to the DEA frontiers. We discuss the practical implementation of the method and illustrate its potential by means empirical examples.Productivity Analysis,

Multi-Dimensional Assessment of Transit System Efficiency and Incentive-based Subsidy Allocation

Over the past several decades, contending with traffic congestion and air pollution has emerged as one of the imperative issues across the world. Development of a transit-oriented urban transport system has been realized by an increasing number of countries and administrations as one of the most effective strategies for mitigating congestion and pollution problems. Despite the rapid development of public transportation system, doubts regarding the efficiency of the system and financing sustainability have arisen. Significant amount of public resources have been invested into public transport; however complaints about low service quality and unreliable transit system performance have increasingly arisen from all walks of life. Evaluating transit operational efficiency from various levels and designing incentive-based mechanisms to allocate limited subsidies/resources have become one of the most imperative challenges faced by responsible authorities to sustain the public transport system development and improve its performance and levels of service. After a comprehensive review of existing literature, this dissertation aims to develop a multi-dimensional framework composed of a series of robust multi-criteria evaluation models to assess the operational and financial performance of transit systems at various levels of application (i.e. region/city level, operator level, and route level). It further contributes to bridging the gap between transit efficiency evaluation and the subsequent subsidy allocation by developing a set of incentive-based resource allocation models taking various levels of operational and financial efficiencies into consideration. Case studies using real-world transit data will be performed to validate the performance and applicability of the proposed models

ANN application in maritime industry : Baltic Dry Index forecasting & optimization of the number of container cranes

This dissertation is a study of dry bulk freight index forecasting and port planning, both based on Artificial Neural network application. First the dry bulk market is reviewed, and the reason for the high fluctuation of freight rates through the demand-supply mechanism is examined. Due to the volatile BDI, the traditional linear regression forecasting method cannot guarantee the performance of forecasting, but ANN overcomes this difficulty and gives better performance especially in a short time. Besides, in order to improve the performance of ANN further, wavelet is introduced to pre-process the BDI data. But when the noise (high frequency parts) is stripped, the hidden useful data may also be eliminated. So the performance of different degrees of de-noising models is evaluated, and the best one (most suitable de-noising model) is chosen to forecast BDI, which avoids over de-noising and keeps a fair ability of forecasting. In the second case study, the collected container terminals and ranked, and the throughput of each combination (different crane number) is estimated by applying a trained BP network. The BP network with DEA output is combined, simulating the efficiency of each combination. And finally, the optimal container crane number is fixed due to the highest efficiency and practical reasons. The Conclusion and Recommendation chapter gives some further advice, and many recommendations are given

Prioritization of patients' access to health care services

L'accès aux services de santé et les longs délais d'attente sont l’un des principaux problèmes dans la plupart des pays du monde, dont le Canada et les États-Unis. Les organismes de soins de santé ne peuvent pas augmenter leurs ressources limitées, ni traiter tous les patients simultanément. C'est pourquoi une attention particulière doit être portée à la priorisation d'accès des patients aux services, afin d’optimiser l’utilisation de ces ressources limitées et d’assurer la sécurité des patients. En fait, la priorisation des patients est une pratique essentielle, mais oubliée dans les systèmes de soins de santé à l'échelle internationale. Les principales problématiques que l’on retrouve dans la priorisation des patients sont: la prise en considération de plusieurs critères conflictuels, les données incomplètes et imprécises, les risques associés qui peuvent menacer la vie des patients durant leur mise sur les listes d'attente, les incertitudes présentes dans les décisions des cliniciens et patients, impliquant l'opinion des groupes de décideurs, et le comportement dynamique du système. La priorisation inappropriée des patients en attente de traitement a une incidence directe sur l’inefficacité des prestations de soins de santé, la qualité des soins, et surtout sur la sécurité des patients et leur satisfaction. Inspirés par ces faits, dans cette thèse, nous proposons de nouveaux cadres hybrides pour prioriser les patients en abordant un certain nombre de principales lacunes aux méthodes proposées et utilisées dans la littérature et dans la pratique. Plus précisément, nous considérons tout d'abord la prise de décision collective incluant les multiples critères de priorité, le degré d'importance de chacun de ces critères et de leurs interdépendances dans la procédure d'établissement des priorités pour la priorisation des patients. Puis, nous travaillons sur l'implication des risques associés et des incertitudes présentes dans la procédure de priorisation, dans le but d'améliorer la sécurité des patients. Enfin, nous présentons un cadre global en se concentrant sur tous les aspects mentionnés précédemment, ainsi que l'implication des patients dans la priorisation, et la considération des aspects dynamiques du système dans la priorisation. À travers l'application du cadre global proposé dans le service de chirurgie orthopédique à l'hôpital universitaire de Shohada, et dans un programme clinique de communication augmentative et alternative appelé PACEC à l'Institut de réadaptation en déficience physique de Québec (IRDPQ), nous montrons l'efficacité de nos approches en les comparant avec celles actuellement utilisées. Les résultats prouvent que ce cadre peut être adopté facilement et efficacement dans différents organismes de santé. Notamment, les cliniciens qui ont participé à l'étude ont conclu que le cadre produit une priorisation précise et fiable qui est plus efficace que la méthode de priorisation actuellement utilisée. En résumé, les résultats de cette thèse pourraient être bénéfiques pour les professionnels de la santé afin de les aider à: i) évaluer la priorité des patients plus facilement et précisément, ii) déterminer les politiques et les lignes directrices pour la priorisation et planification des patients, iii) gérer les listes d'attente plus adéquatement, vi) diminuer le temps nécessaire pour la priorisation des patients, v) accroître l'équité et la justice entre les patients, vi) diminuer les risques associés à l’attente sur les listes pour les patients, vii) envisager l'opinion de groupe de décideurs dans la procédure de priorisation pour éviter les biais possibles dans la prise de décision, viii) impliquer les patients et leurs familles dans la procédure de priorisation, ix) gérer les incertitudes présentes dans la procédure de prise de décision, et finalement x) améliorer la qualité des soins.Access to health care services and long waiting times are one of the main issues in most of the countries including Canada and the United States. Health care organizations cannot increase their limited resources nor treat all patients simultaneously. Then, patients’ access to these services should be prioritized in a way that best uses the scarce resources, and to ensure patients’ safety. In fact, patients’ prioritization is an essential but forgotten practice in health care systems internationally. Some challenging aspects in patients’ prioritization problem are: considering multiple conflicting criteria, incomplete and imprecise data, associated risks that threaten patients on waiting lists, uncertainties in clinicians’ decisions, involving a group of decision makers’ opinions, and health system’s dynamic behavior. Inappropriate prioritization of patients waiting for treatment, affects directly on inefficiencies in health care delivery, quality of care, and most importantly on patients’ safety and their satisfaction. Inspired by these facts, in this thesis, we propose novel hybrid frameworks to prioritize patients by addressing a number of main shortcomings of current prioritization methods in the literature and in practice. Specifically, we first consider group decision-making, multiple prioritization criteria, these criteria’s importance weights and their interdependencies in the patients’ prioritization procedure. Then, we work on involving associated risks that threaten patients on waiting lists and handling existing uncertainties in the prioritization procedure with the aim of improving patients’ safety. Finally, we introduce a comprehensive framework focusing on all previously mentioned aspects plus involving patients in the prioritization, and considering dynamic aspects of the system in the patients’ prioritization. Through the application of the proposed comprehensive framework in the orthopedic surgery ward at Shohada University Hospital, and in an augmentative and alternative communication (AAC) clinical program called PACEC at the Institute for Disability Rehabilitation in Physics of Québec (IRDPQ), we show the effectiveness of our approaches comparing the currently used ones. The implementation results prove that this framework could be adopted easily and effectively in different health care organizations. Notably, clinicians that participated in the study concluded that the framework produces a precise and reliable prioritization that is more effective than the currently in use prioritization methods. In brief, the results of this thesis could be beneficial for health care professionals to: i) evaluate patients’ priority more accurately and easily, ii) determine policies and guidelines for patients’ prioritization and scheduling, iii) manage waiting lists properly, vi) decrease the time required for patients’ prioritization, v) increase equity and justice among patients, vi) diminish risks that could threaten patients during waiting time, vii) consider all of the decision makers’ opinions in the prioritization procedure to prevent possible biases in the decision-making procedure, viii) involve patients and their families in the prioritization procedure, ix) handle available uncertainties in the decision-making procedure, and x) increase quality of care

An interval efficiency analysis with dual‑role factors

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data envelopment analysis (DEA) is a data-driven and benchmarking tool for evaluating the relative efficiency of production units with multiple outputs and inputs. Conventional DEA models are based on a production system by converting inputs to outputs using input-transformation-output processes. However, in some situations, it is inescapable to think of some assessment factors, referred to as dual-role factors, which can play simultaneously input and output roles in DEA. The observed data are often assumed to be precise although it needs to consider uncertainty as an inherent part of most real-world applications. Dealing with imprecise data is a perpetual challenge in DEA that can be treated by presenting the interval data. This paper develops an imprecise DEA approach with dual-role factors based on revised production possibility sets. The resulting models are a pair of mixed binary linear programming problems that yield the possible relative efficiencies in the form of intervals. In addition, a procedure is presented to assign the optimal designation to a dual-role factor and specify whether the dual-role factor is a nondiscretionary input or output. Given the interval efficiencies, the production units are categorized into the efficient and inefficient sets. Beyond the dichotomized classification, a practical ranking approach is also adopted to achieve incremental discrimination through evaluation analysis. Finally, an application to third-party reverse logistics providers is studied to illustrate the efficacy and applicability of the proposed approach

Optimal Personnel Deployment Strategy for Self-Perform Maintenance on Wind Farms

Wind turbine maintenance is a major cost factor and key determinant of wind farm productivity. Many companies outsource critical maintenance procedures while others perform these tasks in-house, referred to as self-perform maintenance. While expected to reduce time to profit on asset investment, self-perform requires an efficient personnel deployment strategy to implement. In this thesis, a partial solution to the optimization of wind turbine maintenance personnel team assignment is presented. A holistic framework is established, through analysis of historical work orders, for defining metrics that evaluate the performance of technicians. These metrics are further transformed into interpretable proficiency coefficients to be incorporated into an application of the team assignment problem. A case study of a large wind farm owner and operator is presented to illustrate the potential benefits and caveats of the proposed metrics and evaluation strategy. Additionally, the practicality of the data-derived metrics and proficiencies is illustrated. Key improvement strategies in data quality and metric aggregation are detailed, as well as discussion of a potential formulation of the task-to-team assignment problem, to be modeled through a standard maximin approach and solved through an integer programming technique