A goal programming model to guide decision-making processes towards conservation consensuses

In this paper we propose a goal programming model that provides a consensual aggregated solution minimizing conflicts to guide multi-stakeholder decision-making processes and gen-erates information regarding stakeholder groups to be exploited for negotiation purposes. This model permits to quantify variations in conflicts when the relative contribution of each criteria changes and gives insight to negotiation strategies with application in conservation areas. A dataset of a case study in the Meseta Ibérica Biosphere Reserve (Portugal-Spain) was used to test and vali-date the model. Fifty people belonging to four groups (scientists, government, farmers and busi-nesspersons) assessed 20 management objectives in four dimensions: conservation, logistical sup-port, development, and governance. The results showed the highest conflicts to be found for fauna and flora, education, and guarantees objectives while the most conflictive groups were scientists and farmers. The proposed model substantially reduced the global and intergroup conflicts associ-ated to the same objectives, modelling the weights assigned to each objective in each dimension to find the most consensual/least conflictive solutions. This model can be a useful tool to improve complex decision-making processes in conservation areas with strong conflicts between stakehold-ers, such as transboundary biosphere reserves.This research was partially funded by FCT/MCTES through project grant UIDB/00690/2020info:eu-repo/semantics/publishedVersio

Multicriteria Decision Making in Sustainable Tourism and Low-Carbon Tourism Research: A Systematic Literature Review

Multicriteria Decision Making (MCDM) is increasingly being utilized as an analytical research tool for sectors that require decision-making with specific objectives and constraints, such as the tourism industry. Sustainable tourism, which examines the balance of numerous aspects, including stakeholders’ interests, is the critical feature propelling the increased usage of MCDM. This paper explores the use of Multicriteria Decision Making (MCDM) methods applied in studies of sustainable tourism and its derivative term, low-carbon tourism, using a systematic literature review (SLR) search from the Scopus database. The analysis has identified 189 relevant studies published between 1987 to April 2022. After selection, screening, and synthesizing processes, we selected 135 pertinent studies, which were analysed in general descriptive data, citation impacts, geographical categorization, categorization of the methodologies’ objectives, and possible trajectories of similar research in the future. We find that highly cited authors and articles are related to sustainable tourism indicators\u27 development and case studies. Furthermore, most relevant studies are concentrated in Asia and Europe rather than other regions. We also categorize the reviewed studies into six classifications depending on each method\u27s intended usage and further suggest four contexts for the studies’ future trajectory

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

Simplified models for multi-criteria decision analysis under uncertainty

Includes abstract.Includes bibliographical references.When facilitating decisions in which some performance evaluations are uncertain, a decision must be taken about how this uncertainty is to be modelled. This involves, in part, choosing an uncertainty format {a way of representing the possible outcomes that may occur. It seems reasonable to suggest {and is an aim of the thesis to show {that the choice of how uncertain quantities are represented will exert some influence over the decision-making process and the final decision taken. Many models exist for multi-criteria decision analysis (MCDA) under conditions of uncertainty; perhaps the most well-known are those based on multi-attribute utility theory [MAUT, e.g. 147], which uses probability distributions to represent uncertainty. The great strength of MAUT is its axiomatic foundation, but even in its simplest form its practical implementation is formidable, and although there are several practical applications of MAUT reported in the literature [e.g. 39, 270] the number is small relative to its theoretical standing. Practical applications often use simpler decision models to aid decision making under uncertainty, based on uncertainty formats that `simplify' the full probability distributions (e.g. using expected values, variances, quantiles, etc). The aim of this thesis is to identify decision models associated with these `simplified' uncertainty formats and to evaluate the potential usefulness of these models as decision aids for problems involving uncertainty. It is hoped that doing so provides some guidance to practitioners about the types of models that may be used for uncertain decision making. The performance of simplified models is evaluated using three distinct methodological approaches {computer simulation, `laboratory' choice experiments, and real-world applications of decision analysis {in the hope of providing an integrated assessment. Chapter 3 generates a number of hypothetical decision problems by simulation, and within each problem simulates the hypothetical application of MAUT and various simplified decision models. The findings allow one to assess how the simplification of MAUT models might impact results, but do not provide any general conclusions because they are based on hypothetical decision problems and cannot evaluate practical issues like ease-of-use or the ability to generate insight that are critical to good decision aid. Chapter 4 addresses some of these limitations by reporting an experimental study consisting of choice tasks presented to numerate but unfacilitated participants. Tasks involved subjects selecting one from a set of five alternatives with uncertain attribute evaluations, with the format used to represent uncertainty and the number of objectives for the choice varied as part of the experimental design. The study is limited by the focus on descriptive rather than real prescriptive decision making, but has implications for prescriptive decision making practice in that natural tendencies are identified which may need to be overcome in the course of a prescriptive analysis

Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management

Development of decision support systems towards supply chain performance appraisement

Purpose: The aim of this research is to develop various Decision Support Systems (DSS) towards supply chain (SC) performance appraisement as well as benchmarking. The purpose of this work is to understand multi-level (measures and metrics) performance appraisement index system to evaluate overall supply chain performance extent, monitor ongoing performance level and to identify ill-performing areas of the supply chain network. Design/methodology/approach: Fuzzy logic as well as grey theory has been explored in developing a variety of SC performance appraisement modules (evaluation index systems). Generalized fuzzy numbers, generalized intervalvalued fuzzy numbers theory have been utilized in order to tackle decision-makers’ linguistic evaluation information towards meaningful and logical interpretation of procedural hierarchy embedded to the said appraisement modules. Fuzzy-grey relation theory, MULTIMOORA method coupled with fuzzy logic as well as grey theory have also been adapted to facilitate overall SC performance assessment, performance benchmarking and related decision making. Findings: Supply chain performance index has been computed in terms of fuzzy as well as grey context, suggesting the present performance status of the said organizational supply chain. Ill-performing areas of the SC have been identified too. Fuzzy as well as grey based MULTIMOORA (MOORA: Multi-Objective Optimization by Ratio Analysis), fuzzy-grey relation analysis, thus adapted, appeared helpful in evaluating performance ranking order (and selecting the best) of various candidate alternatives (industries/enterprises) operating under similar supply chain architecture according to the ongoing SC performance. Empirical illustrations exhibited the fruitful application potential of the developed decision support tools. Practical implications: The decision support tools thus proposed may be proved fruitful for companies that are trying to identify key business performance measures for their supply chains. Ill-performing areas can easily be identified; companies can seek for possible means in order to improve those SC aspects so as to improve/enhance overall SC performance extent. Benchmarking may help in identifying best practices in relation to the SC which is performing as ideal (benchmarked practices). Best practices of the ideal organization need to be transmitted to the others. Companies can follow their peers in order to improve overall performance level of the entire supply chain. In view of this, the work reported in this dissertation may be proved as a good contributor for effective management of organizational SC. Research limitations: The methodology and presentation is conceptual, yet the tool can provide very useful interpretations for both researchers as well as management practitioners. Accessibility and availability of data are the main limitations affecting which model will be applied. Procedural steps towards implementing the said decision support tools have been demonstrated through empirical research. The decision support tools tools have neither been validated by practical case study nor have these been tested for assessing their reliability. Originality/value: This work articulates various approaches for supply chain performance evaluation considering multiple evaluation criteria (subjective evaluation indices), with a flexibility to modify and analyze using the available data sets collected from a group of experts (decision-makers). The approaches of performance evaluation index system are attempted due to structure and fuzzy (as well as grey) sets. The work is aimed at operational researchers, engineers and special managers

Relative efficiency measurement in the public sector with data envelopment analysis

PhDTraditional efficiency measures have two significant drawbacks. Firstly, they fail to recognise that output is the result of all inputs operating in combination; thus output per head is a misleading indicator of intrinsic labour productivity. Secondly, they have often been defined in terms of average levels of performance in least squares production functions. In practice, average performance norms may institutionalise some level of inefficiency. The first of these problems may be overcome in a total-factor view of efficiency. This implies the extension of traditional ratio measures to include all inputs and outputs simultaneously. The second requires the comparison of performance with frontier possibilities. Both of these improvements are embodied in Data Envelopment Analysis (DEA). Two applications of DEA are undertaken on U. K. public sector data. The first of these defines frontier efficiency in local education authorities (LEAs). It develops an 8 variable model with 3 outputs (based on exam pass rates) and 5 inputs. Four of the inputs are uncontrollable background variables allowing for differences in student catchment area; the fifth, teaching expenditure, is under LEA control and can be targeted. The results suggest that 44 authorities are best-practice and at the remainder spending per pupil could have been reduced by an average of 6.8%. These results are replicated on smaller clusters of LEAs to examine the sensitivity of DEA to the size of the performance comparison. The clustering procedure produces marked effects on targets, peer groups and the efficiency status of certain authorities. A second case study investigates the performance of a sample of 33 prisons with a high remand population. The model separately identifies the effects of remand prisoners on costs, and includes separate variables to reflect the levels of overcrowding and offences. In 1984/85 the combined budget of these prisons was overspent by 4.6% vis a vis best-practice costs. Using an alternative constant returns technology this overspend rises to 13.1%. Two aspects of DEA targets are explored. A model of Leibenstein's inert area suggests reasons for the persistence of inefficiency and hence that targets may be unattainable without coercion. Secondly, the literature has justified the recommendation of DEA targets in their being Pareto efficient. This interpretation is disputed and an alternative DEA-Dominance criterion is proposed as a more appropriate basis for targeting