An extended multi-objective mixed integer programming for water resources management through possibility theory

In this study, we develop an extended multi-objective mixed integer programming (EMOMIP) approach for water resources management under uncertainty, in which the parameters are fuzzy random variables while the decision variables are interval variables. Furthermore, some alternatives are considered to retrieve the difference between the quantities of promised water-allocation targets and the actual allocated water. Then, the proposed EMOMIP for the problem is solved by a new method using fuzzy random chance-constrained programming based on the idea of possibility theory. This method can satisfy both optimistic and pessimistic decision makers simultaneously. Finally, a real example is given to explain the proposed method.     

An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on a Fuzzy AHP and Fuzzy ARAS in the Presence of Vagueness

The conveyor system plays a vital role in improving the performance of flexible manufacturing cells (FMCs). The conveyor selection problem involves the evaluation of a set of potential alternatives based on qualitative and quantitative criteria. This paper presents an integrated multi-criteria decision making (MCDM) model of a fuzzy AHP (analytic hierarchy process) and fuzzy ARAS (additive ratio assessment) for conveyor evaluation and selection. In this model, linguistic terms represented as triangular fuzzy numbers are used to quantify experts’ uncertain assessments of alternatives with respect to the criteria. The fuzzy set is then integrated into the AHP to determine the weights of the criteria. Finally, a fuzzy ARAS is used to calculate the weights of the alternatives. To demonstrate the effectiveness of the proposed model, a case study is performed of a practical example, and the results obtained demonstrate practical potential for the implementation of FMCs.     

Modeling process and material alternatives in life cycle assessments

Background, Aims and Scope  Although LCA is frequently used in product comparison, many practitioners are interested in identifying and assessing improvements within a life cycle. Thus, the goals of this work are to provide guidelines for scenario formulation for process and material alternatives within a life cycle inventory and to evaluate the usefulness of decision tree and matrix computational structures in the assessment of material and process alternatives. We assume that if the analysis goal is to guide the selection among alternatives towards reduced life cycle environmental impacts, then the analysis should estimate the inventory results in a manner that: (1) reveals the optimal set of processes with respect to minimization of each impact of interest, and (2) minimizes and organizes computational and data collection needs. Methods  A sample industrial system is used to reveal the complexities of scenario formulation for process and material alternatives in an LCI. The system includes 4 processes, each executable in 2 different ways, as well as 1 process able to use 2 different materials interchangeably. We formulate and evaluate scenarios for this system using three different methods and find advantages and disadvantages with each. First, the single branch decision tree method stays true to the typical construction of decision trees such that each branch of the tree represents a single scenario. Next, the process flow decision tree method strays from the typical construction of decision trees by following the process flow of the product system, such that multiple branches are needed to represent a single scenario. In the final method, disaggregating the demand vector, each scenario is represented by separate vectors which are combined into a matrix to allow the simultaneous solution of the inventory problem for all scenarios. Results  For both decision tree and matrix methods, scenario formulation, data collection, and scenario analysis are facilitated in two ways. First, process alternatives that cannot actually be chosen should be modeled as sub-inventories (or as a complete LCI within an LCI). Second, material alternatives (e.g., a choice between structural materials) must be maintained within the analysis to avoid the creation of artificial multi-functional processes. Further, in the same manner that decision trees can be used to estimate ‘expected value’ (the sum of the probability of each scenario multiplied by its ‘value’), we find that expected inventory and impact results can be defined for both decision tree and matrix methods. Discussion  For scenario formulation, naming scenarios in a way that differentiate them from other scenarios is complex and important in the continuing development of LCI data for use in databases or LCA software. In the formulation and assessment of scenarios, decision tree methods offer some level of visual appeal and the potential for using commercially available software/ traditional decision tree solution constructs for estimating expected values (for relatively small or highly aggregated product systems). However, solving decision tree systems requires the use of sequential process scaling which is difficult to formalize with mathematical notation. In contrast, preparation of a demand matrix does not require use of the sequential method to solve the inventory problem but requires careful scenario tracking efforts. Conclusions  Here, we recognize that improvements can be made within a product system. This recognition supports the greater use of LCA in supply chain formation and product research, development, and design. We further conclude that although both decision tree and matrix methods are formulated herein to reveal optimal life cycle scenarios, the use of demand matrices is preferred in the preparation of a formal mathematical construct. Further, for both methods, data collection and assessment are facilitated by the use of sub-inventories (or as a complete LCI within an LCI) for process alternatives and the full consideration of material alternatives to avoid the creation of artificial multi-functional processes. Recommendations and Perspectives  The methods described here are used in the assessment of forest management alternatives and are being further developed to form national commodity models considering technology alternatives, national production mixes and imports, and point-to-point transportation models. ESS-Submission Editor: Thomas Gloria, PhD (t.gloria@fivewinds.com)     

Practical Decision Methods for Watershed Management

Why are formal statistical methods for risk-based decision-making so seldom used in the practice of watershed management? I contend that complex formal methods, while internally consistent, are often inappropriate to real world decision-making. The primary purpose of risk analysis is to support risk management, and decision methods need to be effective not just in evaluating risk, but also in communicating risk among stakeholders and decision makers. Useful methods must be not only correct, but also readily communicable. Many formal risk-based decision methods have real obstacles to practical application in one of the following areas: (1) many important components of risk that matter to stakeholders are difficult to express in quantitative terms, and any method which turns “fuzzy” information and subjective opinion into hard numbers is prone to be regarded with suspicion; (2) methods which are not understandable and convincing to decision makers have little practical value; (3) a complex formal analysis will be seen as misguided or irrelevant if it does not represent the full spectrum of management goals. This paper compares the process of watershed management with the process of ecological risk assessment, highlighting similarities and key differences. A practical decision method which balances quantitative rigor with ability to communicate to and forge consensus among stakeholders is then outlined with reference to a successful case study.     

Minimizing Environmental Risks Using Fuzzy TOPSIS: Location Selection for the ITU Faculty of Management

ABSTRACT

Environmental risk is a measure of potential threats to the environment and combines the probability that events will cause or lead to degradation of the environment and the severity of that degradation. Evaluation of environmental risk factors requires linguistic terms to be used rather than exact numerical assignments. The fuzzy set theory provides a useful tool for converting linguistic terms into numerical evaluations. In this article, we will try to select the best location for the Faculty of Management of the Istanbul Technical University taking care of the predetermined environmental criteria by using fuzzy TOPSIS. The decision matrix is composed of linguistic evaluations for the alternatives with respect to the environmental risk criteria. The Macka Campus area has been selected after the multicriteria evaluation. A sensitivity analysis is also realized to see if the minor changes in criteria weights cause any change in the ranking of alternatives. Unless the importance of the criteria rock or soil structure, remoteness to health facilities, transportation availability, and transportation costs is changed to be from low to extremely low importance, the Macka Campus is still the selected alternative.     

Risk-Cost Trade Off Considerations for Contaminated Sediment Disposal

Delays in dredging and inability to dredge the nation's harbors, due to the presence of contaminated sediments and the lack of environmentally acceptable disposal sites are interfering with shipping activities and hampering trade growth. The United States Government is committed to provide continuing support to the port industry's goals for enhancing economic growth while protecting, conserving and restoring natural resources within coastal aquatic lands. The government's commitment has resulted in the articulation of a national dredging policy in the Action Plan for Improvement of the Dredging Process in the United States. This national challenge calls for a systematic and consistent decision making approach to dredging and disposal including contaminated sediment management. In building an effective decision mak ing framework for costs, risk reduction and potential beneficial uses of the disposal material must be considered in identifying and evaluating environmentally acceptable and cost-effective disposal alternatives. A conceptual framework for applying a risk-cost trade off approach in making decisions regarding contaminated sediment disposal is presented and applied to a hypothetical disposal scenario involving three alternatives: deepwater confined disposal, nearshore fill or capping and, upland disposal. The approach entails the performance of sequential evaluations consisting of risk analysis, estimation of costs, integration of the results into a computational framework for trade-off analysis, and the application of decision analytical tools to build consensus among stakeholders and the general public in selecting a preferred alternative.     

An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation

Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.     

Clinical evaluation of benign prostatic hyperplasia

Benign prostatic hyperplasia (BPH) is the most common neoplastic condition afflicting men and constitutes a major factor impacting male health. Clinical evaluation to assess the presence and degree of voiding dysfunction and/or the role of BPH in its presence has an increasingly broad spectrum of treatment goals. The goals of the evaluation of such men are to identify the patient's voiding or, more appropriately, urinary tract problems, both symptomatic and physiologic; to establish the etiologic role of BPH in these problems; to evaluate the necessity for and probability of success and risks of various therapeutic approaches; and to present the results of these assessments to the patient so he can make an informed decision about management recommendations and available alternatives.     

Decision errors in evaluating tipping points for ecosystem resilience

When an ecosystem reaches tipping points for selected indicators, resilience to further changes in external drivers can decrease, regime shifts can occur that diminish the capacity of the ecosystem to provide ecosystem services, and the ecosystem is more vulnerable to collapse. Evaluating tipping points for resilience using crisp decision rules can result in decision errors about whether or not resilience has been compromised. The source and nature of those errors are described and a fuzzy decision rule is proposed for evaluating resilience. Decision errors are evaluated for four cases. Cases 1 through 3 (or case 4) derive conditions for evaluating decision errors when there is a single (or multiple) indicator(s). The primary sources of decision errors for the four cases are discrepancies between measured (or established) and true values of the indicators (or tipping points) and using a crisp decision rule to reach conclusions about whether or not resilience has been compromised. A fuzzy decision rule, based on fuzzy TOPSIS, is proposed that evaluates the extent to which an ecosystem is resilient. Although crisp decision rules provide unambiguous conclusions about resilience, those conclusions can be faulty, particularly when measured indicators and established tipping points deviate substantially from their true values. In contrast, the conclusions from the fuzzy decision rule are less susceptible to the decision errors and, hence, faulty decisions.     

Performance management in flexible manufacturing systems

This article treats several performance management decision problems in flexible manufacturing systems (FMSs). This work differs from a number of other studies in that we allow the processing rates at the machines to be varied, and the system has to meet a given throughput goal per unit time. The managerial decision options modeled here include part routing and allocation of tasks to machines, work-in-progress (WIP) levels, capacity expansions, tool-type selection, the setting of throughput goals, and multiperiod production planning. We discuss and explain the insights and implications, partly nonintuitive, gained from our investigations. Finally, extensive numerical evaluations are included to illustrate the economic and performance impact of the various performance management alternatives. These results demonstrate that substantial economic benefits can be achieved by careful tuning of the FMS operational parameters.     

On the use of weighting in LCA: translating decision makers’ preferences into weights via linear programming

Purpose

The main goal of any life cycle assessment (LCA) study is to identify solutions leading to environmental savings. In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these alternatives using an aggregated indicator defined by attaching weights to impacts. We address here the inverse problem. That is, given an alternative, we aim to determine the weights for which that solution becomes optimal.

Methods

We propose a method based on linear programming (LP) that determines, for a given alternative, the ranges within which the weights attached to a set of impact metrics must lie so that when a weighting combination of these impacts is optimized, the alternative can be optimal, while if the weights fall outside this range, it is guaranteed that the solution will be suboptimal. A large weight value implies that the corresponding LCA impact is given more importance, while a low value implies the converse. Furthermore, we provide a rigorous mathematical analysis on the implications of using weighting schemes in LCA, showing that this practice guides decision-making towards the adoption of some specific alternatives (those lying on the convex envelope of the resulting trade-off curve).

Results and discussion

A case study based on the design of hydrogen infrastructures is taken as a test bed to illustrate the capabilities of the approach presented. Given are a set of production and storage technologies available to produce and deliver hydrogen, a final demand, and cost and environmental data. A set of designs, each achieving a unique combination of cost and LCA impact, is considered. For each of them, we calculate the minimum and maximum weight to be given to every LCA impact so that the alternative can be optimal among all the candidate designs. Numerical results show that solutions with lower impact are selected when decision makers are willing to pay larger monetary penalties for the environmental damage caused.

Conclusions

LP can be used in LCA to translate the decision makers’ preferences into weights. This information is rather valuable, particularly when these weights represent economic penalties, as it allows screening and ranking alternatives on the basis of a common economic basis. Our framework is aimed at facilitating decision making in LCA studies and defines a general framework for comparing alternatives that show different performance in a wide variety of impact metrics.     

Foraging theory provides a useful framework for livestock predation management

A societal shift toward plant dominant diets and a reduction in livestock rearing could have broad social, environmental and conservation benefits. Livestock husbandry, however, has a wealthy cultural history, strong support and high consumer demand. It is therefore likely to continue as a major land use and conservation issue for predators. From a producer’s perspective, the primary goals of livestock protection are maximising, or at least maintaining, production by minimising losses and mitigating detriment to stock welfare. Lethal removal of predators remains a commonplace solution. Such management measures are questionable as they raise animal welfare and conservation concerns, risk inhibiting ecological processes, are often expensive, and in some circumstances, exacerbate livestock predation problems. Non-lethal alternatives can facilitate co-existence between livestock farmers and predators, ideally reducing the ecological impact of pastoralism and achieving conservation goals. The need for rigorous study of non-lethal approaches has however been recently highlighted. Tools and methods involved in livestock protection, as well as the theoretical basis of how we perceive and manage the problem, require deeper consideration. Non-lethal approaches require knowledgeable implementation and an effective decision making system is a prerequisite for successful practice. Livestock predation and its prevention are fundamentally influenced by the underlying principles of foraging ecology and risk theory. We propose that manipulating elements of Brown’s (1988) quitting harvest rate model provides a useful conceptual framework for reducing livestock predation and encouraging coexistence.     

Making Decisions on the Resources for Cancer Control

We aim at modelling the strategic decision making process in case of devoting resources to a governmental cancer control program. We use a model based on the theory of Analytic Hierarchy Process. In this article we describe the characteristic features of such a decision making process and reveal the complexity of the problem underlying the decisions. A second article will present and discuss the results from the application of the AHP model. Interventions which are capable of decreasing the burden of cancer in a society need strategic approach. Decisions on interventions seem inevitable to be based on and balance between the priorities and the available resources. There is not much doubt about it that the reason for setting the priorities arises on the one hand from the scarcity of resources. On the other hand, priorities evolve on other bases, and are supposed to "guide" health policy makers devoting the scarce resources. In general, a strategic mode of thought has been based on assumptions, which, in case of cancer control enhance the necessity to assess information on cancer and cancer patients, and to understand the factors contributing towards better health. The capabilities of the NCCP achieving its aims by preventing the development of cancer diseases (primary prevention), by making use of the means of early detection and appropriate therapy (secondary prevention), and by providing modern (comprehensive) tertiary prevention are inevitably affected by the priorities. Health policy should assume a responsibility for enforcing certain priorities and should be aware of the long-term interest of the population. To solve the problem we restrict the model to a simple three level one, representing the goals, the criteria, and the alternatives of the resource allocation. We determine "decreasing the burden of cancer" as the overall goal. "Distributive justice" "cost-effectiveness", "human rights", "evidences", and "standpoints of a community" serve as criteria, while "primary prevention", "early detection and therapy, both belonging to the secondary prevention", "tertiary prevention", "research", and "education" form the alternatives.     

Neuro-fuzzy decision trees

Fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are often criticized to result in poor learning accuracy. In this paper, we propose Neuro-Fuzzy Decision Trees (N-FDTs); a fuzzy decision tree structure with neural like parameter adaptation strategy. In the forward cycle, we construct fuzzy decision trees using any of the standard induction algorithms like fuzzy ID3. In the feedback cycle, parameters of fuzzy decision trees have been adapted using stochastic gradient descent algorithm by traversing back from leaf to root nodes. With this strategy, during the parameter adaptation stage, we keep the hierarchical structure of fuzzy decision trees intact. The proposed approach of applying backpropagation algorithm directly on the structure of fuzzy decision trees improves its learning accuracy without compromising the comprehensibility (interpretability). The proposed methodology has been validated using computational experiments on real-world datasets.     

A fuzzy-based risk assessment model for evaluations of hazards with a real-case study

Abstract

The effectiveness of the traditional risk analysis approach is enhanced by the integration of the fuzzy logic and Multi-Criteria Decision Making (MCDM) methods. In fact, human decisions are ambiguous and blurred and do not fit to express with absolute numerical values. For this reason, it is more realistic to use verbal variables in modeling human decisions. In this paper, a new fuzzy based hazard evaluation approach is proposed to deal with the risk assessment process. The proposed methodology consists on MCDM with a fuzzy system which includes a hybrid structure consists the Pythagorean Fuzzy Analytic Hierarchy Process (PFAHP) method with cosine similarity, and also Neutrosophic Analytic Hierarchy Process (NFAHP) to support facing of uncertainty in the risk assessment process for asphalt production, laying and coating services which are important and should be examined in terms of occupational health and safety. To the best of our knowledge, this study is the first to propose facing uncertainty in the hazard evaluations and risk assessment for asphalt production, laying, and coating services. As an outcome of the analysis by the proposed method, according to PFAHP and NFAHP methodologies the criterions “manometer size” and “calibration” are found to be most critical factors, respectively.     

人工湿地生态系统服务综合评价研究

人工湿地作为一种高生产力的生态系统能为人类提供多种服务。但多年来对人工湿地的研究主要集中在其净化机理上, 而一直缺乏系统、综合的人工湿地生态系统服务评价体系。基于层次分析法和模糊隶属函数法等数学理论, 首次建立了对人工湿地生态系统服务进行综合评价的方法——人工湿地生态系统服务综合指数。开展了以北京奥林匹克森林公园人工湿地为例生态系统服务综合评价。其生态系统服务综合指数的得分为0.7848 分。该得分较理想, 说明北京奥林匹克森林公园人工湿地具有可观的生态系统服务价值。它能在净化水质、提供栖息地、有机质生产、微气候调节、休闲娱乐和科研教育等诸多方面提供良好服务。对人工湿地生态系统服务进行综合评价, 有利于比较不同人工湿地或同一人工湿地不同时期服务质量的优劣, 从而为人工湿地的研发、设计、建设、运行和管理提供指导。       

A fuzzy logic controller.

This paper describes a fuzzy sets method which is very useful for handling uncertainties and essential for knowledge acquisition of a human expert. Kinetics of a reactor is often complex and not trivial to describe by mathematical equations. Reactor control by traditional control technology is therefore difficult. A novel technology is presented. In the following a fuzzy inference (approximate reasoning) is used for decision making in analogy to human thinking, facilitating a more sophisticated control. Readers of this paper do not need any advanced mathematics beyond the four basic operations in arithmetic (+, -, x, divided by) and using the maximum and minimum values. This fuzzy inference is introduced to construct a fuzzy logic controller which is suitable for a nonlinear, multivariable and time variant system applied to a bioreactor.     

Comprehensive Realism's Weight-of-Evidence Based Distributional Dose-Response Characterization

Challenges to low-dose linearity and other default assumptions in cancer risk assessment and the limitations associated with NOAELs, LOAELs, and constant uncertainty factor values in the evaluation of noncancer health effects have stimulated the continued evolution of risk assessment methodologies. The increasing need for more realistic estimates of the dose-response relationship, better uncertainty characterization, and greater utilization of cost-benefit analyses have also contributed to this evolution. “Comprehensive Realism” is an emerging quantitative weight-of-evidence based risk assessment methodology for both cancer and noncancer health effects which utilizes probability distributions and decision analysis techniques to reflect more of the relevant human exposure data, more of the available and pertinent human and animal dose-response data, and the current state of knowledge about the relative plausibility of alternative dose-response analyses. A tree (like a decision tree and a probability tree) is used to decompose the dose-response assessment into component factors, to provide a structure for explicitly considering multiple alternatives for each factor, and to explicitly incorporate the current state of knowledge about the relative plausibility of these alternatives. Groundbreaking work has demonstrated the feasibility of weight-of-evidence based distributional characterizations, and provided initial examples. Computer software implementations are also available.