Yannis A. Phillis

Sustainability: an ill-defined concept and its assessment using fuzzy logic

Abstract Sustainability is an inherently vague concept whose scientific definition and measurement still lack wide acceptance. Fuzzy logic is well suited to handle such a vague, uncertain, and polymorphous concept. In this paper, we develop a model called Sustainability Assessment by Fuzzy Evaluation (SAFE), which provides a mechanism for measuring development sustainability. Ecological (land, water, air, and biodiversity) and human (economical, social, educational, and political) inputs are treated individually and then combined with the aid of fuzzy logic to provide an overall measure. The output of the model is a degree (%) of sustainability of the system under examination (locality, state, country, etc.). The model is open to new inputs as reality and experience change, and it weighs all inputs according to their impact. A number of selected economies are tested and respective degrees of sustainability are derived and analyzed. It should be stressed that this method in itself is both a new definition and numerical assessment of sustainability. The SAFE model may become a useful aid to policy and decision-makers as they strive towards increasingly sustainable policies.

Manufacturing flexibility measurement: a fuzzy logic framework

Flexibility is recognized as an important feature in manufacturing. This paper suggests a knowledge-based methodology for the measurement of manufacturing flexibility. We claim that flexibility is an inherently vague notion and an essential requirement in its measurement is the involvement of human perception and belief. Nine different flexibility types are measured, while the overall flexibility is given as the combined effect of these types. Knowledge is represented via IF (fuzzy antecedents) THEN (fuzzy consequent) rules, which are used to model the functional dependencies between operational characteristics, such as setup time and cost, versatility, part variety, transfer speed, etc. The proposed scheme is illustrated through an example.

On the monotonicity of hierarchical sum--product fuzzy systems

Abstract Motivated by the authors’ previous work on the control of queueing systems, the assessment of sustainable development, and the measurement of material recyclability, this paper provides sufficient conditions on the parameters of hierarchical fuzzy systems under which the output of the system is monotonic with respect to its inputs. This property could be useful in designing multistage fuzzy inference systems and fuzzy controllers.

Controller design of systems with multiplicative noise

General linear continuous stochastic systems are considered with multiplicative noise in the control and state channels and stabilizing optimal inputs are synthesized for both time-varying and time-invariant situations. In the nonstationary case, a set of nonlinear matrix differential equations has to be solved. In the stationary problem, a set of nonlinear matrix algebraic equations provides the solution. The separation principle is not valid and both the state filtering and the control synthesis problem should be treated simultaneously.

Fuzzy assessment of machine flexibility

Manufacturing flexibility is a difficult and multifaceted concept that because of its inherent complexity and fuzziness is amenable to an artificial intelligence treatment. Fuzzy logic offers a suitable framework for measuring flexibility in its various aspects. This paper deals with the measurement of machine flexibility. When data are precise, this is done via a simple analytical formula. But if such data, and hence knowledge, are not precise, fuzzy-logic modeling should be employed by transforming the human expertise into IF-THEN rules and membership functions. An implementation of the interval-valued fuzzy-set approach, together with a max-min schema, provides the approximate inference mechanism for the computation of machine flexibility. This approach has the advantage of revealing second-order semantic uncertainty with the associated nonspecificity measure. The models are illustrated with a number of examples.

Trace bounds on the covariances of continuous-time systems with multiplicative noise

Matrix equations such as AP+PA/sup T/+FPF/sup T/+ Omega =0 and AQ+QA/sup T/+-QVQ+FPF/sup T/+ Omega =0, which arise in the estimation problem of systems with both additive and multiplicative noise, are treated. Trace bounds on the steady-state and error covariances P and Q are established, under complete and incomplete noise information. An example illustrates the usefulness of these bounds in determining the size of the estimation error. >

Fuzzy control of queueing systems with heterogeneous servers

We consider the problem of optimal control of queueing systems with heterogeneous servers in parallel. The system objective is to assign customers dynamically to idle servers based on the state of the system so as to minimize the average cost of holding customers. Three cases, either known in the literature or new, are studied in detail: queueing systems with server heterogeneity in-service rates, in-service functions, and both in-service rates and in-service functions. An approach is presented using fuzzy control to solve these problems. Simulation shows that this approach is efficient and promising, especially in cases where analytical solutions do not exist.

Sustainability Assessment of Nations and Related Decision Making Using Fuzzy Logic

This paper refines and extends in fundamental ways an existing model for the numerical assessment of sustainability called sustainability assessment by fuzzy evaluation (SAFE). SAFE, in its basic form, uses fuzzy logic to combine a large suite of basic indicators and then computes numerical values of sustainability for a number of composite indicators such as air, land, economy, health, etc. At a higher hierarchy it computes the sustainability of an ecological and a human component, and finally, it computes overall sustainability of a country or region. As state-of-the-art in fuzzy analysis has advanced, we are prompted to modify SAFE accordingly. The refined model uses the so-called Takagi-Sugeno-Kang inference scheme (TSK) which together with a few technical requirements guarantees monotonicity, i.e., an improvement of a basic indicator leads to an improvement of sustainability. Another refinement concerns the data inputs. To include the effects of past environmental pressures and development policies on the present state of sustainability, we use exponential smoothing to take account of the past with exponentially decaying weights. Finally, the model is now applied to all countries of the world for which data could be obtained and their corresponding sustainabilities are computed. Also, through sensitivity analysis, the most important indicators that affect sustainability are identified.

Assessment of Corporate Sustainability via Fuzzy Logic

Corporations interact with society and the physical and biological environment in ways that affect both sides. In this capacity, corporations play an important role in the sustainability of a region or country. Symmetrically, a corporation’s sustainability depends on the sustainability of its wider environment. In this paper, a multi-stage fuzzy reasoning model is presented to assess a corporation’s sustainability. The model has two fundamental components: human and ecological. The human component has up to four inputs: economic, political, knowledge, and welfare. The ecological component can also have up to four inputs: air, water, land, and biodiversity. Each of these eight components has a number of basic inputs suitable for evaluating a given corporation. The model can be used to assess a corporation’s sustainability, record its historical evolution, and compare the company to its direct competitors. Equally importantly, sensitivity analysis of the model reveals the most important basic indicators affecting corporate sustainability, identifying areas which managers and executives should place special attention. An application example based on three large international corporations in the same industry illustrates the usefulness of the model.

Discrete event modeling and optimization of unreliable production lines with random rates

We consider a serial production system with unreliable machines maintained by a limited number of repairmen, and finite storage between machines. Processing times may be random variables with exponential or gamma distributions, or deterministic. We develop a continuous-flow model for such a system utilizing simulation and analysis. Random processing times are approximated by sums of deterministic variables using a simple probabilistic technique. The model observes a limited number of events which are sufficient to determine system performance and mean buffer levels. By appropriately reducing the rates of starved and blocked machines and using analysis to compute the times of next event at each machine and buffer, discrete part computations are avoided. It is demonstrated that this approximate model is highly accurate and faster by a factor of 3 or more when compared to conventional simulators. The paper addresses also optimal repair allocation to maximize the expected throughput of the system. Two different approaches are proposed: perturbation analysis and experimental evaluation of various nonpreemptive rules for assigning a repairman to failed machines. >