Fuzzy logic based leanness assessment and its decision support system

Manufacturing organisations have been witnessing a transition from mass manufacturing to lean manufacturing. Lean manufacturing is focused on the elimination of obvious wastes occurring in the manufacturing process, thereby enabling cost reduction. The quantification of leanness is one of the contemporary research agendas of lean manufacturing. This paper reports a study which is carried out to assess the leanness level of a manufacturing organisation. During this research study, a leanness measurement model has been designed. Then the leanness index has been computed. Since the manual computation is time consuming and error-prone, a computerised decision support system has been developed. This decision support system has been designated as FLBLA-DSS (decision support system for fuzzy logic based leanness assessment). FLBLA-DSS computes the fuzzy leanness index, Euclidean distance and identifies the weaker areas which need improvement. The developed DSS has been test implemented in an Indian modular switches manufacturing organisation.     

A reliable decision support system for fresh food supply chain management

The paper proposes a decision support system (DSS) for the supply chain of packaged fresh and highly perishable products. The DSS combines a unique tool for sales forecasting with order planning which includes an individual model selection system equipped with ARIMA, ARIMAX and transfer function forecasting model families, the latter two accounting for the impact of prices. Forecasting model parameters are chosen via two alternative tuning algorithms: a two-step statistical analysis, and a sequential parameter optimisation framework for automatic parameter tuning. The DSS selects the model to apply according to user-defined performance criteria. Then, it considers sales forecasting as a proxy of expected demand and uses it as input for a multi-objective optimisation algorithm that defines a set of non-dominated order proposals with respect to outdating, shortage, freshness of products and residual stock. A set of real data and a benchmark – based on the methods already in use – are employed to evaluate the performance of the proposed DSS. The analysis of different configurations shows that the DSS is suitable for the problem under investigation; in particular, the DSS ensures acceptable forecasting errors and proper computational effort, providing order plans with associated satisfactory performances.     

Intelligent decision support systems for nuclear power plants in Japan

This paper describes the MITI (Ministry of International Trade and Industry) projects for decision support systems for NPPs in Japan. Main attention is paid to the new project of an advanced man-machine system for nuclear power plants, of which a conceptual design was initiated in 1984. Some of the aspects of the design and the outline of the prototype system are discussed.     

Advanced functions and systems for operator support and plant management

The present paper describes developments by Siemens/KWU in the field of operator support, including plant management systems, which are currently planned and in some cases have already been introduced. The considerations involved in deciding to implement these developments are presented and the effects they will have on the daily work routine in the plant along with their advantages to nuclear power plant operating personnel are discussed in detail.     

Consensus oriented fuzzified decision support for oil spill contingency management

Studies on multi-group multi-criteria decision-making problems for oil spill contingency management are in their infancy. This paper presents a second-order fuzzy comprehensive evaluation (FCE) model to resolve decision-making problems in the area of contingency management after environmental disasters such as oil spills. To assess the performance of different oil combat strategies, second-order FCE allows for the utilization of lexical information, the consideration of ecological and socio-economic criteria and the involvement of a variety of stakeholders. On the other hand, the new approach can be validated by using internal and external checks, which refer to sensitivity tests regarding its internal setups and comparisons with other methods, respectively. Through a case study, the Pallas oil spill in the German Bight in 1998, it is demonstrated that this approach can help decision makers who search for an optimal strategy in multi-thread contingency problems and has a wider application potential in the field of integrated coastal zone management.     

Towards building an intelligent decision support system for project management

Management of large projects, especially the ones in which a major component of R&D is involved and those requiring knowledge from diverse specialised and sophisticated fields, may be classified as semi-structured problems. In these problems, there is some knowledge about the nature of the work involved, but there are also uncertainties associated with emerging technologies. In order to draw up a plan and schedule of activities of such a large and complex project, the project manager is faced with a host of complex decisions that he has to take, such as, when to start an activity, for how long the activity is likely to continue, etc. An Intelligent Decision Support System (IDSS) which aids the manager in decision making and drawing up a feasible schedule of activities while taking into consideration the constraints of resources and time, will have a considerable impact on the efficient management of the project. This report discusses the design of an IDSS that helps in project planning phase through the scheduling phase. The IDSS uses a new project scheduling tool, the Project Influence Graph (PIG).     

A real-time interval logic and its decision procedure

Real-Time Future Interval Logic is a temporal logic in which formulæ have a natural graphical representation, resembling timing diagrams. It is a dense real-time logic that is based on two simple temporal primitives:interval modalities for the purely qualitative part andduration predicates for the quantitative part. This paper describes the logic and gives a decision procedure for satisfiability by reduction to the emptiness problem for Timed Büchi Automata. This decision procedure forms the core of an automated proof-checker for the logic. The logic does not admit instantaneous states, and is invariant under real-time stuttering, properties that facilitate proof methods based on abstraction and refinement. The logic appears to be as strong as one can hope for without sacrificing elementary decidability. Two natural extensions of the logic, along lines suggested in the literature, lead to either non-elementariness or undecidability.     

A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems

A support vector machine (SVM) approach to the classification of transients in nuclear power plants is presented. SVM is a machine-learning algorithm that has been successfully used in pattern recognition for cluster analysis. In the present work, single- and multiclass SVM are combined into a hierarchical structure for distinguishing among transients in nuclear systems on the basis of measured data. An example of application of the approach is presented with respect to the classification of anomalies and malfunctions occurring in the feedwater system of a boiling water reactor. The data used in the example are provided by the HAMBO simulator of the Halden Reactor Project.     

Is there a need for hydrological modelling in decision support systems for nuclear emergencies

This paper discusses the role of hydrological modelling in decision support systems for nuclear emergencies. In particular, most recent developments such as, the radionuclide transport models integrated in to the decision support system RODOS will be explored. Recent progress in the implementation of physically-based distributed hydrological models for operational forecasting in national and supranational centres, may support a closer cooperation between national hydrological services and therefore, strengthen the use of hydrological and radiological models implemented in decision support systems.     

空分设备现场安装与焊接管理

介绍空分设备安装工程现场安装、焊接的特点与准备工作 ,侧重介绍冷箱焊接管理、塔内管道预制和安装焊接管理、上塔与冷凝蒸发器焊接管理、三大机组与仪表管焊接管理 ,以及焊接资料管理。因采取了相应措施 ,从严要求 ,使各个工程均为优良。     

A decision model for corrective maintenance management

Proper management of maintenance offers many companies significant potential for improving productivity and profitability. Traditional management thinking regards maintenance costs as accidental, rather than planned and controllable. Additionally, research in maintenance management has focused on preventive maintenance and has ignored corrective maintenance even though the latter is also considered to be a critical activity in industry. This study proposes a decision model that could assist in a comparative evaluation of alternative corrective maintenance policies. This decision model consists of a simulation model and economic analysis. The simulation model predicts inventory costs and delivery performance of a corrective maintenance policy in various production systems. Based on simulation results, an economic analysis, consisting of a net present value model and breakeven models, determines the economic value of alternative maintenance policies. A detailed example is offered to evaluate two particular correciive maintenance policies (machine redundancy and worker flexibility) although the decision model can be applied to other options. The results of the example demonstrate the decision model's capability to assist managers in selecting the best corrective maintenance policy.     

A survey of logic formalisms to support mishap analysis

Mishap investigations provide important information about adverse events and near miss incidents. They are intended to help avoid any recurrence of previous failures. Over time, they can also yield statistical information about incident frequencies that helps to detect patterns of failure and can validate risk assessments. However, the increasing complexity of many safety critical systems is posing new challenges for mishap analysis. Similarly, the recognition that many failures have complex, systemic causes has helped to widen the scope of many mishap investigations. These two factors have combined to pose new challenges for the analysis of adverse events. A new generation of formal and semi-formal techniques have been proposed to help investigators address these problems. We introduce the term ‘mishap logics’ to collectively describe these notations that might be applied to support the analysis of mishaps. The proponents of these notations have argued that they can be used to formally prove that certain events created the necessary and sufficient causes for a mishap to occur. These proofs can be used to reduce the bias that is often perceived to effect the interpretation of adverse events. Others have argued that one cannot use logic formalisms to prove causes in the same way that one might prove propositions or theorems. Such mechanisms cannot accurately capture the wealth of inductive, deductive and statistical forms of inference that investigators must use in their analysis of adverse events. This paper provides an overview of these mishap logics. It also identifies several additional classes of logic that might also be used to support mishap analysis.     

A knowledge-based decision support system for locating a manufacturing facility

This paper examines the design and implementation of a knowledge-based decision support system (KBDSS) in the facility location domain. The KBDSS for locating a manufacturing facility is intended to support facilities planners in improving the quality of strategic decision-making in facility location. A survey of past location studies was conducted to identify the major considerations of location analysts and to develop a hierarchy of factors for locating a manufacturing facility in the USA. Location evaluation is accomplished via a location model that is formulated as a linear additive multiattribute utility model. The microcomputer-based DSS developed integrates an object-oriented expert system with other modeling tools, such as a database management system and a C-based interactive graphical user interface. The system incorporates subjective, judgemental evaluations from management with objective, factual location data to recommend the best location for a new manufacturing facility. The system also makes transparent the justification for its recommendations by providing appropriate textual and graphical explanation facilities.     

A decision support system for selection of net-shape primary manufacturing processes

Developments in the capabilities of the manufacturing processes increased the number of processes that can produce a part within the requirements determined by its design and market research. The increased number of processes and unfamiliarity of manufacturing engineers to many new manufacturing processes forces the researchers to develop systematic process selection tools instead of depending on the accumulated human expertise only. In this paper, a net-shape primary manufacturing process selection decision support system (DSS), which is named PROSEL (PROcess SELection), is developed. The developed selection programme eliminates the unsuitable processes step by step by checking a part’s material, annual production quantity, specified shape, thickness and presents the most economical process as the most appropriate net-shape primary process after a final cost analysis. The developed DSS is written in Visual Studio and tested with a great deal of real-life examples. It can be concluded from the tests that the programme provides the same or better primary manufacturing process selection decisions than the practical usage, and it is a very useful support tool for net-shape primary process selections.     

A decision support system for the design of unpaced production lines

Consider a realistic unpaced-line problem: there is a set of discrete tasks, whose task-time distributions have diverse shapes. The line designer must (i) decide to which work station to assign each task, and (ii) specify the size of each buffer area in such a way that the configuration has the ‘best’ operating characteristics. This paper describes a decision support package that can efficiently generate the operating characteristics of the many alternative line designs which the line designer may want to try. We also show that the package can be conveniently used to extend earlier results in theoretical unpaced-line research.     

A strategic decision support system for logistics and supply chain network design

This paper aims to develop a strategic decision support system for logistics and supply chain network design of a multi-stage, multi-commodity, and multi-period distribution and transportation system. A mixed integer linear programming model is proposed to tackle the problem while minimizing the operating, transportation and handling cost through all tiers of the supply chain network. A genetic algorithm based method has been proposed to solve the problem in a large scale realistic environment. The efficacy of the developed strategic decision support model in achieving better utilization of network and resources to fulfil the customer demand is demonstrated using illustrative scenarios inspired from the real case of a logistics company.     

Assessment of fire vulnerability for nuclear power plant safety systems by combining fault-and success-oriented logic with physical models

The time behaviour of potential accident sequences may carry important information regarding nuclear power plant (NPP) safety operation and shutdown. In the case of external and environmental events, the ability of NPP components to operate correctly can be changed dramatically in a short time. In contrast to the failures caused by internal events, these two groups of undesirable events may lead to dynamic dependent failures among components of one or several systems. Such kinds of failure should be taken into account in the models of NPP behaviour. To evaluate how successfully the tasks of the safety systems will be carded out, logical models such as fault trees are usually used. The fault trees are not efficient at describing the short-term changes of the failure probabilities for system components. A method that has some advantages over the pure fault tree logic is proposed. The main features of the method are demonstrated by using examples.     

Procedure for the analysis of errors of commission during non-power modes of nuclear power plant operation

The continued, historical occurrence of human interactions which place nuclear power plants in a condition of potentially heightened risk is of increasing interest to regulators, utility management, and industry observers alike. These Errors of Commission (EOCs), as they are often called, lead to a variety of questions such as: ‘Can the event lead to a potentially dangerous condition such as core damage? By what failure mechanisms? With what frequency? What defences does the plant have to mitigate the event? Are these actions in the Probabilistic Risk Assessment (PRA) model of the plant?’. EOCs are often excluded from the bounds of a typical PRA model, yet they have the potential for being significant contributors to risk. This paper is the second of two describing procedures for the analysis of the potential for significant errors of commission. The first paper addressed operations at power while this paper describes the procedure for non-power operations. Each procedure describes a method for identifying the opportunities for error, identifying failures modes of functions, systems, or components that could arise from such errors (referred to in this paper as error expressions), and the identification of the most significant of these EOCs based on consideration of consequences, recovery potential, and likelihood.     

Uncertainty modeling and decision support

We first formulate the problem of decision making under uncertainty. The importance of the representation of our knowledge about the uncertainty in formulating a decision process is pointed out. We begin with a brief discussion of the case of probabilistic uncertainty. Next, in considerable detail, we discuss the case of decision making under ignorance. For this case the fundamental role of the attitude of the decision maker is noted and its subjective nature is emphasized. Next the case in which a Dempster–Shafer belief structure is used to model our knowledge of the uncertainty is considered. Here we also emphasize the subjective choices the decision maker must make in formulating a decision function. The case in which the uncertainty is represented by a fuzzy measure (monotonic set function) is then investigated. We then return to the Dempster–Shafer belief structure and show its relationship to the fuzzy measure. This relationship allows us to get a deeper understanding of the formulation the decision function used Dempster– Shafer framework. We discuss how this deeper understanding allows a decision analyst to better make the subjective choices needed in the formulation of the decision function.     

A decision support system for factory automation: a case study

Economic justification for factory automation (FA) and development of a decision support system (DSS) have recently been two independent ‘hot’ research topics. This paper integrates the two issues by developing the Decision Support System for Factory Automation (DSSFA) which follows generally accepted DSS framework and considers critical factors in justifying FA. Then, the DSSFA is applied to a real plant which is considering factory automation.