Some considerations on the treatment of uncertainties in risk assessment for practical decision making

This paper discusses the challenges involved in the representation and treatment of uncertainties in risk assessment, taking the point of view of its use in support to decision making. Two main issues are addressed: (1) how to faithfully represent and express the knowledge available to best support the decision making and (2) how to best inform the decision maker. A general risk-uncertainty framework is presented which provides definitions and interpretations of the key concepts introduced. The framework covers probability theory as well as alternative representations of uncertainty, including interval probability, possibility and evidence theory.     

Practical solutions for multi-objective optimization: An application to system reliability design problems

For multiple-objective optimization problems, a common solution methodology is to determine a Pareto optimal set. Unfortunately, these sets are often large and can become difficult to comprehend and consider. Two methods are presented as practical approaches to reduce the size of the Pareto optimal set for multiple-objective system reliability design problems. The first method is a pseudo-ranking scheme that helps the decision maker select solutions that reflect his/her objective function priorities. In the second approach, we used data mining clustering techniques to group the data by using the k-means algorithm to find clusters of similar solutions. This provides the decision maker with just k general solutions to choose from. With this second method, from the clustered Pareto optimal set, we attempted to find solutions which are likely to be more relevant to the decision maker. These are solutions where a small improvement in one objective would lead to a large deterioration in at least one other objective. To demonstrate how these methods work, the well-known redundancy allocation problem was solved as a multiple objective problem by using the NSGA genetic algorithm to initially find the Pareto optimal solutions, and then, the two proposed methods are applied to prune the Pareto set.     

Using Interactive Multiobjective Optimization in Continuous Casting of Steel

We discuss some pros and cons of using different types of multiobjective optimization methods for demanding real-life problems like continuous casting of steel. In particular, we compare evolutionary approaches that are used for approximating the set of Pareto-optimal solutions to interactive methods where a decision maker actively takes part and can direct the solution process to such Pareto-optimal solutions that are interesting to her/him. Among the latter type of methods, we describe an interactive classification-based multiobjective optimization method: NIMBUS. NIMBUS converts the original objective functions together with preference information coming from the decision maker into scalar-valued optimization problems. These problems can be solved using any appropriate underlying solvers, like evolutionary algorithms. We also introduce an implementation of NIMBUS, called IND-NIMBUS, for solving demanding multiobjective optimization problems defined with different modelling and simulation tools. We apply NIMBUS and IND-NIMBUS in an optimal control problem related to the secondary cooling process in the continuous casting of steel. As an underlying solver we use a real-coded genetic algorithm. The aim in our problem is to find a control resulting with steel of the best possible quality, that is, minimizing the defects in the final product. Since the constraints describing technological and metallurgical requirements are so conflicting that they form an empty feasible set, we formulate the problem as a multiobjective optimization problem where constraint violations are minimized.     

INTEREST: a reference-point-based interactive procedure for stochastic multiobjective programming problems

This paper presents a reference point-based interactive algorithm, which has been specifically designed to deal with stochastic multiobjective programming problems. This algorithm combines the classical information used in this kind of methods, i.e. values that the decision maker regards as desirable for each objective, with information about the probabilities the decision maker wishes to accept. This novel aspect allows the method to fully take into account the randomness of the final outcome throughout the whole solution process. These two pieces of information have been introduced in an adapted achievement-scalarizing function, which assures each solution obtained to be probability efficient.     

过水围堰施工导流联合泄流管理决策支持系统

过水围堰施工导流联合泄流管理是典型的半结构化决策问题，为此设计开发了过水围堰施工导流联合泄流管理决策支持系统（CDDSS）。系统地介绍了CDDSS的结构框架、设计原则、开发思路、决策模式和基本功能，该系统由数据库子系统、模型库子系统、图形库子系统和人机界面四部分组成，此系统是建立在数学模型的基础上，综合利用系统科学、管理科学、施工导流的理论及方法的人机交互系统。工程实例分析表明，系统友好的用户界面帮助决策者/用户充分利用系统的功能做出有效的实时决策。     

Multi-objective scheduling problems: Determination of pruned Pareto sets

There are often multiple competing objectives for industrial scheduling and production planning problems. Two practical methods are presented to efficiently identify promising solutions from among a Pareto optimal set for multi-objective scheduling problems. Generally, multi-objective optimization problems can be solved by combining the objectives into a single objective using equivalent cost conversions, utility theory, etc., or by determination of a Pareto optimal set. Pareto optimal sets or representative subsets can be found by using a multi-objective genetic algorithm or by other means. Then, in practice, the decision maker ultimately has to select one solution from this set for system implementation. However, the Pareto optimal set is often large and cumbersome, making the post-Pareto analysis phase potentially difficult, especially as the number of objectives increase. Our research involves the post Pareto analysis phase, and two methods are presented to filter the Pareto optimal set to determine a subset of promising or desirable solutions. The first method is pruning using non-numerical objective function ranking preferences. The second approach involves pruning by using data clustering. The k-means algorithm is used to find clusters of similar solutions in the Pareto optimal set. The clustered data allows the decision maker to have just k general solutions from which to choose. These methods are general, and they are demonstrated using two multi-objective problems involving the scheduling of the bottleneck operation of a printed wiring board manufacturing line and a more general scheduling problem.     

A decision framework for maximising lean manufacturing performance

Since the development of the original value stream mapping (VSM) by Taichi Ohno at Toyota, a number of authors have suggested several additional VSM tools to understand and improve the value stream through waste reduction. A single best VSM tool, though effective in dealing with a certain waste type, becomes redundant as other wastes take centre stage and/or organisational priorities change. To overcome this, a decision framework based on a novel formulation of the integrated analytical hierarchy process (AHP) – pre-emptive goal programming (PGP) has been proposed. This framework not only guarantees accurate selection of an ideal VSM tool, based on the current organisation's priorities, but also aids the decision maker to arrive at the optimum implementation sequence of a chosen set of VSM tools to identify and reduce all wastes present in the system, thereby maximising organisational performance in the shortest timeframe.     

决策者具有重要性大小的群体冲突决策方法

讨论了利益冲突的多个决策者参与、决策者具有重要性程度不一的群冲突决策问题,给出了群冲突决策的一种解决方法.利用决策者之间的互相评价,给出了确定决策者重要性大小的方法;通过决策者之间的不断交互式决策过程,最后达到一致性意见,形成群体决策结果.给出的一个由四个决策者参与的群体冲突决策问题的实例,说明决策者重要性占优势时,最后的决策结果偏向于该决策者的结论.     

紫式决策分析架构模型及其实证研究

探讨工业工程在企业决策的定位,发展系统化的"紫式决策分析架构"和方法论,以辅助决策者有效地从问题定义与架构、目标和决策元素的分清、数据收集、信息整理、方案评估乃至决策支持的完整思维过程,建立决策问题的分析方式和解决方案,以找到最适合的优化决策,提供复杂管理问题数字化系统化之决策依据.具体说明紫式决策分析架构各阶段对应之步骤和方法,并以实证研究案例以检验效度.     

Decision Support Systems: A Perspective for Industrial Engineers

This article describes a methodology by which Decision Support Systems can be better understood by the Industrial Engineer. For years the IE has been at the forefront of technological change in the industrial arena, leading management and decision makers of all kinds in the use of appropriate tools as aids in the decision making process. One such tool has been Information Systems. IE's, as with the use of other tools, have been at the forefront in their planning and use, albeit not in their development. While the information systems industry has evolved from a transaction processing orientation to full-scale computer-based information systems orientation there seems to be a reticence on the IE's part in bringing the full power of the computer into the realm of the decision maker. Although attempts have been made over the years to define a decision support system for the decision maker, the implementation of that technology has been slow and extremely limited in scope. The authors of this paper propose that the slowness to develop adequate decision systems is more of a symptom of poor understanding of the decision process rather than a lack of technological skill. This paper reviews, in a general sense, the decision process, the evolution of information systems as currently understood by Industrial Engineers, and the current efforts in the area of Decision Support Systems. An attempt is then made to chart a course for the future: a course for IE's to follow and a course in which Decision Support Systems can be expected to follow.     

基于Arduino平台的交互原型设计研究

目的探索基于Arduino平台的交互原型设计和实现方法。方法首先简要介绍交互原型设计和Arduino平台。然后总结基于Arduino平台的交互原型设计和实现的一般流程,是软硬件结合的全过程,依次为需求分析和用户分析、系统设计(包括设计原理分析、硬件部分设计和软件部分设计)、系统实现(包括硬件连接、软件编程和包装设计与制作)、系统评价和迭代。最后通过两个案例详细阐述该流程,这两个案例不同于传统智能产品,更强调环境感知的创新设计。其中,VR交互手套采用低廉的硬件实现了三维空间鼠标,达到人与环境交互的目的;PM2.5感知器不仅将空气中的PM2.5可视化,而且还能与用户互动。结果总结了基于Arduino平台的交互原型设计的一般流程,通过两个案例将该流程细化实施。结论鉴于Arduino低成本、开放性、与传感器连接容易等特点,适合交互原型的设计,这里总结设计和开发的软硬件全过程,使读者可以初步掌握基于Arduino平台进行交互原型设计的基本能力。     

SETTING SAFETY LEADTIMES FOR PURCHASED COMPONENTS IN ASSEMBLY SYSTEMS

We consider the problem of setting appropriate safety leadtimes in an assembly system where all components are purchased and the only manufacturing operation is final assembly. First, we describe two formulations of this problem: (1) minimize total inventory carrying and tardiness costs and (2) minimize inventory carrying costs subject to a service constraint. Second, we discuss how these formulations sometimes fail to adequately provide a decision maker with sufficient information to establish leadtimes when applied independently but how they can be used together to provide a useful decision support system. Finally, we describe a procedure similar to ABC analysis that allows us to solve a much smaller related problem whose solution provides a good approximation to the solution of the original problem. We conclude the paper with numerical examples and a discussion of an actual implementation.     

An interactive memetic algorithm for production and manufacturing problems modelled as a multi-objective travelling salesman problem

In this paper, a preference-based, interactive memetic random-key genetic algorithm (PIMRKGA) is developed and used to find (weakly) Pareto optimal solutions to manufacturing and production problems that can be modelled as a symmetric multi-objective travelling salesman problem. Since there are a large number of solutions to these kinds of problems, to reduce the computational effort and to provide more desirable and meaningful solutions to the decision maker, this research focuses on using interactive input from the user to explore the most desirable parts of the efficient frontier instead of trying to reproduce the entire frontier. Here, users define their preferences by selecting among five classes of objective functions and by specifying weighting coefficients, bounds, and optional upper bounds on indifference tradeoffs. This structure is married with the memetic algorithm – a random-key genetic algorithm hybridised by local search. The resulting methodology is an iterative process that continues until the decision maker is satisfied with the solution. The paper concludes with case studies utilising different scenarios to illustrate possible manufacturing and production related implementations of the methodology.     

Software change decision support: a proposed framework and case study

Software change is a common and continuous process. It must be controlled in a rigorous way to avoid software quality degradation. Despite the existence of a number of research studies on software change control, software professionals often rely on their experience and intuition to decide whether to accept or reject software change requests. The research reported on here suggests a framework for software change decision support. The first part of the framework is a conceptual model which contains a comprehensive list of software attributes that a software change might affect. The literature has been surveyed in order to construct this conceptual model. The second part of the framework provides a formulation of the software change decision as a fuzzy multiple attribute group decision problem. Software analysts are required to linguistically assess the impact of every change implementation solution on every attribute. Then, the provided solution algorithm for the fuzzy multiple attribute decision problem has to be executed in order to come out with a ranking of the different implementation solutions. An implementation of the framework has been validated through a limited industrial application. This experiment proved that the proposed solution has an interesting potential.     

Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: Part 1: Overview of the IDAC Model

This is the first in a series of five papers that discuss the information, decision, and action in crew context (IDAC) model for human reliability analysis (HRA). An example application of this modeling technique is also discussed in this series. The model is developed to probabilistically predict the responses of the nuclear power plant control room-operating crew during an accident for use in probabilistic risk assessments. The operator response spectrum includes cognitive, emotional, and physical activities during the course of the accident. This paper provides an overview of the IDAC architecture and principles of implementation as a HRA model. IDAC includes a crew model of three types of operators: decision maker, action taker, and consultant. Within the crew context, each individual operator's behaviors are simulated through a cognitive model under the influence of a number of explicitly modeled performance-influencing factors.     

Survey of methods to visualize alternatives in multiple criteria decision making problems

When solving decision problems where multiple conflicting criteria are to be considered simultaneously, decision makers must compare several different alternatives and select the most preferred one. The task of comparing multidimensional vectors is very demanding for the decision maker without any support. Different graphical visualization tools can be used to support and help the decision maker in understanding similarities and differences between the alternatives and graphical illustration is a very important part of decision support systems that are used in solving multiple criteria decision making problems. The visualization task is by no means trivial because, on the one hand, the graphics must be easy to comprehend and not too much information should be lost but, on the other hand, no extra unintentional information should be included. In this paper, we survey and analyze different ways of visualizing a small set of discrete alternatives graphically in the context of multiple criteria decision making. Some of the ways discussed are widely used and some others deserve to be brought into a wider awareness. This survey provides a starting point for all those who deal with multiple criteria decision making problems and need information of what kind of visualization techniques could be put to use in order to support the decision maker better.     

Evaluating ERP implementation choices on the basis of customisation using fuzzy AHP

An enterprise resource planning (ERP) system is a software solution that puts together a package that allows information flow through various levels and departments of an organisation. Manufacturing enterprises have understood the importance of information systems within their organisations and have begun using them extensively. To achieve the right fit of the ERP system to the business organisation, customisation may be needed. However, customisation is seen as an issue in the application of an ERP. There are many attributes that can be considered for the selection of an ERP system. In this paper, we have applied fuzzy analytical hierarchy process (FAHP) to a framework to evaluate an ERP for implementation purposes. The framework provides different methods for customisation of ERP. With the use of FAHP, obtained results provide a better match in terms of understanding the problem and giving options that are a closer fit to the requirements rather than providing the ‘correct’ decision to be taken. This paper is aimed at helping managers to understand and select among the various customisation options available. The application of FAHP to the framework is demonstrated with opinions gathered from managers in various companies. These opinions are translated into numerical values and are compared to the problem as a whole.     

Interactive multiobjective optimization with NIMBUS for decision making under uncertainty

We propose an interactive method for decision making under uncertainty, where uncertainty is related to the lack of understanding about consequences of actions. Such situations are typical, for example, in design problems, where a decision maker has to make a decision about a design at a certain moment of time even though the actual consequences of this decision can be possibly seen only many years later. To overcome the difficulty of predicting future events when no probabilities of events are available, our method utilizes groupings of objectives or scenarios to capture different types of future events. Each scenario is modeled as a multiobjective optimization problem to represent different and conflicting objectives associated with the scenarios. We utilize the interactive classification-based multiobjective optimization method NIMBUS for assessing the relative optimality of the current solution in different scenarios. This information can be utilized when considering the next step of the overall solution process. Decision making is performed by giving special attention to individual scenarios. We demonstrate our method with an example in portfolio optimization.     

Equivalent Formulations of Inventory Control Problems

A general multi-item periodic-review inventory control problem is formulated in several ways, each reflecting a different point of view of the decision maker. These formulations are evaluated with respect to implementation criteria of: (i) technical difficulty in obtaining an optimal solution from the model, (ii) estimation effort required to find the values of the parameters in the model, (iii) effort needed to maintain the model continuously updated during the inventory management process. The most promising one is then compared with the conventional formulation, where the objective function is expressed as the sum of expected inventory holding and shortage costs. It is shown that both formulations lead to the same optimal solution under a certain budgetary constraint. This result offers an instrument for implementation improvements in inventory control systems.     

A hierarchical approach to warehouse design

The design of large complex systems, such as warehouses, requires multiple experts and analyses as well as methods to organise and integrate their knowledge. While there are many models for optimising individual aspects of warehouses, there is not, today, a comprehensive design methodology that incorporates and supports all of the design decisions and provides a method to effectively integrate the solutions to these subproblems into a complete warehouse system specification. In this research, we propose a hierarchical design decision support methodology based on decomposing the design problem into a set of subproblems and using a formal model of the system to integrate the solutions to these subproblems. The methodology enables a thorough search of the design space and the identification of many candidate designs for consideration by the design decision maker. The hierarchical design methodology is demonstrated with an example of designing a forward pick area.