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The bid/no-bid decision is critical to the success of construction contractors. The factors affecting the bid/no-bid decision are either qualitative or quantitative. Previous studies on modeling the bidding decision have not extensively focused on distinguishing qualitative and quantitative factors. Thus, the purpose of this paper is to improve the bidding decision in construction projects by developing tools that consider both qualitative and quantitative factors affecting the bidding decision.

This study proposes a mixed qualitative-quantitative approach to deal with both qualitative and quantitative factors. The mixed qualitative-quantitative approach is developed by combining a rule-based expert system and fuzzy-based expert system. The rule-based expert system is used to evaluate the project based on qualitative factors and the fuzzy expert system is used to evaluate the project based on the quantitative factors in order to reach the comprehensive bid/no-bid decision.

Three real bidding projects are used to investigate the applicability and functionality of the proposed mixed approach and are tested with experts of a construction company in Alberta, Canada. The results demonstrate that the mixed approach provides a more reliable, accurate and practical tool that can assist decision-makers involved in the bid/no-bid decision.

This study contributes theoretically to the body of knowledge by (1) proposing a novel approach capable of modeling all types of factors (either qualitative or quantitative) affecting the bidding decision, and (2) providing means to acquire, store and reuse expert knowledge. Practical contribution of this paper is to provide decision-makers with a comprehensive model that mimics the decision-making process and stores experts' knowledge in the form of rules. Therefore, the model reduces the administrative burden on the decision-makers, saves time and effort and reduces bias and human errors during the bidding process.

Many instructors have implemented decision-based learning (DBL) into their courses. This chapter is a careful qualitative analysis of the narratives in this book done by the editors. The author found common themes among all the narratives. The first theme was that many instructors discovered that they were missing conditional knowledge in their instruction. Second, the author found common issues around the complexity of designing an expert decision model (EDM). Included in this theme are stories about selecting problems and organizing the EDM, building the EDM around specific course learning outcomes, providing just-enough, just-in-time instruction, and introducing the decision model and software to students. Instructors also discovered that assessing the learning of students needed to go beyond traditional goals and began to include new goals related to conditional knowledge. Finally, the author describes the comments made by both faculty and students about the experience of using DBL. Several authors described the value of using DBL in the process of taking students from novice thinkers to expert thinkers. Many students expressed that they enjoyed the process that DBL presented to them and that they had a new level of confidence to be able to approach problems in the content area. Summaries and quotes from the chapters in this book are referenced by the authors’ names and the content areas they were teaching.

Discusses the development of expert systems (ES) as an improvement on computer‐aided valuation techniques (CAV), due to its allowing the modelling of complex non‐linear and qualitative relationships and processes, such as those which exist within the field of valuation. Explores valuation as a set of processes and details current technology with reference to the impact of CAVs in Australia. Discusses expert systems as the latest stage in the evolution of computer methods which support humans in decision making, outlines ES procedure and gives examples of current applications. Considers the application of expert systems to rating valuation and reviews the theory of ′information chunking.

Over the past two years, fraudulent financial reporting has become a major concern of both the Securities and Exchange Commission and investors. These concerns have been spurred by evidence that several high-profile companies such as Enron, Tyco, WorldCom, and HealthSouth have published false and/or misleading financial reports. Statement on Auditing Standards (SAS) No. 82 specifies that auditors have a responsibility to assess the likelihood of management fraud and identifies specific risk factors that should be considered when making that assessment. Apostolou et al. (2001b) examined how internal and external auditors rate the relative importance of these factors. This study extends Apostolou et al. (2001b) by examining how forensic experts at four Big 5 professional service firms assess the factors specified in SAS No. 82. These assessments produced two different models of relative importance: (a) a statistical model (produced by the Analytic Hierarchy Process); and (b) a subjective model (based on subjects’ assessment of the relative weights). These models are then used to assess the self-insight of and the degree of agreement among the forensic experts. The results indicate that forensic experts have a moderately high degree of self-insight. A moderate to high degree of consensus among experts’ judgments about the relative importance of fraud risk factors was noted.

To teach principles of information literacy effectively, an instructor must convey not only the tools and methods for locating information but also the conditions under which their use is most appropriate. Similarly, a key attribute of an information-literate student is not only the ability to find information but also to assess whether it is appropriate in the context of the need and information environment. Decision-based learning provides a method for helping instructors build these types of conditional knowledge within students. The process for leveraging the affordances of this method in a library instruction setting is outlined herein, including a discussion of considerations that impact course design. Process impacts on the instructor and student are also considered, and results are provided from a multi-semester study where this method was implemented in an academic environment.

The industrialized housing system (IHS) is regarded as an effective building philosophy based on off-site construction techniques to achieve rapid and cost-effective housing development. The purpose of this paper is to develop a multi-criteria decision-making support system (DMSS) model for the evaluation of housing systems to select the relevant decision factors and to identify the types and characteristics of suitable IHSs for application in a mass housing development.

A multi-criteria DMSS model with the analytical hierarchy process was designed. Based on the literature review and also the response of the ten experts’ interviews, 30 decision factors were identified for evaluation. In addition, 5 IHSs were considered as a case study for testing the model. Then, 30 professionals participated in a questionnaire survey conducted to evaluate the priority vector importance level of the decision factors and housing systems.

The result of the decision-making process showed that the top three decision factors are customer needs, supply chain and the construction industry. In addition, both precast concrete beam and slab blocks, as well as agro stone panels are identified as suitable housing systems. The systems have the characteristics of being lightweight, easy to produce and erect, and cost-effective, and they use local input resources and semi-skilled labor. The findings also revealed the potential and practicality of the model among multiple alternatives across multiple decision factors.

The study has faced the limitations of available professionals and experts who have rich experience in the application of IHSs. In addition, there were few types of alternative IHSs and limited practice of IHSs implementation in large-scale housing construction. These challenges caused limitations to the relevant data collection. In order to address these challenges, all the available experts from the different sectors of the construction industry with the experience of IHSs construction are invited to participate and the available alternative IHSs in the market are selected for evaluation.

The rational evaluation method used to determine the important decision factors and the general characteristics of the suitable housing systems can help housing developers and decision makers in developing countries to make informed and effective decisions.

The findings of the study help to address the challenge of lack of sufficient housing supply to the overwhelming housing demand that exists and identify the most important decision factors and suitable housing systems that can be applied for the rapid and decent large-scale housing developments at an affordable price.

This paper bridges the knowledge gaps that exist regarding the identification and evaluation of IHSs in Ethiopia. This study can help practitioners, housing developers, and decision makers to make informed and effective decisions regarding the evaluation and selection of IHSs.

Transitioning engineering knowledge to practice is critical for the success of a working engineer. Decision-Based learning (DBL) provides a path for developing this capability during the educational experience rather than relying on post-educational experiences in the field. In this chapter, DBL is framed in the context of an engineering educational experience, with a discussion of how certain attributes of engineering and DBL can complement each other. An overall class structure conducive to implementing DBL instruction methods is presented. A possible process for defining an expert decision model (EDM) for engineering topics is explored. This type of decision model can help communicate and teach students the small decisions that culminate to an expert conclusion on a topic, giving the student a clear way to justify analysis and design decisions. An EDM for selecting of a static failure theory with an accompanying learning module and representative assignment problems that accompany the module are presented. The implementation of a DBL approach in an engineering classroom is discussed, and reflections are given about the experience for both the instructor and the students.

Graduate students often come to statistics courses with varying levels of motivation and previous academic preparation. Within the statistics education literature, there is a growing consensus to guide instructors who want to help their students gain the requisite statistical knowledge so they can conduct their own research and report their results accurately. Recommendations from the literature include using real data, showing worked-out example problems, and providing immediate feedback to allow students to reflect on the correct and incorrect decisions they made in their analyses. This chapter describes the use of expert decision models (EDMs) in two graduate-level statistics courses – multiple regression and structural equation modeling. Decision-Based learning is an effective way to support graduate students’ developing thinking about statistics. In both courses, the students encounter the EDM through a series of assignments which guides students through the process of specifying a statistical model, running that model in Statistical Package for the Social Sciences or Mplus, and interpreting the results. These assignments use real datasets whenever possible and are designed to expose students to various issues they may experience in their research (missing data, violations of assumptions, etc.) and to illustrate how an expert would have adapted to those issues to complete the analysis. The EDM, with its just-in-time, just-enough instruction, helps students navigate these obstacles through guided practice and allows them to develop the conditional knowledge to handle issues that will arise as they carry out their own research.

The purpose of this paper is to take advantage of Internet of Things (IoT) for intelligent route programming of crowd emergency evacuation in metro station. It is a novel approach to ensure the crowd safety and reduce the casualties in the emergency context. An evacuation route programming model is constructed to select a suitable evacuation route and support the emergency decision maker of metro station.

The IoT technology is employed to collect and screen information, and to construct an expert decision model to support the metro station manager to make decision. As a feasible way to solve the multiple criteria decision-making problem, an improved multi-attributive border approximation area comparison (MABAC) approach is introduced.

The case study indicates that the model provides valuable suggestions for evacuation route programming and offers practical support for the design of an evacuation route guidance system. Moreover, IoT plays an important role in the process of intelligent route programming of crowd emergency evacuation in metro station. A library has similar structure and crowd characteristics of a metro station, thus the intelligent route programming approach can be applied to the library crowd evacuation.

The highlights of this paper are listed as followings: the accuracy and accessibility of the metro station’s real-time information are improved by integrating IoT technology with the intelligent route programming of crowd emergency evacuation. An improved MABAC approach is introduced to the expert support model. It promotes the applicability and reliability of decision making for emergency evacuation route selection in metro station. It is a novel way to combine the decision-making methods with practice.

Problem solving is a key skill for success in general chemistry, and yet it remains a persistent challenge for instructors to teach and for students to learn. One of the primary causes of this difficulty is that the majority of problem solving is actually deciding how to solve the problem – a task that experts do automatically and that befuddles novice students. The author created a decision-based learning (DBL) model for the general chemistry students to solve problems related to heat and enthalpy. The process of creating the decision model was enlightening to the author as an instructor. It revealed expert blind spots and made the author aware of places in the curriculum where the instruction was unclear or incomplete. When the author implemented the model with the students, it resulted in significant learning gains for students who worked with the model outside of class to practice problem solving. Additionally, the students reported improved attitudes toward problem solving such as decreased anxiety and being able to see the big picture. Since this initial foray into DBL, the author has incorporated explicit instruction for conditional knowledge construction in other parts of the curriculum. As the author works to make thinking more explicit, students are better able to master challenging problem-solving skills in chemistry.