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The purpose of this paper is to introduce a decision support aid for deciding an overseas construction project (OCP) using an adaptive neuro fuzzy inference system (ANFIS).

This study presents an ANFIS approach as a decision support aid for assessment of OCPs. The processing data were derived from 110 simulation cases of OCPs. In total, 21 international factors observed from a Delphi survey were determined as assessment variables to examine the cases. The experts were involved to evaluate and judge whether the company should Go or Not Go for an OCP, based on the different parameter scenarios given. To measure the performance of the ANFIS model, root mean square error (RMSE) and coefficient of correlation (R) were employed.

The result shows that optimum ANFIS model indicating RMSE and R scores adequately near between 0 and 1, respectively, was obtained from parameter set of network algorithm with two input membership functions, Gaussian type of membership function and hybrid optimization method. When the model tested to nine real OCPs data, the result indicates 88.89 percent accurate.

The use of simulation cases as data set in development the model has several advantages. This technique can be replicated to generate other case scenarios which are not available publicly or limited in terms of quantity.

This study evidences that the developed ANFIS model can predict the decision satisfactorily. Therefore, it can help companies’ management to make preliminary assessment of an OCP.

The steel construction market has undergone gradual development in the past decades given its profound impacts on environment, economy and society. The purpose of this paper is to facilitate a better understanding of the major drivers and issues behind the market development of the steel construction industries around the world.

A three-step desktop research was conducted to select relevant research outputs published in the past 20 years. The research methodology in conducting these studies and their research trends were analyzed. Then the potential influencing factors for the market development of steel construction were identified through a content analysis of the selected studies.

A total of 59 articles were identified accordingly. These influencing factors were divided into five main themes: contextual, institutional, industrial, project-related and individual factors. In terms of the frequencies of these factors appeared in previous studies, “continuous development of standards, codes, and specifications” and “advance in product and process technology” were the top two driving forces in the market development of steel construction, while “cost issues” was the most frequently reported obstacle.

The study takes an initiative to establish a practical classification framework that can be dedicated to illuminating the critical issues or success factors affecting the development of the steel construction market. This framework can help policymakers, industry practitioners and researchers achieve sustaining success in steel construction in the developed, emerging and inactive markets.

The highest number of accidents in proportion to the employment rate is found in construction industry among all industries in Pakistan. The purpose of this paper is to identify and prioritize the contributory factors of accident causation that can significantly reduce the rate of accident in the construction industry.

In total, 32 contributory factors of accident causation were identified through a triangulation strategy comprising eight face-to-face semi-structured interviews with the academic and industry experts coupled with a comprehensive literature review. Delphi survey was then conducted among the four respondent groups (clients, contractors, safety official and academia) to prioritize these factors. A consensus was achieved among the respondent groups after conducting two rounds of Delphi survey. Finally, the results were validated using the technique of inter-rater agreement (IRA) analysis.

All the shortlisted accident causation factors were graded as “important” to “extremely important”. Moreover, a “moderate” to “strong level” agreement was developed among the respondent groups. The three most significant factors were highlighted as “poor enforcement of safety rules and regulations by the Government agencies”, “insufficient allocation of safety budget and safety incentives by the client”, and “insufficient provision of safety training and resources by the contractor”.

The findings will help the key stakeholders to prioritize their energies towards achieving zero accident in the construction industry. Moreover, addition of academic experts as one of the respondent groups will enhance the linkages between the academia and the industry practitioners.

Besides highlighting the underlying causes of construction accidents in Pakistan, a detailed methodology is presented in this study for the analysis and validation of the Delphi survey data, which can be extrapolated in other regions and industries for elements prioritization. The findings of the study can also be generalized for other developing countries having similar work environment. The results validation through the use of IRA analysis is an addition to the field of construction safety research. The study also authenticates the applicability of IRA analysis to assess the agreement level among the respondents.

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based micro-simulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

The digital revolution has brought many challenges and opportunities for the manufacturing firms. The impact of Industry 4.0 technology adoption on sustainable manufacturing and circular economy has been under-researched. This paper aims to review the latest articles in the area of Industry 4.0, sustainable manufacturing and circular economy and further developed a research framework showing key paths.

Qualitative research is performed in two stages. In the first stage, a review of the extant literature is performed to identify the barriers, drivers, challenges and opportunities. In the second stage, a research framework is proposed to integrate Industry 4.0 technology (big data analytics powered artificial intelligence) adoption, sustainable manufacturing and circular economy capabilities.

This research extends the knowledge base by providing a detailed review of Industry 4.0, sustainable manufacturing, and circular economy and proposes a research framework by integrating these three contemporary concepts in the context of supply chain management. Through an exploration of this integrative research framework, the authors propose a future research agenda and seven research propositions.

It is important to understand the interplay between institutional pressures, tangible resources and human skills for Industry 4.0 technology (big data analytics powered artificial intelligence) adoption. Industry 4.0 technology (big data analytics powered artificial intelligence) adoption can positively influence sustainable manufacturing and circular economy capabilities. Managers must also put more attention to sustainable manufacturing to develop circular economic capabilities.

Factory workers and the local communities generally suffer from various adverse effects resulting from the traditional manufacturing process. The quality of the environment is deteriorating to such an extent that people even staying miles away from the factory are also affected due to environmental pollution that is generated from factory operations. Hence, sustainable manufacturing is the only choice left to manufacturers that can help in the transition to a circular economy. The research framework can help firms to enhance circular economy capabilities.

This review paper contains the most updated work on Industry 4.0, sustainable manufacturing and circular economy. It also proposes a research framework to integrate these three concepts.

The purpose of this paper is to examine whether the underlying option impacts an insider’s propensity to purchase and sell before corporate announcements, the proportion of insiders’ trading after announcements relative to before announcements, and the insider’s profitability around corporate announcements.

The authors test whether the timing information and option have impacted on the tendency of insider trade, the percentage of all shares traded by insiders in the post-announcement to pre-announcement periods and the average cumulative abnormal stock returns during the pre-announcement period.

Insiders’ propensity to trade before announcements is higher for stocks without options listed than for stocks with traded options. This result is stronger for unscheduled announcements than for scheduled ones. The proportion of insiders’ trade volume after announcements relative to before announcements in stocks that have not options listed is higher than those in stocks with traded options. The positive relationship between the insiders’ signed volume and the informational content of corporate announcements is stronger in stocks without traded options than in stocks with options listed. Insider trades prior to unscheduled announcement are more profitable than those before scheduled ones.

The paper examines whether there is a difference between the effects of optioned stock and non-optioned stock. Roll et al. (2010) use the relative trading volume of options to stock ratio (O/S) to proxy for informed options trading activity. Future research could explore the impact of O/S. Moreover, the authors examine how insiders with private information use such information to trade in their own firms. Mehta et al. (2017) argue that insiders also use private information to facilitate trading (shadow trading) in linked firms, such as supply chain partners or competitors. Therefore, future research could consider the impact of shadow trading.

Since the insider’s propensity to buy before announcements in stocks without options listed is larger than in stocks with traded options and the relationship is stronger for unscheduled announcements than for scheduled ones, the efforts of regulators should focus on monitoring insider trading in stocks without options listed prior to unscheduled announcements.

First, Lei and Wang (2014) find that the increasing pattern of insider’s propensity to trade before unscheduled announcements is larger than that before scheduled announcements. The authors document the underlying option has impacted the insider’s propensity to purchase and sell, and the relationship is stronger for unscheduled announcements than for scheduled ones. Second, related studies show insider’s trading activity has shifted from periods before corporate announcements to periods after corporate announcements to decrease litigation risk. This paper find the underlying option has influenced the proportion of insiders’ trading after announcements relative to before announcements when the illegal insider trade-related penalties increase.

The purpose of this paper is to propose a stochastic optimisation model for integrating service and maintenance policies in order to solve the queuing problem and the cost of maintenance activities for public transport services, with a particular focus on urban ropeway system.

The authors adopt the following approaches: a discrete-event model that uses a set of interrelated queues for the formulation of the service problem using a cost-based expression; and a maintenance model consisting of preventive and corrective maintenance actions, which considers two different maintenance policies (periodic block-type and age-based).

The work shows that neither periodic block-type maintenance nor an age-based maintenance is necessarily the best maintenance strategy over a long system lifecycle; the optimal strategy must consider both policies.

The maintenance policies are then evaluated for their impact on the service and operation of the transport system. The authors conclude by applying the proposed optimisation model using an example concerning ropeway systems.

This is the first study to simultaneously consider maintenance policy and operational policy in an urban aerial ropeway system, taking up the problem of queuing with particular attention to the unique requirements public transport services.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

An assimilation and a synthesis of the major General Accounting Office (GAO) reports released on the Internal Revenue Service (IRS) for calendar year 2002 reveal a variety of tax administration problems. An analysis of the GAO reports also suggests that the IRS might be a contributor to the non-compliance problem. Additionally, Congresses and Administrations have not facilitated meaningful improvements.

While the GAO reports generally attempt to portray the ongoing modernization efforts within the IRS favorably, the weaknesses identified suggest that significant tax administration problems exist. This article presents a detailed analysis of the GAO overall report on the 2002-filing season and assesses GAO reports and testimonies that contain IRS in their titles.

The purpose of this paper is to evaluate naturally occurring phytic acid (PA) as a green-water-based lubricant.

Lubrication is studied using a ball-on-disk tribometer with silica glass against silicon nitride contact, and the friction coefficient and wear are measured in the boundary lubrication regimes.

Excellent lubrication performance was found by using PA aqueous solutions. After the running-in process, the sliding coefficient of friction could drop to as low as 0.01 with a quite low concentration of 7.5 × 10⁻⁴ M. The lubricating performance of PA solution could be further improved by increasing PA concentration. The work suggests that the excellent lubricity of PA in aqueous solution can be mostly contributed to its adsorption on the silica surface.

The paper shows that the natural products could be used as water-based lubricant additives.