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Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.

The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.

In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.

This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

The purpose of this paper is to provide a better understanding of the driving forces and structural changes of China as a market provider for Korea. This paper gives the answers for the following questions: How do China’s final demands trigger the growth of its imports from Korea? And what’s the impact of China’s final demands on the import in different industries?

Based on the Multi-Regional Input-Output model and World Input-Output Table database, this paper constructs the non-competitive imports input-output (IO) table of China to Korea. According to this table, we can calculate the induced imports coefficient and comprehensive induced import coefficients of China’s four final demands for imports from Korea in the 56 industries in China.

Among the four driving forces, the strongest one is changes in inventories and valuables. The impact of final consumption expenditure and fixed capital formation is much lower than that of changes in inventories and valuables, but they have a broader impact for the 56 industries. This paper finds out the China’s import induction of the final demands to Korea peaked in 2005 and 2010 and decreased greatly in 2014, so the position of China as market provider for Korea will no longer rise substantially, contrarily it will be in a steady state.

First, this paper constructs the non-competitive IO table to analyze the market provider issues between two countries and provides practical ways and methods for studies on the issues of imports and market provider. Second, this paper investigates the different roles of four final demands on driving force of China as market provider for Korea and the structural changes of China as a market provider for Korea among 56 industries from 2000 to 2014.

Construction projects managers try their best for the project to go according to the plans. They always attempt to complete the projects on time and consistent with the predetermined budgets. Amid so many problems in project planning, the most critical and well-known problem is the Resource-Constrained Project Scheduling Problem (RCPSP). The purpose of this paper is to solve RCPSP using hybrid algorithm ICA/PSO.

Due to the existence of various forms for scheduling the problem and also the diversity of constraints and objective functions, myriad of research studies have been conducted in this realm of study. Since most of these problems are NP-hard ones, heuristic and meta-heuristic methods are used for solving these problems. In this research, a novel hybrid method which is composed of meta-heuristic methods of particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) has been used to solve RCPSP. Finally, a railway project has been examined for RCPS Problem in a real-world situation.

According to the results of the case study, ICA/PSO algorithm has better results than ICAs and PSO individually.

ICA/PSO algorithm could be used for solving problems in a multi-mode situation of activities or considering more constraints on the resources, such as the existence of non-renewable resources and renewable. Based on the case study in construction project, ICA/PSO algorithm has a better solution than PSO and ICA.

In this study, by combining PSO and ICA algorithms and creating a new hybrid algorithm, better solutions have been achieved in RCPSP. In order to validate the method, standard problems available in PSPLib library were used.

This paper aims to develop an algorithm to study the impact of dynamic resource disruption on project makespan and provide a suitable resource disruption ratio for various complex industrial and emergency projects.

This paper addresses the RCPSP in dynamic environments, which assumes resources will be disrupted randomly, that is, the information about resource disruption is not known in advance. To this end, a reactive scheduling model is proposed for the case of random dynamic disruptions of resources. To solve the reactive scheduling model, a hybrid genetic algorithm with a variable neighborhood search is proposed.

The results obtained on the PSLIB instances prove the performance advantage of the algorithm; through sensitivity analysis, it can be obtained, the project makespan increases exponentially as the number of disruptions increase. Furthermore, if more than 50% of the project's resources are randomly disrupted, the project makespan will be significantly impacted.

The paper focuses on the impact of dynamic resource disruptions on project makespan. Few studies have considered stochastic, dynamic resource uncertainty. In addition, this research proposes a reasonable scheduling algorithm for the research problem, and the conclusions drawn from the research provide decision support for project managers.

In the present knowledge economy, intellectual capital (IC) is regarded as one of the significant determinants of efficiency, profitability, and ultimately value of a firm. This chapter empirically investigates the ramifications of the IC on the level of efficiency of the firm. In addition, exploration of the changing dynamics in the relationship between IC and firm level efficiency in the face of global economic crisis is of special interest of this chapter. In attaining the objectives of the study, a comprehensive database of 299 manufacturing firms (chosen randomly from a stratification of six BSE manufacturing industry subsectors) were utilized during the period from 1999–2000 to 2013–2014. Firm level efficiency scores and implications of IC (as measured by employing Pulic's Value Added Intellectual Capital Model) on the level of efficiency of the firms were examined simultaneously using Stochastic Frontier Analysis. Empirical results revealed that IC significantly determines the efficiency of the manufacturing firms during the period of study. However, the impact of financial crisis was not robust in changing the synergy between efficiency and IC. Size, age, and leverage were also found to be significant determinants of efficiency during the period of study.

The purpose of this paper is to explore the matching process before and after the Great Recession in the Netherlands. The Dutch case is interesting because it is characterised by increasing matching efficiency.

This paper uses data from 2001 to 2014 to study the Dutch labour market matching process accounting for the three labour market states and their heterogeneities.

The elasticity of hires with respect to the short-term employed was significant, positive and countercyclical, while elasticities relating to new entrants were procyclical. The matching function (MF) displays constant returns to scale (CRTS) when using an alternative labour supply (LS) measure that includes the short-term employed as jobseekers. The findings are at odds with the idea of mismatch and a shortage of skills. Search frictions for employers were lower and vacancies were filled faster. This can be related to the fact that in a loose labour market context with increasing short-term employment, employers increase their hiring of employed workers which generates negative externalities on unemployed.

The implications concern the specification of the MF and the CRTS assumption when using unemployment as a LS measure.

Human capital flight from developing countries to developed nations has been rising and giving concerns to governments and scholars alike. This paper aims to explore the impact migration from Nigeria has on economic output growth by focusing on the migration rate, remittances, population growth and secondary school enrolment. This has not received adequate attention in the literature, as many papers have primarily focused on the impact of remittances on economic growth.

Leveraging on the macro-level approach to migration, remittances and the economy, this research considers the nexus among the human capital flight and output growth variables by using the autoregressive distributed lag (ARDL) method of analysis for time series data between 1986 and 2018.

The net migration rate from Nigeria was found from the empirical analysis to be more disadvantageous for the economy, given its negative relationship with economic growth despite the large volume of foreign incomes (remittances). It also shows that secondary school enrolment positively and significantly impacted the Nigerian growth rate in the long run.

This research has widened the use of variables by combining net migration rate, remittances from abroad, population growth rate and secondary school enrolment to obtain a more robust outcome with implications for research and practice.

It is increasingly important to study labour market outcomes for people who are not in employment, education, or training (NEET). Where most studies focus solely on young people, the purpose of this paper is to include both younger and older NEETs to find out if there is any long-term scarring effect, and if the effect is different between these two groups.

This study uses a twin-based estimation method for the first time to measure the long-term effect of economic inactivity on income. The analysis is based on biological twins, in order to control for individuals’ unobservable heterogeneity. It is assumed that twins are similar to each other and the only unobservable heterogeneity is at the family level. Register-based data from Statistics Sweden is used.

The result indicates a significant negative income effect for those who have been in NEET, and is larger for those who have been in NEET for several consecutive periods of time. Individuals who were in NEET during 2001-2003 had on average 62 per cent lower income compared with their twin in 2011. The corresponding number for individuals who were in NEET for just one period was 33 per cent. Hence, time in NEET reduces income. The results show that the long-term scarring effect is not affected by age.

This study utilises for the first time a twin-based estimation method to measure the long-term effect of inactivity. Most studies focus solely on young people, but the authors also include an older group of people.

I study state dependence in social assistance receipt in Germany using annual survey data from the German Socio-Economic Panel for the years 1995–2011. There is considerable observed state dependence, with an average persistence rate in benefits of 68 per cent comparing to an average entry rate of just above 3 per cent. To identify a possible structural component, I estimate a series of dynamic random-effects probit models that control for observed and unobserved heterogeneity and endogeneity of initial conditions. I find evidence of substantial structural state dependence in benefit receipt. Estimates suggest that benefit receipt one year ago is associated with an increase in the likelihood of benefit receipt today by a factor of 3.4. This corresponds to an average partial effect of 13 percentage points. Average predicted entry and persistence rates and the absolute level of structural state dependence are higher in Eastern Germany than in Western Germany. I find only little evidence for time variation in state dependence around the years of the Hartz reforms.

This paper is the first to present empirical evidence consistent with models of signaling through unemployment and to uncover a new stylized fact using the 1988–2006 Displaced Worker Supplement (DWS) of the Current Population Survey (CPS), namely that, among white-collar workers, post-displacement earnings fall less rapidly with unemployment spells for layoffs than for plant closings. Because high-productivity workers are more likely to be recalled than low-productivity ones, they may choose to signal their productivity though unemployment, in which case the duration of unemployment may be positively related to post-displacement wages. Identification is done using workers whose plant closed as they cannot be recalled, and no incentives to signal arise.