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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.

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.

Libraries need to develop information processing systems for evaluation, budgeting, planning, and operations. Electronic spreadsheets lend themselves to a variety of applications, but are time‐consuming to create. A model template and macros that can be used in many different types of library data analysis have been developed here. The procedures demonstrated here can build an essential set of tools for meeting fundamental goals of administrative efficiency, effective use of library resources, staff motivation, and rational policy making.

We augment a standard bilateral gift-exchange game to allow employees to communicate their gratitude for, or disapproval toward, the wage assigned to them by their manager. This provides employees with a means of reciprocation or emotion expression toward the employee which is not available in a standard gift-exchange game and may substitute for the higher-than-equilibrium efforts commonly seen in this environment. We find that employees express gratitude or disapproval according to the wage received, but these messages are not a substitute for monetary reciprocation as the relationship between wages and effort is unchanged. These results suggest that employees view the messages as a form of emotional expression independent from rewarding or punishing managers. Average wage levels are little affected by allowing messages, although wages do fall more over time in the absence of messages and individual managers’ wage choices are affected by the messages they receive.

This chapter analyzes the effects of introducing a graduated minimum wage in a model with optimal income taxation in which a government seeks to maximize social welfare. It shows that the optimal graduated minimum wage increases social welfare by increasing the low-productivity workers’ consumption and bringing it closer to the first-best. The chapter also describes how the graduated minimum wage in a social welfare optimum depends on important economy characteristics such as the government’s revenue needs, the social welfare weight of low-productivity workers, and the numbers and productivities of the different types of workers.

With economic reforms increasing market competition, greater efficiency and productivity of factors of production, particularly of the workforce, have become important prerequisites for firms' growth and survival. Consequently, designing appropriate strategies to motivate the workforce in this direction appears as a critical aspect of human resource management. However, an important issue is if increase in wages, salaries, and other benefits can necessarily result in the desired outcomes. This chapter will examine this aspect. Estimating long-term trends in share of wages, salaries, and total emoluments in major industries, it is found that while the share of wages, salaries, and total emoluments has increased in recent years, such changes are not reflected in higher productivity. It is, therefore, suggested that, in addition to higher wages, salaries, and other benefits, enhancing efficiency and productivity of human resources requires adequate emphasis on human aspects as well.

Relying on an international panel data set, the purpose of this paper is to quantify the economic impact of labor unionization on corporate financial performance.

Static panel regression analysis is performed for a firm-level multinational data set to elucidate the postulated empirical relationships between employee unionization and corporate performance. The transmission mechanisms intermediating the studied effects are discussed and operationalized.

The empirical evidence demonstrates that firms with a higher level of employee unionization spend more on wages and labor-related expenses. The concomitant downside of higher resource extraction by unions is a lower rate of net employment creation and a higher possibility of redundancy layoffs.

Overall, the authors demonstrate that by creating a credible threat of employee disobedience manifested through strikes and internal wage disputes, labor unions remain an effective mechanism of increasing employees’ bargaining power. Despite the discovered weak negative associative link between the degree of unionization and corporate financial performance, the authors perceive the overall evidence to be inconclusive on this matter.

Working time accounts (WTAs) allow firms to smooth hours worked over time. The purpose of this paper is to analyze whether this increase in flexibility has also affected how firms adjust employment in Germany over the business cycle.

This paper uses rich microeconomic panel data and fixed effects estimations to compare the employment adjustment of firms with and without WTAs.

The authors show that firms with WTAs show a similar separation and hiring behavior in response to revenue changes as firms without WTAs. One possible explanation is that firms without WTAs used short-time work (STW) to adjust hours worked instead. However, the authors find that firms with WTAs use STW more than firms without WTAs.

These findings call into question the popular hypothesis that WTAs were the key driver of the unusually small increase in German unemployment in the Great Recession.