Line-of-balance (LOB) methodology produces a work schedule in which resource allocation is automatically performed to provide a continuous and uninterrupted use of resources, but the distribution of resources could be further improved by resource leveling even if multiple resources are involved. The objective of this study is to develop a genetic algorithm (GA)-based multiresource leveling model for schedules that are established by LOB. The proposed model postulates that the production rate and duration of an activity are governed by the resource that requires the longest duration in completing a unit. Once the LOB schedule is established, resource leveling is performed according to the principle of optimum crew size that makes use of a utility data curve, which shows that productivity will suffer if the crew size is different than the optimum crew size, and the principle of natural rhythm that allows shifting the start times of an activity forward or backward at different units of production by ...

This study investigates the impacts of using different objective functions in leveling resources in schedules established by using Line-Of-Balance (LOB) methodology. A genetic algorithm-based model that considers different objective functions was used for leveling resources in LOB schedules. This model uses the principles of “optimum crew size” and “natural rhythm” that assume that the highest productivity can be achieved as long as an activity is performed in a unit of production by one or multiple crews of optimum size. Therefore, one needs to change the number of crews employed to shift the start times of an activity forwards or backwards at different units of production. The total project duration, the duration of an activity in any unit and the precedence relationships between activities remain the same during this procedure. Two LOB schedules are established for a pipeline project and are used to investigate the impacts of using different objective functions for resource leveling in LOB. It was observed that the objective functions provided the same optimal resource distribution. However, prudent schedulers should consider all ten objective functions because the distribution of resources depends on the number of activities, the float of each activity, and the precedence relationships between activities.

Construction project managers occupy middle management positions and their performance is as important as the performance of skilled/unskilled workers to the success of a project. This study investigates the factors that motivate construction managers, identifies the dominant human values of construction managers, and explores the relationship between construction managers’ human values and their motivators. A total of 101 construction managers participated in a survey to rate the importance of 20 motivators and 18 human values. Although simple correlation analysis indicated that many motivators are highly correlated with many human values, this finding ignores the interrelationships between the many variables. Factor analysis was used to reduce the 20 motivators to six motivating factors, and regression analysis was conducted to explore the relationship between each of the six motivating factors and human values, albeit without much success because of the nonlinear nature of the problem. However, by using an artificial neural network (ANN) model, it was possible to show that a relationship exists between human values and each of the six motivational factors with 75% accuracy. It is concluded that the performance of construction project managers can be enhanced by promoting motivators that agree with construction managers’ human values.

Sustainability is one of the emerging areas in building construction, and it is widely investigated in terms of bringing sustainable technology into construction. However, one of the biggest challenges in the construction industry is implementing sustainability. Building contractors and construction practitioners mostly struggle with the implementation of sustainability due to the lack of certain parameters that define sustainability. Therefore, this study investigates the factors for effective sustainability implementation to provide industry practitioners with the benefit of the extensive list of parameters when designing their sustainability program. To research the parameters, a questionnaire was designed and administered to construction professionals with wide experience in sustainability implementation. The respondents were asked to evaluate a set of sustainability factors in the economic, social, and environmental domains. This way, the study provides reflections from construction practitioners working on large projects. The findings indicated that Ethical and Relational Factors in economic sustainability, Historical and Social Relations Factors in social sustainability, and Material Usage Factors in environmental sustainability are the most important for effective sustainability implementation. The findings are expected to help construction practitioners understand metrics of sustainability, effectively manage their projects, and successfully implement sustainability with the consideration of proper metrics.