For scheduling some projects that have repeatable bases as highway constructions and high rise building we have to use some special method like LOB and LSM instead of CPM. So far for resource leveling in these methods (LOB, LSM), it has been suggested various algorithms like mathematical algorithms and genetic algorithm which have many advantages in comparison with other methods. In this paper first, we explain mathematical and genetic algorithm and we examine two case study for each methods and then we make an comparison between these. At the end we study advantages and disadvantages of each algorithm in various situations.

We have a huge civic project, includes several sub-projects, which are divided into activities. This project is a project to rehabilitate 550, 000 hectares of agricultural land in the provinces of Khuzestan and Ilam. The project has been divided according to the plots of land. The Jehad in Tehran is managing the projects. They need project scheduling as well as resource levelling and "lot-sizing". Levelling and lot sizing are the most important issues in utilizing the limited resources. For determining the scope and the size of those sub-projects as well as their parallel activities, so far, many models have been proposed. However, the models are weak either in higher resource utilization or in solving numerically the problems. In this paper, our effort has been concentrated on developing scheduling, resource levelling, and lot sizing model, based on balancing utilization of resources, so that the real size civil project could be solved within an acceptable duration time. This paper proposes a goal programming linear model for simultaneous project scheduling and resource levelling. This model determines the best schedule of sub-activities (optimal "lot size" of each sub-activity) to reach the minimum amount of diversion of resources consumed from the number of resources available for the entire periods of the planning horizon. In fact, if the best "lot sizes" have been taken, then minimum fluctuation of the active resources is reached. The proposed model has been used to schedule a project with 87 activities. This project has been scheduled and, accordingly, the optimal volume (the "lot size") of sub-activities have been determined for each activity at any period of time. In this way, only 4 resources out of a total of 32 resources are in shortage. In contrast, the scheduling of this project, using the CPM, results in a shortage of 13 resources.

In project scheduling, many problems can arise when resource fluctuations are beyond acceptable limits. To overcome this, mathematical techniques have been developed for leveling resources. However, these produce a hard and inflexible approach in scheduling projects. The authors propose a simple resource leveling approach that can be used in scheduling projects with multi-mode execution activities. In the mentioned approach, an ant algorithm determines the execution mode of each activity so that resource leveling index and project time become optimum. In the model, some visibility functions (defined in accordance with problem characteristics) are utilized, and the best, which return the best result, is selected for the model.

Project managers often face some challenges resulting from the scarcity of resources in construction management. Levelling the used resources in multiple projects is a frequently encountered problem in construction areas and manufacturing sectors. In this regard, the current study proposes a robust Forensic-Based Investigation (FBI) algorithm for resource leveling in multiple projects, considering dierent objective functions of the resource graphs. To this end, Fuzzy C-Means (FCM) clustering approach was fused into the main operation of the FBI to enhance the convergence rate using the population information. The proposed scheduling examines dierent objective functions to eciently optimize the resource prole selection. Two case studies were taken into account in this research to elaborate on the performance of the improved optimization algorithm while dealing with the resource-leveling problem in multiple projects. The experimental ndings and statistical comparisons revealed that the developed Fuzzy clustering Forensic-Based Investigation (FFBI) could acquire solutions of high quality and outperform the compared optimization algorithms.

Resource leveling has a important role in project management. Most of researches has been done in this field by considering only one project, while in most of organizations more than a project have been done simultaneity. In this paper we model the problem under the multi project construction and consider the objective function as the minimize of the difference between resource usages. This problem is shown to be NP-hard and can not be solve with ordinary optimizations methods. So we offer a genetic algorithm for obtaining near optimum. The computational section shows the accuracy of proposed method.

Resource leveling is very important in projects and project managers always need a schedule based on the optimal use of resources needed to complete their projects. Most resource leveling research has been done in a single project, while in many organizations several projects are done simultaneously. For this purpose, a mathematical model is presented by all projects with the aim of minimizing changes in the level of different resources. Leveling and allocating resources is one of the most basic tasks of project management. Typically, project management uses specific methods such as GERT and PERT to plan and control the project (Boctor, 1990). Because the problem of resource leveling is an NP-complete problem and it is not possible to achieve the optimal solution in the general case (Guo et al., 2012), the cuckoo optimization algorithm has been used, which is one of the newest and most efficient evolutionary optimization methods.

The aim of this study was to identify and level the competency components of human resource managers based on spirituality in the workplace based on interpretive structural modeling. In order to analyze the data, interpretive structural modeling technique was used to level the factors and classify them. Participants in this study were 10 academic experts and human resources managers of the gas company in the qualitative section and 200 employees of the gas company in the quantitative section. After identifying the components, questionnaires were developed and distributed among statistical samples to determine validity and reliability. This questionnaire is of descriptive-exploratory and qualitative-quantitative type. According to the research results, 44 identified components were divided into seven factors. 21 components were related to the competence of human resource managers and 23 components were related to spirituality in the workplace. And the questionnaire index was affected by seven underlying factors, which explain more than 73% of the variability (variance) of variables.

The importance of resource leveling cannot be ignored throughout a project, especially in cases where a trivial fluctuation in using resources will lead into high financial risks or costs. Traditional approaches are usually insufficient in solving the problems of multi-objective constraint resource leveling. In this paper, we propose an optimized technique based on Differential Evolution (DE) algorithm and ELECTERE method in order to overcome the drawbacks of the traditional resource leveling algorithms. Furthermore, obviating the challenges of normalizing and weighting objective functions, this approach takes the uncertainty and ambiguity of the objective functions into account through application of pseudo-criterion. The structure of proposed model is designed based on real situations. Results obtained from the numerical example verify the efficiency of the proposed model in solving the problems of resource leveling, and in comparison with traditional models; they confirm the better performance of this model to overcome single and multi-objective problems.