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In this paper, the performance of a specific gas station (Parvin) has been studied and investigated. The purpose of this research is to design a second-order regression model based on simulated data to optimize the queuing system in line with the fuel sales and costs. Thus, the influential variables including the number of pumps and the number of pump operators need to be optimally determined.

The simulation was combined with design of experiments (DoE) techniques to achieve a predictable model for optimizing Gas Station performance considering both the sales rate and the queue length. First, the Gas Station was simulated with Arena software, and then by using DoE and response surface methodology (RSM), the gas station performance was optimized in terms of three objectives including costs. A face-centered central composite design (CCD) has been implemented to reach the optimal number of pumps and pump workers.

The results of the optimization model derived from the CCD indicate that the performance of the Gas Station system has been improved considerably. Moreover, after the detailed study of optimization and RSM outputs, it seems that the variations of both the pumps and the number of pump operators have significant impacts on the performance of the Gas Station including costs, sales rate and queue length.

In general, it has been proved that simulation-based RSM can be considered as a powerful and effective technique in both single and multi-objective experimental optimization. The present study has been able to help managers to make decisions and conduct the Gas Station in critical conditions in different hours of a day.

The target of this research is to develop a mathematical model which combines the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) and the Multi-Skilled Resource-Constrained Project Scheduling Problem (MSRCPSP). Due to the importance of resource management, the proposed formulation comprises resource leveling considerations as well. The model aims to simultaneously optimize: (1) the total time to accomplish all projects and (2) the total deviation of resource consumptions from the uniform utilization levels.

The K-Means (KM) and Fuzzy C-Means (FCM) clustering methods have been separately applied to discover the clusters of activities which have the most similar resource demands. The discovered clusters are given to the scheduling process as priori knowledge. Consequently, the execution times of the activities with the most common resource requests will not overlap. The intricacy of the problem led us to incorporate the KM and FCM techniques into a meta-heuristic called the Bi-objective Symbiosis Organisms Search (BSOS) algorithm so that the real-life samples of this problem could be solved. Therefore, two clustering-based algorithms, namely, the BSOS-KM and BSOS-FCM have been developed.

Comparisons between the BSOS-KM, BSOS-FCM and the BSOS method without any clustering approach show that the clustering techniques could enhance the optimization process. Another hybrid clustering-based methodology called the NSGA-II-SPE has been added to the comparisons to evaluate the developed resource leveling framework.

The practical importance of the model and the clustering-based algorithms have been demonstrated in planning several construction projects, where multiple water supply systems are concurrently constructed.

Reviewing the literature revealed that there was a need for a hybrid formulation that embraces the characteristics of the RCMPSP and MSRCPSP with resource leveling considerations. Moreover, the application of clustering algorithms as resource leveling techniques was not studied sufficiently in the literature.