mohammad hassan sebt

In this paper, a Fully Informed Particle Swarm (FIPS) algorithm is proposed for solving the Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. In the proposed FIPS, A random key and the related mode list (ML) representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. In particular, a new fitness function which reduces the average deviation from optimality and CPU-time is presented. Comparing the results of the proposed FIPS with other approaches using the well-known benchmark sets in PSPLIB validate the effectiveness of the proposed algorithm to solve the MRCPSP.

In this paper, a new genetic algorithm (GA) is presented for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. A random key and the related mode list (ML) representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. In this paper, a simple, efficient fitness function is proposed which has better performance compared to the other fitness functions in the literature. Defining a new mutation operator for ML is the other contribution of the current study. Comparing the results of the proposed GA with other approaches using the well-known benchmark sets in PSPLIB validates the effectiveness of the proposed algorithm to solve the MRCPSP.

In different projects the speed of different machinery can be estimated using manufacturer's handbooks and a number of modification factors to consider the environmental effects, type of the project and status of site management. Since the statuses of different factors of the domestic projects are totally different from those of the international projects, there is a wide discrepancy between the determined speed by handbooks and the actual values in the domestic projects. This paper is aimed to develop a fuzzy system to estimate soil excavation rates at earthmoving jobsites. The proposed fuzzy system is based on IF-THEN rules; a genetic algorithm improves the overall accuracy. The obtained results clearly revealed the capability and applicability of the proposed system to properly estimate soil excavation speed. The average error of fuzzy system, handbook method and nearest neighbor interpolation are 10, 92 and 32 percent, respectively.

Resource-Constrained Project Scheduling Problem (RCPSP) is one of the most popular problems in the scheduling phase of any project. This paper tackles the RCPSP in which activity durations can vary within their certain ranges such as RCPSP with variable activity durations. In this paper, we have attempted to find the most suitable hybridization of GA variants to solve the mentioned problem. For this reason, three GA variants (Standard GA, Stud GA and Jumping Gene) were utilized for first GA, and two GA variants (Standard GA, Stud GA) for the second one, and their hybridizations were compared. For this purpose, several comparisons of the following hybridizations of GAs are performed: Standard-Standard GA, Standard-Stud GA, Stud-Standard GA, Stud-Stud GA, Jumping Gene-Standard GA, and Jumping Gene-Stud GA. Simulation results show that implementing Stud-Stud GA hybridization to solve this problem will cause convergence on the minimum project makespan, faster and more accurate than other hybrids. The robustness of the Stud GA in solving the well-known benchmarking RCPSP problems with deterministic activity durations is also analyzed.

In this paper, the issue of optimum location of yielding dampers in steel structures has been studied, thereby in addition to the least yielding dampers used in a special building, the restriction for the destruction of main members is considered. The selection of suitable types of yielding dampers at each span also must be studied. In the first generation, different distributions are suggested for dampers and then an analyzer program determines the amount of destruction of main members thoroughly. In other generations, first various distributions are evaluated and then each of them is affected by selection, crossover and mutation as genetic algorithm operators and, in final generation, four distributions are submitted as better options. Since these offered distributions have been selected under an earthquake, therefore four resulted distributions of yielding dampers must be cost-benefit analyzed for different earthquakes. Finally, the optimum location for dampers is selected based on the comparison between benefit to cost ratio, drift stories and the number of different ADAS types of various distributions. The amount of dissipation energy by dampers located in different levels and the status of plastic hinges in main members are confirmations to the optimum design for the location of dampers.