Time, quality, and cost are the most critical performance indicators in project management. It has always been considered a tough challenge for project managers to optimize them simultaneously. This paper aims at establishing a simulation-based integer linear programming tool that helps project managers, at the preliminary stages, to assess the risks related to the feasibility and profitability of the projects within the framework of a stochastic discrete time-cost-quality tradeoff problem. The computational experiments on a wide range of benchmark instances from the literature were performed, and the results were compared with those of the deterministic version of the problem. The proposed approach is able to assess the impact of the stochastic behavior of the duration and the quality of the tasks on the cost, duration, and quality of the whole project. Moreover, the simplicity and the reduced time required for the computation of large size networks revealed to be very promising for giving a practical solution for real-life projects. INDEX TERMS Monte-Carlo simulation, integer linear programming, project management, risk.
Variable neighborhood search (VNS) algorithm is proposed for scheduling identical parallel machine. The objective is to study the effect of adding a new neighborhood structure and changing the order of the neighborhood structures on minimizing the makespan. To enhance the quality of the final solution, a machine based encoding method and five neighborhood structures are used in VNS. Two initial solution methods which were used in two versions of improved VNS (IVNS) are employed, namely, longest processing time (LPT) initial solution, denoted as HIVNS, and random initial solution, denoted as RIVNS. The proposed versions are compared with LPT, simulated annealing (SA), genetic algorithm (GA), modified variable neighborhood search (MVNS), and improved variable neighborhood search (IVNS) algorithms from the literature. Computational results show that changing the order of neighborhood structures and adding a new neighborhood structure can yield a better solution in terms of average makespan.
In this paper, an identical parallel machine problem was considered with the objective of minimizing the makespan. This problem is NP-hard in the strong sense. A mathematical formulation based on an improved arc flow model with enhanced bounds was proposed. A variable neighborhood search algorithm was proposed to obtain an upper bound. Three lower bounds from the literature were utilized in the improved arc flow model to improve the efficiency of the mathematical formulation. In addition, a graph compression technique was proposed to reduce the size of the graph. As a consequence, the improved arc flow model was compared with an arc flow model from the literature. The computational results on benchmark instances showed that the improved arc flow model outperformed the literature arc flow model at finding optimal solutions for 99.97% of the benchmark instances, with the overall percentage of the reduction in time reaching 87%.
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